HGS MathComp Upcoming Events

Collecting, processing and analyzing data are central activities for virtually every researcher. Topics like data sharing and data publication are becoming increasingly important. Nevertheless, many research projects lack a structured and well-organized data management. This course is meant to give a general, discipline-independent introduction into various topics central to an efficient management of research data with a special focus on questions related to data archiving and data sharing. Both are central aspects of good scientific practice. Archiving and long-term preservation of research data are prerequisites for the scrutiny of scientific results based on the analysis of this data. Data sharing on the other hand increases transparency of research results and enables possible re-usage of data for new research questions, in combination with additional data sets and in interdisciplinary contexts.

Abstract: The overall aim of this course is to study matrix computations that lie at the core of a wide variety of applications in machine learning and data science. We will give an introduction to the mathematical theory of computational linear algebra (with derivations of the methods and some elementary proofs). This will broadly include methods for solving linear systems of equations, least-squares problems, eigenvalue problems, and other matrix decompositions. We will learn to implement the computational methods efficiently, as well as how to thoroughly test their implementations for accuracy and performance. More importantly, we will learn how to apply these methods to solve realistic machine learning application problems, including collaborative filtering (recommendation systems), image deblurring, kernel methods, and beyond.

It’s far more than “just” a presentation: defending your thesis is the last (and one of the biggest) challenges in your academic qualification – and it doesn’t only require well-prepared content. This workshop addresses aspects of the defense related to communication and presentation. We will work on participants’ body language as well as on strategies for convincing argumentative and rhetorical positioning during the disputation. A significant portion of the workshop will be devoted to simulated defenses and evaluations of the performances.

Galaxy formation and evolution is a multi-scale, multi dimensional and multi-physics problem. While using modern high-resolution telescopes can offer us high-quality insights in galaxies, deciphering the physics of galaxy formation requires cosmological simulations which track the evolution over the history of the Universe. In this talk, I will describe the various ways to compare cosmological simulations with observations of galaxies. I will present a rich dataset of observed galaxies and present novel ways to constrain galaxy formation models. I will show that modern cosmological simulations reproduce various properties of observed galaxies however, fall short in matching the dynamical properties.

"Wow, that presenter is so good in front of an audience. If only that were easier for me!" Being a good speaker is often just a question of developing a set of skills and techniques. The use of voice and body language, an effective presentation structure and the dynamic use of language require awareness and practice. The workshop helps to identify and explore these requirements, from self-reflection to self-assurance and long-term excellence.

Computational-based mass spectrometry (MS) is rapidly evolving, challenging us to process and interpret complex multi-dimensional data. There are many computational MS projects to aid in this process, but mastering or even starting to use them can be a challenge. Therefore, we want to provide a platform in Prague to meet, discuss, and learn how to move MS-based omics forward. Additionally, we want to offer a hands-on training workshop on the use of generative AI tools for scientific writing presentations, where we can learn their use, opportunities, and ethical usage.

The recent development of AI-based software to create texts, images or even videos has an increasing impact on the work of journalists. This talk will outline benefits as well as concerns and risks of this emerging technology for journalism.

14:30 Janik Fechtelpeter (Supervisor G. Koppe)
“Control of dynamical systems and recurrent neural networks with applications to clinical and behavioral psychology“

14:50 Luise Nottmeyer (Supervisor J. Rocklöv)
“Indicator Development for Environmental Antimicrobial Resistance Monitoring“

15:10 Yichao Liu (Supervisor J. Rocklöv)
“Using machine learning for citizen science surveillance of ticks and prediction of tick- borne diseases with climate change”

15:30 Denis Kiesewetter (Supervisor F. Gräter)
"Incorporating Redox Chemistry into Classical Molecular Dynamics Simulations by Machine Learning"

15:50 Letao Huang (Supervisor A. Dreuw)
“The Ground and Excited State Absorption of Triphenylamine (TPA) and Dimethylmethylene-Bridged Triphenylamine (DTPA): A Quantum-Chemical Study”

Prerequisites:

Basic Python knowledge is required. Participants are recommended to bring a laptop.

Summary:

A good test suite makes extending, maintaining and debugging a codebase both easier and faster. In this course we will look at the different kinds of tests, and understand how to write good tests. We will also cover different testing strategies, such as test-driven-design when writing new code, or acceptance testing when working with legacy code that doesn’t have a good test suite. Code samples will use the Python testing framework pytest but the concepts also apply to other frameworks and languages.

Learning Objectives:

After the course participants will

- Understand the different kinds of tests
- Understand different testing strategies
- Write better tests of their code
- Deal better with legacy code that is missing tests

In times of globally growing pressure on universities the principle of academic freedom is fiercely debated within and outside academia. What does it mean? What is covered? What is not? Is it an institutional or a personal right? Where are its limits? What are the threats?

Surprisingly few academics have well-informed knowledge what academic freedom is and what it is not. Debates and actions are often driven by emotions and subjective feelings about a “proper university“ instead of proper knowledge.

In this course we will:

I. discuss the principle of academic freedom – its legal status and its formal applicability

II. learn to discern academic freedom from other civil liberties (e.g. open speech, freedom of opinion)

III. understand the complexity of taking action in the face of violations to academic freedom

IV. sharpen our awareness for threats to academic freedom in our own societies.

The course and discussions will be in English.

We expect engagement and interest – no marks will be given!

For any queries please contact thomas.meier@zaw.uni-heidelberg.de

The consideration of uncertainty in optimization problems is an important aspect in many practical applications. In this talk we consider optimization problems with partial differential equation (PDE) constraints, where uncertainty occurs in parameters (for example material parameters, usage scenarios) as well as in the optimization variables (e.g. manufacturing tolerances). There exist several approaches to handle uncertainty in optimization, in particular stochastic optimization, probabilistic constraints and (distributionally) robust optimization.

We will focus on robust and distributionally robust optimization approaches for problems with expensive PDE-constraints. In robust optimization, relevant realizations of the uncertain variables are described by an uncertainty set and it is required that the optimization variables satisfy critical constraints for all realizations of the uncertain variables in the uncertainty set. In distributionally robust optimization one considers an ambiguity set of relevant distributions for uncertain parameters and requires that the constraints are satisfied in expectation for all distributions in the ambiguity set.

The resulting robust formulation is a semi-infinite optimization problem and can be written in a min-max form. We present various techniques how to approximate and reformulate the robust problem by using optimality conditions and duality theory in order to obtain a tractable problem. Depending on the approach, the reformulation contains conic constraints, complementarity conditions and conjugate functions. We will sketch suitable solution methods.

Moreover, we will illustrate, how adjoint techniques as well as model order reduction and error estimation can be used to solve these problems efficiently in the PDE constraint case.

We present several application examples, in particular the design optimization of electric machines under uncertainty.

Machine learning can be performed in all major programming languages, but Python has established itself as a standard. In this course, you will learn the fundamentals of machine learning (data collection, cleaning, modeling and prediction) through hands-on exercises on real-world data sets. You will understand the differences between supervised and unsupervised learning and get to know common models and algorithms. Furthermore, you will be able to evaluate the performance of the trained models.

For more see the abstract_file:

Nowadays researchers can obtain quantitative measurements of the abundances and activities of different types of molecules across experimental conditions and spatio-temporal resolutions. In this talk, I will discuss how latent variable models can help us to extract major underlying patterns from the noisy, heterogeneous and high-dimensional data and how to accommodate the statistical properties of molecular data in such models. Case studies will demonstrate how this can help us to highlight core biological processes along development, in health and disease and pinpoint underlying molecular drivers.

Mastering the craft of writing is a core aspect of developing as doctoral students. This workshop provides a comprehensive view (and review) of helpful norms, rules and skills to help doctoral students improve their writing while gaining confidence in their capacity to exercise a foundation skill. As ‘expert writers,’ participants will be encouraged not just to write but also to align with readers’ expectations, and to avoid pretentious prose. Selected issues of grammar, vocabulary, and text structure are discussed in service of these topics.

Objectives:

- Identify and fix problems in your own long-form texts with respect to paragraph writing, coherence and unity;
- Make your writing more impactful and valuable;
- Address problems relating to grammar and (academic) vocabulary;
- Effectively proofread your own texts and incorporate feedback from your peers, teacher and doctoral advisor into your own text.

From blogging to news and opinion articles, writing remains one of the best ways for scientists to reach the public. While there are many styles and formats for popular science writing, certain elements of good writing are required. In this workshop, we discuss the various formats of writing as well as why and when each is used. We will also discuss how to tell a good story and common bad habits scientists have when writing for the public. Participants will practice writing short pieces based on their own work or interests that will be shared and discussed during the workshop.

Prerequisites:

No prior knowledge by the participants is necessary to participate in this course and it is intended for all scientific audiences. Participants are required to bring their own laptops to work on during the course. Network access (e.g. through Eduroam) is recommended.

Summary:

Jupyter notebooks are a great tool for exploring and interacting with data using the Python programming language and its rich ecosystem of libraries. In this course we will cover basic usage of the Pandas library to download a dataset, explore its contents, clean up missing or invalid data, filter the data according to different criteria, and plot visualizations of the data.

Learning Objectives:

After the course participants will

- Explore a dataset
- Filter the dataset
- Clean up missing / invalid entries
- Generate plots and visualizations of the data

Modelle bilden die Grundlage für quantitative Entscheidungen: Analyse, Verbesserung und Optimierung basieren auf mathematischen Beschreibungen unserer Umwelt. Der Zusammenhang zwischen Optimierung und Nachhaltigkeit zeigt sich deutlich in der effizienten Nutzung von Ressourcen und der Reduktion von Umweltbelastungen, wodurch langfristige und nachhaltige Lösungen entwickelt werden können. Beim Modellierungstag stellen Redner:innen der Universität und ihrer Partnerinstitute und -unternehmen Forschung mit Bezug zur Energieversorgung, Logistik und Hitzestressvermeidung vor.

Auf dieser Netzwerkveranstaltung der HGS MathComp Graduate School treffen Expert:innen und Wissenschaftler:innen zusammen und beleuchten gemeinsam Trends in der modellgestützten Forschung und Entwicklung aus unterschiedlichen Blickwinkeln. In diesem Erfahrungsaustausch liegt das Potenzial für zukünftige Zusammenarbeit zwischen Universität und Wirtschaft für den direkten Transfer von Forschungsergebnissen zur Prozessoptimierung und zum intelligenten Ressourceneinsatz.

Understanding and predicting maladaptive human behavior and cognition, which are pertinent to many psychiatric disorders, requires understanding their underlying dynamics and dynamical mechanisms. Modifying these behaviors requires control over their dynamics.

My group focuses on inferring the dynamics underlying human behavioral and neural time series, utilizing both process-driven and data-driven modeling approaches. Based on these methods, we develop mental health applications where dynamics are used to predict and control future system states, or to study their generative mechanisms. Our applications include: 1) Mobile apps that assess psychological ratings as proxies for mental health states, forecast changes over time, and use these forecasts to tailor mobile mental health exercises. 2) Web-based platforms in which we specifically tailor cognitive experimental paradigms to reliably assess complex decision-making. And 3) we develop and apply models for the reliable detection of neural dynamical systems features predictive of psychiatric dysfunction.

In this colloquium, I want to introduce to you the new RG and its members, along with current and future research directions.

Prerequisites:

Basic Python knowledge and knowledge about data processing, ML models and training of models is required.

Summary:

The AI revolution is moving even more rapidly than the digital revolution and leads to the emergence of completely new tools and technologies that affect the scientific process. In this course, we will learn about data-based research software, tools and communities that are relevant in creating and sharing such software, and about best practices in training machine-learning models. Research software that is based on ML models requires an additional layer of best practices in the implementation, including testing of non-deterministic processes. Security aspects as well as bad examples are discussed to highlight the importance of adhering to a best practices code of conduct.

Learning Objectives:

After the course participants will

- Understand and follow best practices in the underlying dataset
- Understand and follow best practices in training ML models
- Write better data-based research software, including appropriate tests
- Avoid negative impact from legal and security issues

Prerequisites:

Basic Python knowledge is required. Participants need a laptop/PC with Visual Studio Code installed and a working Python environment. Participants need to have access to GitHub copilot (either through free trial, individual license such as GitHub student (free), or other form of license).

Summary:

Generative AI is emerging as a major creative force that supports humans in content creation. Specifically trained models can support software developers with their software projects and lead to time savings and a shift in what aspects of generating software are more important on a day-to-day basis. In this course, we will learn about GitHub Copilot, the differences to ChatGPT, and how to set up and use GitHub Copilot in coding projects. Best practices in using Copilot, as well as recommendations how to use it efficiently and safely will be introduced.

Learning Objectives:

After the course participants will

- Know what GitHub Copilot is and what is happening under the hood
- Use Copilot in an IDE
- Improve Copilot's suggestions through providing appropriate context
- Know when and when not to use Copilot
- Know about privacy and security when using GitHub copilot

It’s far more than “just” a presentation: defending your thesis is the last (and one of the biggest) challenges in your academic qualification – and it doesn’t only require well-prepared content. This workshop addresses aspects of the defense related to communication and presentation. We will work on participants’ body language as well as on strategies for convincing argumentative and rhetorical positioning during the disputation. A significant portion of the workshop will be devoted to simulated defenses and evaluations of the performances.

Prerequisites:

Basic Python knowledge is required.

Summary:

Python has rapidly advanced to the most popular programming language in science and research. From data analysis to simulation and preparation of publications, all can be done in Python with appropriate libraries and implementing own modules. We will discuss Python Enhancement Proposals (PEP) and how these can help you write cleaner code. Common pitfalls in Python will be explained with examples. We will demonstrate typical “bad programming” and how to code the examples in a more pythonic way.

Learning Objectives:

After the course participants will

- Understand the basic PEP recommendations
- Use a linter and code formatter to ensure following of the guidelines
- Write better=more readable code
- Avoid bugs through best practices for example in passing keyword arguments

Syllabus:

- Motivation: coherent structures in PDE
- Stability theory for non-linear ODEs
- Asymptotic stability in dissipative systems
- Essential and point spectrum of a linear operator
- Semigroups and exponential stability
- Non-linear estimates and orbital stability

Prerequisites:

Prior knowledge about version control with Git is required. This can e.g. be acquired by attending the SSC’s introductory course “Version Control with Git” and “Intermediate Topics in Version Control with Git”. This course is intended for researchers that develop their own research software. Participants are required to bring their own laptops to work on during the course. Network access (e.g. through Eduroam) is recommended.

Summary:

Applying version control can be a game changer for a collaborative research software project. However, even in projects that already successfully use Git, there is often room for improvement of the employed Git workflows. This workshop explores some advanced topics that increase the participants’ repertoire of Git workflows: Bisection, Worktrees, Submodules, Large File Storage and Pre-commit Hooks. Additionally, we showcase invasive methods of modifying a Git repository via Rebase.

Learning Objectives:

After the course participants will

- Know about advanced Git features like submodules, large file storage
- Be able to bisect regression in a Git repository
- Understand the advantages of worktrees
- Be able to apply pre-commit hooks for their Git repositories
- Have used Rebase to change a repository’s Git history

Prerequisites:

Prior knowledge about version control with Git is required. This can e.g. be acquired by attending the SSC’s introductory course “Version Control with Git”. This course is intended for all scientific audiences. Participants are required to bring their own laptops to work on during the course. Network access (e.g. through Eduroam) is recommended.

Summary:

Applying version control can be a game changer for a collaborative research software project. However, even in projects that already successfully use Git, there is often room for improvement of the employed Git workflows. This workshop tries to delve into collaborative workflows using Git branches and discuss their strengths. Special emphasis is given to the resolution of merge conflicts that may be required in the process of dealing with diverging branches.

Learning Objectives:

After the course participants will

- Understand git branches and typical workflows using them
- Have seen collaborative workflows on GitHub (Pull Requests, Forks etc.)
- Gained first experience in resolving merge conflicts
- Know how to use rebase to clean up a branch’s history

Prerequisites:

No prior knowledge by the participants is necessary to participate in this course and it is intended for all scientific audiences. Participants are required to bring their own laptops to work on during the course. Network access (e.g. through Eduroam) is recommended.

Summary:

The Unix shell is a powerful tool that allows people to do complex things with just a few keystrokes. More importantly, it helps them combine existing programs in new ways and automate repetitive tasks so they aren’t typing the same things over and over again. Use of the shell is fundamental to using a wide range of other powerful tools and computing resources. The course will include hands-on live coding sessions where participants exercise the learned commands on their own computers.

Version control is the lab notebook of the digital world: it is used to keep track of what was done and to collaborate with other people. Its use is the state of the art in software development projects of all scales. However, it is not limited to software: books, papers, small data sets, and anything that changes over time or needs to be shared can and should be stored in a version control system. The course will include hands-on live coding sessions where participants exercise the learned commands on their own computers.

Learning Objectives:

- Have a fundamental understanding of how and why to use the Unix Shell
- Be comfortable with handling files and directories using the command line
- Have experience with advanced usage of the shell e.g. loops, pipes, redirects etc.
- Know how to write their workflows as reusable shell scripts
- Understand the benefits of using version control
- Understand basic git terminology
- Have a good working knowledge of common tasks in Git
- See how Git repositories can help them to move towards practicing Open Science

Computational modeling of the cardiovascular system can help to understand the relevant mechanisms and help to tailor treatments for heart diseases. We introduce a fully coupled PDE system for electrophysiology and cardiac mechanics combined with ODEs of ionic concentrations, the blood pressure in the cardiac chambers and a model for the circulatory system.

The model is realized in the parallel software system M++, and we show that the full model for a heart beat is scaling very well on modern HPC systems.

10:30 - Robin Fallegger (Supervisor J. Saez-Rodriguez)
“Meta-analysis of chronic kidney disease single cell atlases“

10:50 - Chiara Schiller (Supervisor D. Schapiro)
“Profiling intrahepatic cholangiocarcinoma samples to leverage spatial information into clinical Applicability“

11:10 - Philipp Schäfer (Supervisor J. Saez-Rodriguez)
“Dissecting immune system dysfunction with spatial omics data”

11:30 - 10-minute break

11:40 - Bárbara Zita Peters Couto (Supervisor J. Saez-Rodriguez)
“On Targeted Therapy Discovery to Overcome Chemoresistance in Ovarian Cancer“

12:00 - Leonie Küchenhoff (Supervisor J. Saez-Rodriguez)
“Fibrotic disease signatures across organs”

12:20 - Miguel Ángel Hernández Hernández (Supervisor J. Saez-Rodriguez)
“Multi-omic integration and analysis of IgA nephropathy disease mechanisms”

12:40 - 80-minute break / 12:45 - 13:30 HGS MathComp Mixer

14:00 - Marc Ickler (Supervisor F. Hamprecht)
“Machine Learning for Molecular Physics”

14:20 - Carolin Lindow (Supervisor A. Marciniak-Czochra)
“Mathematical modelling and model-based data analysis of structured stem cell systems”

14:40 - 10-minute break

14:50 - Albert Li (Supervisor C. Herrmann)
“Development of deep learning models to predict context-specific response to interferon treatment”

15:10 - Fariha Mahzabin Annesha (Supervisor J. Wittbrodt)
“Correlative Super-Resolution light and X-ray Microscopy to probe genome organisation in Cells”

Lab presentations:
Tobias Buck, Holger Fröning, Denis Schapiro

Science talks:
• William Oliver (Buck lab): Machine Learning for cosmological simulations
• Hendrik Borras (Fröning lab): Probabilistic Photonic Computing with Chaotic Light
• Miguel A. Ibarra Arellano (Schapiro lab): Finding broken cells with AI and computer vision

14:30 - Charles Hatfield (Supervisor J. Rockloev)
“Modeling of urban microclimate and mobility effects on mosquito abundance and vectorial competence”

14:50 - Hannah Weiser (Supervisor B. Höfle)
“It all starts with data – Improving machine learning-based point cloud algorithms for forest applications with synthetic training data generated by virtual laser scanning”

15:10 - Ronald Tabernig (Supervisor B. Höfle)
„Simulating laser scanning of dynamic virtual 3D scenes for improved 4D point cloud-based topographic change analysis“

15:30 - 10-minute break

15:40 - Niklas Reinhardt (Supervisor J. Zech)
“Statistical Operator Learning”

16:00 - Marco Hübner (Supervisor L Maier-Hein)
“Spectral Image Synthesis for Training Intelligent Surgical Systems”

16:20 - Julian Niederer (Supervisor E. Kostina)
"Numerical Methods for Nonlinear Model Predictive Control for Control Problems with Blocks of Vanishing Constraints”

Syllabus:

- Introduction: mathematical models and inverse problems.
- Basic topics in probability theory and functional analysis.
- Inverse problems, Bayesian inference and uncertainty quantification.
- Existence and consistency (stability) of posterior distributions.
- Exploring the posterior and sampling methods.
- Stability estimates for linear PDEs and physically informed models.

- Day 1: 2 x 90 minutes sessions
- Day 2 to 4: 1 90 minutes session + 1 90 minutes practice session

This course introduces the general-purpose programming language Python, which is used by web developers, data scientists and machine learning experts. Understanding the basics of Python will allow you to grasp the concepts of tools you might encounter and quickly apply them to your own research.

Within the scope of this course, you will master the Python philosophy, syntax, and writing your own scripts and modules. In addition, you will use your newly acquired skills to perform hands-on exercises and learn to conduct reproducible in-silico research. After completing this course, you will be ready to start your own Python journey and delve deeper into the world of Data Science.

Target group

This workshop is designed for doctoral candidates with previous presentation experience.

Objectives

"Wow, that presenter is so good in front of an audience. If only that were easier for me!" Being a good speaker is often just a question of developing a set of skills and techniques. The use of voice and body language, an effective presentation structure and the dynamic use of language require awareness and practice. The workshop helps to identify and explore these requirements, from self-reflection to self-assurance and long-term excellence.

Description

This seminar provides participants the opportunity to improve their conference presentation skills. Constructive feedback from the trainer and group members give the speaker a healthy amount of input while practicing new ideas and techniques to enhance the quality of their speech and overall impact of the talk.

Participants will be required to prepare a 3 to 5 minute overview of their work; the use of slides is optional. This will provide a basis for applying the practical aims of the workshop.

Throughout the two-day workshop, participants will be guided through interactive exercises to improve non-verbal communication, improve the ability to listen and react generously, and to integrate focusing techniques, which empower the speaker. Attention will also be given to structural and language aspects to improve clarity and flow of the talk.

Contents in Brief

- Effectively introducing yourself
- Engaging the audience in your talk
- Affirming the strengths and individual style of the speaker
- Improving body language and vocal quality
- Structuring your talk
- Constructive tactics for dealing with nervousness
- Dealing with challenging questions (Q&A sessions)
- Networking at conferences

Methods

- Voice and body techniques
- Partner work
- Language practice and analysis
- Interactive activities with online tools
- Videotaping and feedback sessions

Observations of the cosmos on the largest visible scales have revealed it to be amazingly simple. Likewise, the Large Hadron Collider has found no new particles on the tiniest scales probed. In contrast, the most popular theoretical paradigms including string theory and inflation, have grown increasingly complex and contrived. We have therefore launched a search for more economical and predictive explanations. In the process, we stumbled upon 1) the simplest-yet explanation for the cosmic dark matter, soon to be tested by galaxy surveys, 2) a thermodynamic explanation for the large scale geometry of the cosmos, based on an elegant new calculation of gravitational entropy, 3) an explanation of the big bang singularity as a kind of “mirror” which enforces CPT symmetry (the deepest known symmetry of nature, linking particles to antiparticles), and 4) a new explanation of the primordial density variations which led to the formation of galaxies, explaining them quantitatively in terms of known physics. I’ll outline the new theory’s main challenges and opportunities, ranging from understanding the Higgs boson to consistently coupling the quantum fields of the Standard Model to gravity, as well as future observational tests.

On the internet, video formats have long conquered the world. YouTube has long since overtaken television, especially among the younger generation, and many people move naturally in the world of short videos. In science, however, a large part of the community still shies away from using this medium.

In this workshop we will discuss the basics of (short) video production from the idea to the final upload. More importantly, we will shoot our first videos and gain our own experience with the medium of science video. In particular, we will also address the type of equipment needed and options for each budget.

Topics (selection)

- Equipment for video production
- Basics of video storytelling
- Video production from start to finish
- Best practice in recording
- Introduction to video editing software
- Upload to popular platforms

The possibility to record video formats should be given. A smartphone is sufficient for this purpose. Essential video equipment (light, audio and video hardware) will be provided.

In the academic world, clear, concise, and well-written texts play an important role in convincing journal editors and conference organisers to accept a paper for review and publication, or to invite a researcher to present at a conference. This workshop supports PhD students and novice researchers who need to produce scientific papers in English. Providing participants with strategies to write short texts efficiently and effectively, it also focuses on standards of professional papers aimed at Anglo-Saxon or international research communities. It enables participants to organise ideas and structure texts effectively, to present their own and other researchers‘ findings and opinions appropriately, and to use correct terminology and vocabulary.

During the workshop, participants work with their own texts as well as with examples from their own disciplines that they bring along and consider to be particularly well written. They discuss features of good scientific papers and are equipped to use adequate language in different genres and for different audiences. In addition, they receive peer feedback on their own drafts. All exercises empower them to produce clearer, and more correct, concise, and reader-oriented papers.

The two-day workshop covers the following topics:

- taking inventory: participants‘ strengths and challenges in writing scientific papers in English
- a brief introduction to research and writing processes
- using text analysis to become a better writer
- reporting findings, ideas, and opinions professionally and adequately
- making yourself understood: principles of clear and concise writing
- structuring ideas, organising texts: transitions, connectives, & co.
- working effectively with co-authors and constructive text feedback
- useful online and offline resources

We are currently witnessing the second quantum revolution and with it the advent of quantum technological devices for information processing purposes. Understanding these systems and their capabilities as well as developing robust algorithms for them, requires a fundamental understanding of complex quantum many-body systems as well as ways to characterize their properties efficiently. Within this course, we will help the participants to obtain proficiency in all of these aspects of quantum theory. A tentative list of topics includes:

- Entanglement/Non-Locality
Lecture 1: States, Measurement, Tensor Product, Maximally Entangled State
Lecture 2: Tensor Product (contd.), Density Matrices, Reduced States, Entropy, Entanglement Entropy.
Lecture 3: Bell-inequalities, Bell’s Theorem, Non-local Games/Protocols.

- Quantum Spin-System
Lecture 1: Spin Hamiltonians, Spin-½ Heisenberg Model, SU(2), Ground States, Ground State Energy
Lecture 2: Free Energy, Thermodynamic Limit, Thermal State
Lecture 3: Symmetry Breaking, Mermin-Wagner
Lecture 4: AKLT, Matrix Product States

For each of these topics, we will provide introductory lectures to get students acquainted with the topics before entering into the in-person phase, where experts and practitioners of these fields will give further insight.

Learning Outcome:

The goal is to provide the students with a blended learning approach to the course content on the mathematics of quantum theory with special emphasis on complex quantum many-body systems and quantum information theory. The main intended learning outcomes include an overview of the latest developments in those fields. Furthermore, the course will help the participants to develop competencies to engage in self-organized cross-university and interdisciplinary collaborations via online group work as well as to give and receive peer-feedback on results. Inviting external experts in the field will also give young researchers in the field the opportunity to grow their professional network considerably.

Podcasts offer researchers diverse opportunities to participate actively and creatively in public debates and present their research. Whether in the form of a personal interview, a background report or an entertainingly moderated show, in podcasts listeners get to know the people behind the science.

What are the current trends on the worldwide podcast scene? How do you turn a good idea into a viable concept for a podcast? What do you need to produce your own podcast? What are the dos and don’ts of planning and implementation? And how do you cut a podcast?

The online podcast seminar introduces you to the world of podcast science communication. You will get tips, learn the tricks of using podcasts for your research topic and discover how to build a podcast step by step by means of practical exercises: from the initial concept through the production phase to subsequent marketing.

In addition to work during the seminar, the online sessions will be followed by two assignments that are a constituent part of the seminar to be done at home. Please allow some extra time for them.

Thematic focus

- (Science) Podcasts: what formats and trends are there?
- Planning and drawing up your own podcast
- Writing for the ear: how do you generate images in the mind?
- Step by step to your first podcast audio clip
- Sound and technology: how do you create a rich sound without a recording studio?
- Marketing: how do I reach listeners with my podcast?

Single-cell RNA-sequencing records the expression values of thousands of genes in individual cells, making the study of cell functionality and development possible at high resolution. To understand the structure of single-cell data on a global level, topological descriptors, such as the presence of loops or voids in the data, are useful. For instance, loops might challenge the assumption of hierarchical cell development or be indicative of missing data in datasets of fully differentiated cells. The established computational tool for detecting the topology of point clouds is persistent homology. However, single-cell data is high-dimensional and notoriously noisy. We show that in high dimensionality traditional persistent homology becomes very sensitive to noise and fails to detect the correct topology. The same holds true for existing refinements of persistent homology. As a remedy, we find that spectral distances, such as diffusion distance and effective resistance, allow persistent homology to detect the correct topology even in the presence of high-dimensional noise. In particular, persistent homology with spectral distances can robustly detect the correct topology of single-cell data.
For more see the abstract_file:

Prerequisites:

Experience or interest in publishing your Python code and a laptop is required.

Summary:

In this course we will learn how to package a Python library, how to publish it on PyPI and on conda-forge, as well as look at more advanced topics like building pre-compiled wheels including c++ extensions using pybind11, and automatically publishing new releases using continuous integration and cibuildwheel.
Learning Objectives

After the course participants should be able to:

- Create a modern pyproject.toml Python package
- Publish this package to PyPI
- Set up continuous integration to automatically publish to PyPI
- Understand the basics of conda-forge publishing
- Create binary wheels including c++ pybind11 extensions

14:30: Anna Lena Schaible (Supervisor T. Buck)
“A numerical investigation on galactic outflows using galaxy simulations and MUSE and ALMA data”

14:50: Miguel Angel Ibarra Arellano (Supervisor D. Schapiro)
“Leveraging spatial-omics through the development of accessible machine learning frameworks with a focus on tissue architecture”

15:10: Josef Martinek (Supervisor R. Scheichl)
“Monte Carlo methods for Particle Transport”

15:30: Charlotte Boys (Supervisor J. Saez-Rodriguez)
“Multi-omics analysis of IgA nephropathy”

From blogging to news and opinion articles, writing remains one of the best ways for scientists to reach the public. While there are many styles and formats for popular science writing, certain elements of good writing are required. In this workshop, we discuss the various formats of writing as well as why and when each is used. We will also discuss how to tell a good story and common bad habits scientists have when writing for the public. Participants will practice writing short pieces based on their own work or interests that will be shared and discussed during the workshop.

All participants need a microphone and ideally a webcam.

Hardware integrated into the laptop is sufficient.

Topics (selection)

- Formats (blogs, news, opinion, etc.)
- Writing styles
- Elements of a good story
- Story structure
- Common mistakes (passive voice, jargon, etc.)

In recent years, machine learning (ML) and artificial intelligence (AI) methods have begun to play a more and more enabling role in the sciences and in industry. In particular, the advent of large and/or complex data corpora has given rise to new technological challenges and possibilities. In his talk, Müller will touch upon the topic of ML applications in the sciences, in particular in chemistry and physics. He will also discuss possibilities for extracting information from machine learning models to further our understanding by explaining nonlinear ML models. Finally, Müller will briefly discuss perspectives and limitations.

About Prof. Müller:

Klaus-Robert Müller has been a professor of computer science at Technische Universität Berlin since 2006; at the same time he is directing rsp. co-directing the Berlin Machine Learning Center and the Berlin Big Data Center and most recently BIFOLD. He studied physics in Karlsruhe from1984 to 1989 and obtained his Ph.D. degree in computer science at Technische Universität Karlsruhe in 1992. After completing a postdoctoral position at GMD FIRST in Berlin, he was a research fellow at the University of Tokyo from 1994 to 1995. In 1995, he founded the Intelligent Data Analysis group at GMD-FIRST (later Fraunhofer FIRST) and directed it until 2008. From 1999 to 2006, he was a professor at the University of Potsdam. From 2012 he has been Distinguished Professor at Korea University in Seoul. In 2020/2021 he spent his sabbatical at Google Brain as a Principal Scientist. Among others, he was awarded the Olympus Prize for Pattern Recognition (1999), the SEL Alcatel Communication Award (2006), the Science Prize of Berlin by the Governing Mayor of Berlin (2014), the Vodafone Innovations Award (2017), Pattern Recognition Best Paper award (2020), Digital Signal Processing Best Paper award (2022). In 2012, he was elected member of the German National Academy of Sciences-Leopoldina, in 2017 of the Berlin Brandenburg Academy of Sciences, in 2021 of the German National Academy of Science and Engineering and also in 2017 external scientific member of the Max Planck Society. From 2019 on he became an ISI Highly Cited researcher in the cross-disciplinary area. His research interests are intelligent data analysis and Machine Learning in the sciences (Neuroscience (specifically Brain-Computer Interfaces, Physics, Chemistry) and in industry.

In this talk, I will venture a glimpse into the future of cutting-edge numerical methods and software for computational fluid dynamics (CFD) for many aerospace applications. While established, off-the-shelf commercial solutions for a broad range of applications exist, the current push towards fundamental changes in the fields of energy generation and transport necessitates computational tools that can keep pace and facilitate discovery through computation.
This talk will give an overview of the ingredients of modern simulation software for CFD, in particular for multi-scale, multi-physics and multi-fidelity problems. Among these are high-order discretization schemes that can adapt locally in space and time to the underlying resolution requirements and thus combine accuracy and robustness. While the potential of leveraging data-driven approaches has become apparent, I advocate for a shift from a purely data-driven to a data-informed approach, which combines both machine learning models (ML) and classical solution schemes consistently. Last but not least, modern discretization methods and ML/CFD hybrid schemes must produce efficient and reliable results. For this, GPU-parallelization and an incorporation of the FAIR principles into the simulation stack must be part of the next generation CFD solvers.

Working as a PhD student you have the challenging task of developing research findings and write you doctoral thesis within three years. This alone is a demanding job. In addition, it is vital to the scientific process that your findings are presented to the scientific community. For most PhD students this is the first big project in their professional life and it could have a crucial impact on their future professional career. PhD students are highly motivated when they start their PhD studies but may underestimate the need for professional management for this three-year project "doctoral thesis".

This seminar demonstrates how to approach the doctoral thesis in a professional way. Project management tools and techniques are used, tailored to the specific situation of PhD students. You will learn how to set a project vision, define clear objectives, gain buy-in from your supervisor and other colleagues in your group, and how to develop a project plan, which is structured and at the same time flexible enough to easily adjust to unexpected findings. You will establish a "controlling cycle" which helps you to recognize risks and problems as early as possible, and you will learn how to manage critical situations and deal with ups and downs. Furthermore, networking with colleagues, supervisors and other people are important topics of this seminar.

Throughout the seminar, you will work on your own doctoral thesis and share your experience with others. This seminar is most beneficial for PhD students who are in the early phases of their doctoral thesis. At the end of the seminar you will have established a strategy on how to approach your own doctoral thesis. During the follow-up REVIEW we will share experience and best practices and deal with open questions from the first module.

This seminar will help you to make the most effective use of your three years and finish your doctoral thesis on time.
You will also learn and practice the basic concepts of project management – a prerequisite in industries and research institutions. Working as a PhD student you have the challenging task of developing research findings and write you doctoral thesis within three years. This alone is a demanding job. In addition, it is vital to the scientific process that your findings are presented to the scientific community. For most PhD students this is the first big project in their professional life and it could have a crucial impact on their future professional career. PhD students are highly motivated when they start their PhD studies but may underestimate the need for professional management for this three-year project "doctoral thesis".

This seminar demonstrates how to approach the doctoral thesis in a professional way. Project management tools and techniques are used, tailored to the specific situation of PhD students. You will learn how to set a project vision, define clear objectives, gain buy-in from your supervisor and other colleagues in your group, and how to develop a project plan, which is structured and at the same time flexible enough to easily adjust to unexpected findings. You will establish a "controlling cycle" which helps you to recognize risks and problems as early as possible, and you will learn how to manage critical situations and deal with ups and downs. Furthermore, networking with colleagues, supervisors and other people are important topics of this seminar.

Throughout the seminar, you will work on your own doctoral thesis and share your experience with others. This seminar is most beneficial for PhD students who are in the early phases of their doctoral thesis. At the end of the seminar you will have established a strategy on how to approach your own doctoral thesis. During the follow-up REVIEW we will share experience and best practices and deal with open questions from the first module.

This seminar will help you to make the most effective use of your three years and finish your doctoral thesis on time.
You will also learn and practice the basic concepts of project management – a prerequisite in industries and research institutions.

In this lecture, we cover concepts and selected techniques in visual data analysis. We investigate approaches for data transformation, visual representation, and interactive exploration, and their role in visual sense-making.

Lab presentations:
Tristan Bereau, Fred Hamprecht, Caroline Heneka

Science talks:
• Tristan Bereau: Machine learning representation for molecular dynamics
• Roman Remme (Hamprecht lab): Machine Learning meets Density Functional Theory 
• Lara Alegre (Heneka lab): Machine Learning for large radio surveys

Navigating the challenges of the global COVID-19 pandemic required strategic decisions tailored to each country's unique circumstances. In less developed nations, the threat of overwhelming hospital capacity was especially severe. Rather than building scenarios based on mathematical models, our team used a dynamic forecasting approach. We developed a series of models that provided 4-week probabilistic forecasts, complete with uncertainty quantification. These forecasts, crucially informed by real-time data, predicted the demand for hospital beds and ventilators, which served as the backbone for decisions and public policies adopted by federal health authorities in Mexico from April 2020 to January 2022. The journey was a challenging one. An incompletely characterized virus and the unpredictable dynamics of societal behavior made crafting a useful model difficult.

In this talk, I will present a retrospective of these models and critically review their objectives, successes, and limitations. Our methodology and modeling decisions will be presented, from the intricacies of data used for model fitting to the balance between model complexity and parameter identifiability. The predictive power and performance of our models will also be reviewed. Beyond the algorithms and forecasts, I'll share our experiences collaborating with health authorities and communicating with the general public. Discover the highs and lows, the challenges faced, and the insights we gained.

This presentation consists of two parts and perspectives.

PD Delia Dumitrescu from social/political sciences will speak about communication research using a multi-country, multi-language data set containing COVID-19 social media disinformation posts. Delia will present the initial difficulties related to the dataset and how she approached the Scientific Software Center for support. This support resulted in AMMICO (AI Media and Misinformation Content Analysis Tool): AMMICO is a publicly available software package written in Python 3, whose purpose is the simultaneous evaluation of the text and graphical content of image files. After describing the software features, Delia will provide an assessment of its performance using the social media disinformation posts. This part will conclude by highlighting the tool’s advantages for communication research and by discussing further development ideas.

In the second part of the talk, Dr Inga Ulusoy from the Scientific Software Center (SSC) will speak about the role of the SSC in this project. The initial consultation evolved into a software development project of the SSC. Inga will describe how the project and communication was structured and how the software was designed to fit the needs of the researchers. In the end, Inga will demonstrate how AMMICO can be utilized in further research projects, and conclude by summarizing the ways the SSC can support research projects through research software development and the implementation of software engineering best practices.

Neuroscience-inspired artificial intelligence aims to develop AI systems that learn and act in ways similar to the human brain. This talk will discuss some recent advances in this field, starting with successful examples of neuroscience-inspired machine learning approaches, such as Vector Symbolic Architectures, Hierarchical Temporal Memory, and FlyHash. We also outline recent studies on the similarity of processing in transformers, the deep learning models at the core of Large Language Models, and the structure of brain activities recorded with fMRI during listening tasks. Finally, we discuss the fundamental differences between backpropagation and likely biological learning mechanisms, and explore alternative learning algorithms such as Geoffrey Hinton's forward-forward algorithm. We conclude by examining the implications of these findings for open problems in AI such as one-shot learning, model robustness, and explainability.

Numerical simulations are widely used as a third pillar besides experimental and theoretical investigations in many sciences such as physics and chemistry as well as engineering science. It requires the development of robust and efficient numerical methods for the resolution of the underlying physical laws that arise often in form of partial differential equations (PDEs). In this talk, I will describe two interdisciplinary stories. While illustrating the key-ideas of the theory and methods, I will also highlight the occasions where not only the mathematical tools have been successfully developed and transferred to the application but also where the interdisciplinary interactions raised new mathematical questions and triggered new answers and theories in mathematics. From the application viewpoint, this talk will touch upon implicit solvation models and Born-Oppenheimer molecular dynamics but the methods rely on various and divers mathematical concepts from Grassmann manifolds to descriptors from machine learning and perturbation theory.

Prerequisites:

No prior knowledge by the participants is necessary to participate in this course and it is intended for all scientific audiences. Participants are required to bring their own laptops to work on during the course. Network access (e.g. through Eduroam) is recommended.

Summary:

The Unix shell is a powerful tool that allows people to do complex things with just a few keystrokes. More importantly, it helps them combine existing programs in new ways and automate repetitive tasks so they aren’t typing the same things over and over again. Use of the shell is fundamental to using a wide range of other powerful tools and computing resources. The course will include hands-on live coding sessions where participants exercise the learned commands on their own computers.

Version control is the lab notebook of the digital world: it is used to keep track of what was done and to collaborate with other people. Its use is the state of the art in software development projects of all scales. However, it is not limited to software: books, papers, small data sets, and anything that changes over time or needs to be shared can and should be stored in a version control system. The course will include hands-on live coding sessions where participants exercise the learned commands on their own computers.
Learning Objectives

After the course participants will:

- Have a fundamental understanding of how and why to use the Unix Shell
- Be comfortable with handling files and directories using the command line
- Have experience with advanced usage of the shell e.g. loops, pipes, redirects etc.
- Know how to write their workflows as reusable shell scripts
- Understand the benefits of using version control
- Understand basic git terminology
- Have a good working knowledge of common tasks in Git
- See how Git repositories can help them to move towards practicing Open Science

Feeling anxious about an upcoming international conference? Is public speaking one of your greatest fears? Unsure how you will sound in front of an audience of experts? The aim of this workshop is to help you plan and deliver an effective academic presentation in English. The workshop will be divided into two parts:

Part 1: the nuts and bolts of an effective academic

Part 2: interactive and practice session with attention to both rhetorical devices and non-verbal communication strategies.

The workshop’s ultimate goal is to help you gain a sense of comfort and confidence while presenting your research in public. Participants are encouraged to bring a sample presentation of their own for practice.

Objectives:

- Identify and review rhetorical and oral presentation strategies
- Develop confidence and poise for the effective delivery of your research results
- Help you develop your own presentation style

Mutual predictability is the key to interaction. Or in simpler terms: "experience makes teamwork". Of course, prediction isn't all that simple. We'll look at "generative AI", but without the hype, and see how we use that to learn the dynamics of complex stochastic systems, use that to predict -- and control. And give some examples, in robotics and beyond, of where this can be used.
For more see the abstract_file:

Lab presentations:
Christoph Dieterich, Sandy Engelhardt, Britta Velten

Science talks:
• Adrian Chan (Dieterich lab): Fingerprinting your RNA - one molecule at a time
• Sven Köhler (Engelhardt lab): 4D Self-Supervised Deep Cardiac Motion Modelling
• Florin Walter (Britta Velten lab): Probabilistic Factor Models for Subcellular Spatial Transcriptomics

Collecting, processing and analyzing data are central activities for virtually every researcher. Topics like data sharing and data publication are becoming increasingly important. Nevertheless, many research projects lack a structured and well-organized data management. This course is meant to give a general, discipline-independent introduction into various topics central to an efficient management of research data with a special focus on questions related to data archiving and data sharing. Both are central aspects of good scientific practice. Archiving and long-term preservation of research data are prerequisites for the scrutiny of scientific results based on the analysis of this data. Data sharing on the other hand increases transparency of research results and enables possible re-usage of data for new research questions, in combination with additional data sets and in interdisciplinary contexts.

14:30 Armand Rousselot (Supervisor U. Köthe)
"Invertible Neural Networks for Model-Based AI"

14:50 Maike Rees (Supervisor L. Maier-Hein)
“Deep learning for functional parameter quantification in medicine”

15:10 Lin Tian (Supervisor A. Dreuw)
“Theoretical Description and Prediction of Molecular Properties of N-Heteropolycycles through quantum chemical methods”

15:30 Julian Heidecke (Supervisor J. Rocklöv)
“Climate Change and infectious diseases – the case of West Nile virus: A modeling and data science approach”

In the academic world, clear, concise, and well-written texts play an important role in convincing journal editors and conference organisers to accept a paper for review and publication, or to invite a researcher to present at a conference. This workshop supports PhD students and novice researchers who need to produce scientific papers in English. Providing participants with strategies to write short texts efficiently and effectively, it also focuses on standards of professional papers aimed at Anglo-Saxon or international research communities. It enables participants to organise ideas and structure texts effectively, to present their own and other researchers‘ findings and opinions appropriately, and to use correct terminology and vocabulary.

During the workshop, participants work with their own texts as well as with examples from their own disciplines that they bring along and consider to be particularly well written. They discuss features of good scientific papers and are equipped to use adequate language in different genres and for different audiences. In addition, they receive peer feedback on their own drafts. All exercises empower them to produce clearer, and more correct, concise, and reader-oriented papers.

It’s far more than “just” a presentation: defending your thesis is the last (and one of the biggest) challenges in your academic qualification – and it doesn’t only require well-prepared content. This workshop addresses aspects of the defense related to communication and presentation. We will work on participants’ body language as well as on strategies for convincing argumentative and rhetorical positioning during the disputation. A significant portion of the workshop will be devoted to simulated defenses and evaluations of the performances.

The understanding of the appearance of extremes in real-life time series (such as weather and climate observations, returns of stock prices, exchange rates, and stock indices, insurance claim data, failures in energy and social networks) requires suitable probabilistic models and their statistical analyses. Over the last 15-20 years such models and statistical tools have been developed under the assumption of serial dependence. They supplement classical extreme value analysis which deals with independent data.

The goals of the course are

- to introduce and discuss the recent developments of extreme value theory in the time series context. The main focus will be on heavy-tail phenomena, where extremes are particularly severe, and clustering effects when extremes appear in clumps, 

- to provide suitable statistical tools for analyzing the aforementioned phenomena,

- to provide relevant knowledge to graduate students about extreme behavior of random systems in contrast to their average behavior, 

- to learn about applications of extreme value theory from top experts in the field.

Prerequisites:

Basic Python knowledge and a laptop is required. Experience with writing tests is not required.

Summary:

An automated test suite makes it much easier to maintain, extend and debug your Python code. In this course we will learn how to write automated tests in Python using the pytest library. After introducing the key concepts, the majority of the course will be hands-on, writing and running tests.
Learning Objectives

After the course participants should be able to:

- Install and run pytest
- Write simple tests
- Use temporary files in tests
- Use fixtures to manage resources
- Parametrize tests
- Add an automated test suite to their existing python projects

You will learn to visually communicate your complex research ideas and results so your messages are effortlessly understood by any specific audience (scientists or non-scientists). We will not focus on aesthetics but on how understanding human visual perception can inform your design decision for better comprehension of your scientific images, posters, and slides. You will also design a graphical abstract of your research, discuss it with peer scientists in a group exercise, and get actionable advice and feedback on your own materials. It is an immersive workshop, comprehensive, structured, memorable, easy to follow, useful and fun. More at https://www.seyens.com

Content & Method:

The training is offered as blended learning that combines a self-study module and a live online workshop. All participants get 12 month access to all materials.

Teaching methods:

The course is divided into six blocks (half days). Each block will consist of a mixture of presentations and hands-on exercises. In the presentations we take starting point in real-world datasets and scientific problems and discuss the relevant statistical methods. Focus is on the link between data and methods, implementation, and interpretation of results. Exercises will be practical, with exploration and analysis of real-world datasets. Participants are expected to use the statistical software environments R and RStudio for the exercises.

Teaching material:

The teachers will provide slides, programs/output from R Markdown, data, and exercises. Apart from this, notice that the book R for Data Science (https://r4ds.had.co.nz or https://r4ds.hadley.nz) is useful for data manipulation and visualization.

Tentative plan:

Each block (A-F) is expected to last for a morning/afternoon.

A. Recap (getting a common language)

• Basic statistical principles (data types, random variation, estimation, hypothesis tests, etc.)
• Workflow with R Markdown

B. Models for continuous outcomes

• Models for regression, ANOVA, clustered/blocked data (linear mixed models)
• Model diagrams
• Hypothesis tests for overall effects in regression, ANOVA, linear mixed models

C. Elaborate analysis of data with continuous outcome

• Estimated marginal means (emmeans)
• Pairwise comparisons and adjustment for multiple testing
• Statistical power

D. Modifications of models and analyses for continuous outcomes

• Model validation and data transformation
• Bootstrap computation of standard errors and p-values

E. Models with non-linear relationships

• Additive models (GAMs)
• Non-linear regression

F. Prediction with multiple/many covariates

• Preprocessing
• Training/validation/testing
• Regression-based models for prediction
• Principal component analysis (PCA)

This second seminar is planned as follow-up session to the Introduction seminar for those researchers who would like to review their own work in terms of potential patent protection. For that purpose, participants of the Introduction seminar will be asked to provide a short summary of their own already-disclosed innovations. Three to four selected cases will be analyzed and discussed with all participants with regard to the patentability requirements.
Concerning the patentability of not-yet-disclosed innovations, at the end of the workshop there will be the possibility to plan specific one-to-one meetings to avoid compromising the novelty of the underlying subject matter.

11:00 Antonia Papapostolou (Supervisor A. Dreuw)
“Development of Quantum Chemical Methods for the Calculation of Molecular Response Properties”

11:20 Adrian Müller (Supervisor A. Dreuw)
“Quantum mechanical method development for MOST systems”

11:40 Friederike Schneider (Supervisor A. Dreuw)
"High-Performance Quantum Chemical Computing"

12:00 Panasun Manorost (Supervisor P. Bastian)
“Surface-subsurface flow simulation. Creating Method and Application”

12:20 Pascal Lafrenz (Supervisors H. Kaessmann, B. Velten)
"Multi-omics data integration and evolutionary models to understand de novo gene birth and human evolution"

14:00 Leonie Kreis (Supervisor R. Herzog)
„Multilevel architectures and algorithms in deep learning“

14:20 Yannick Pauler (Supervisor C. Rother)
“Recovering 3D Information from a Moving Camera"

14:40 Leo Nguyen (Supervisor U. Koethe)
„Advancing Lithium Battery Design through Machine Learning using Invertible Neural Networks“

This seminar illustrates the strategic relevance of patents in the field of software for academic institutions and explains the requirements for software-based innovation to be considered as patentable software inventions. In particular, a series of examples will demonstrate opportunities and pitfalls when protecting software-based innovation by means of patent applications. One goal of the seminar is to provide researchers with a tool which they can use for assessing their own research results with regard to the potential that the underlying innovation can be protected via a patent.

In the 21st century, society faces multiple and highly interwoven global health challenges, linked to accelerating climate change, antimicrobial resistance, food and water insecurity, and an increasing number of infectious diseases. Threatening convergence, these health issues call for more effective and sustainable solutions to ensure that the global population continues to thrive. Scientists and researchers can massively contribute to discovering, co-designing, and evaluating these solutions.

In this context, Nature-based Solutions (NbS), while not yet commonplace, are increasingly gaining recognition alongside more conventional engineering solutions and healthcare interventions. The concept of NbS describes the innovative use of existing knowledge about natural systems to assist society in effectively addressing contemporary environmental, social and economic challenges while simultaneously providing ecological and health benefits. These win-win NbS include the management, restoration and protection of ecosystems, strategies for climate adaptation and mitigation and the greening of infrastructure.

The Summer School will explore the innovative potential of NbS to tackle two pressing global health challenges – climate change and pandemic prevention, which are highly interconnected and provide opportunities to design, apply and evaluate a range of win-win NbS to inform future policies based on scientific evidence.

The guest speakers will share their expertise and experience with the participants and will collaboratively work together on a capstone project, relevant to the development and presentation of a research project on NbS to tackle climate change and pandemics.The Summer School will promote interdisciplinary thinking and will encourage participants to bring examples of NbS beyond these two challenges for discussion to consider how to develop NbS that address concrete challenges and implement solutions in very diverse settings. As a result, the Summer School will allow participants to further connect, interact, and build a worthwhile network of expertise on NbS.

Advanced statistical methods are rapidly impregnating many scientific fields, offering new perspectives on long-standing problems. In materials science, data-driven methods are already bearing fruit in various disciplines, such as hard condensed matter or inorganic chemistry, while comparatively little has happened in soft matter. I will describe how we use multiscale simulations to leverage data-driven methods in soft matter. We aim at establishing structure-property relationships for complex thermodynamic processes across the chemical space of small molecules. Akin to screening experiments, we devise a high-throughput coarse-grained simulation framework. Coarse-graining is an appealing screening strategy for two main reasons: it significantly reduces the size of chemical space and it can suggest a low-dimensional representation of the structure-property relationship. I will briefly mention a biological application of our methodology that led to the discovery of in vivo active compounds. Finally, I will mention a number of ways machine learning can help fulfill the promise of connecting models at different scales.

Biosketch:

Tristan Bereau is a computational physicist working at the interface between multiscale modeling and machine learning for soft matter and biomolecules. He earned a Ph.D. in Physics at Carnegie Mellon University. After a postdoc at the University of Basel, he led an Emmy Noether research group at the Max Planck Institute for Polymer Research. He then moved to the University of Amsterdam as an assistant professor in chemistry and computer science, followed by a role in Industry. Tristan serves on the editorial boards of the journals Machine Learning: Science & Technnology and Computational Science and Engineering. He is currently a professor at the Institute for Theoretical Physics at the University of Heidelberg.

Convolutional Neural Networks (CNN) are the current backbone of deep learning architectures in a wide range of applications which process array data, like 2D/3D images. Despite this overwhelming success of the application of CNNs in terms of qualitative (visual) results and classification test set accuracies, this talk will point out some servere inherent problems of CNNs regarding their insufficient signal processing capabilities.
While these “signal processing flaws” have been largely ignored as test accuracies on many problems have been increasing over many years, recent research showed that current models are highly vulnerable to even the slightest changes in input distributions. In our latest works [1-5], we showed that the missing “robustness” of CNNs is not only due to insufficient training data, but to a large extend is also caused by faulty operators and architectures which are failing to adhere to basic signal processing demands. The aim of this talk is to give an overview of the dominant problems in the context of image processing and analysis and discuss possible counter measures.

References:
[1] Durall, R., Keuper, M., & Keuper, J. (2020). Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions. CVPR 2020
[2] Grabinski, J., Keuper, J., & Keuper, M. (2022). Aliasing and adversarial robust generalization of CNNs. Machine Learning, 111(11), 3925-3951.
[3] Grabinski, J., Jung, S., Keuper, J., & Keuper, M. (2022). FrequencyLowCut Pooling--Plug & Play against Catastrophic Overfitting. ECCV 2022
[4] Grabinski, J., Gavrikov, P., Keuper, J., & Keuper, M. Robust Models are less Over-Confident. NeurIPS 2022
[5] Gavrikov, P., & Keuper, J. (2022). CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters. CVPR 2022 (oral)

In 2021 a long-term HPC infrastructure (“Nationales Hochleistungsrechnen”; NHR) has been established to further develop HPC capabilities, HPC expertise and training offerings at German universities. Nine NHR centers are now offering their compute services, support activities and training free of charge to researchers at all German universities. These centers coordinate their investments as well as application and support profiles within the NHR-Verein.
The presentation will introduce the structure of the NHR program, central NHR activities of the NHR-Verein and the structure of the application process. It will specially report on offerings and developments at the NHR center at Friedrich-Alexander-Universität Erlangen-Nürnberg (NHR@RFAU) which focuses on atomistic applications and node-level performance engineering.

This hands-on workshop provides an overview of the German research funding landscape and explains how to exploit funding opportunities at the postdoctoral stage for further career advancement. Selected EU programmes within the framework of Horizon Europe are also covered. Practice scenarios for different research settings help to understand how to select and approach individual funding programmes to establish yourself as a principal investigator and build your research capacity.

This highly interactive workshop employs theoretical input, plenary discussions, practice scenarios, individual and small group work, experience sharing, and expert advice.

The school spreads over five days with theory sessions in the morning and hands-on exercises in the afternoon. The list of topics covered includes:

- Numerical methods for quantum dynamics
- MCTDH theory
- Hands-on exercises with the Heidelberg MCTDH package
- Introduction to polyspherical coordinates
- Mode combinations and multilayer tree generation
- Potential energy operators in sum-of-products form
- MCTDH for bosons and fermions (MCTDH-X)
- Density matrix renormalization group (DMRG) through the lens of MCTDH
- DMRG in electronic and vibrational problems

In this workshop we will analyze examples of effective and ineffective texts aimed at communicating research to a general audience. We will share tips and tricks on effective science writing, and finally, work in groups on translating a technical paper or report into an effective text that addresses a general audience. Participants can send proposals of papers and reports drawn from their research area, to be worked on in the workshop by 4th June (email address: nm-gastprofessur@mk.uni-heidelberg.de)

Lab presentations:
Jan Budczies, Daniel Durstewitz, Carsten Rother

Science talks:
• Iordanis Ourailidis (Budczies lab): AI-based detection of tumor budding in head and neck cancer
• Florian Hess (Durstewitz lab): Learning generative models of dynamical systems from time series data
• Felix Draxler (Rother lab): Generative Models - Applications and Guarantees

Machine-learning, which can learn to predict given labeled data, bares many promises for medical applications. And yet, experience shows that predictors that looked promising most often fail to bring the expected medical benefits. One reason is that they are evaluated detached from actual usage and medical outcomes.
And yet, test runing predictive models on actual medical decisions can be costly and dangerous. How do we bridge the gap? By improving machine-learning model evaluation. First, the metrics used to measure prediction error must capture as well as possible the cost-benefit tradeoffs of the final usage. Second, the evaluation procedure must really put models to the test: on a representative data sample, and accounting for uncertainty in model evaluation.

Biosketch:

Gaël Varoquaux is a research director working on data science at Inria (French Computer Science National research) where he leads the Soda team on computational and statistical methods to understand health and society with data. Varoquaux is an expert in machine learning, with an eye on applications in health and social science. He develops tools to make machine learning easier, suited for real-life, messy data. He co-funded scikit-learn, one of the reference machine-learning toolboxes, and helped build various central tools for data analysis in Python. He currently develops data-intensive approaches for epidemiology and public health, and worked for 10 years on machine learning for brain function and mental health. Varoquaux has a PhD in quantum physics supervised by Alain Aspect and is a graduate from Ecole Normale Superieure, Paris.

This talk presents a feasible study of optimal control techniques for cardiac defibrillation based on the bidomain-bath equations posed on a rabbit ventricle geometry. The bidomain model consists of a system of elliptic partial differential equations coupled with a non-linear parabolic equation of reaction-diffusion type, where a set of ordinary differential equations describes the reaction term, modeling ionic transport. Since ODEs describe the ionic currents in the tissue, the PDE part dominates the solving effort. Thus, it is unclear if commonly used splitting methods can outperform a coupled approach by maintaining good accuracy. The results will be presented in the first part based on comparing the coupled solver approach with commonly used splitting schemes to solve more sophisticated physiological models. Consequently, the novel memory-efficient computational technique will be demonstrated to solve the coupled systems of equations. In the second part, the optimal control approach for successful cardiac defibrillation will be shown based on minimizing a properly chosen cost functional depending on the extracellular current as input at the boundary of the bidomain-bath domain.

Biosketch:

Nagaiah Chamakuri got his M.Tech at IIT Madras, India, in 2002 and obtained his Ph.D. from Otto-von-Guericke University in 2007. After that, he worked as a Postdoc at several places in Austria and Germany. He worked as an Associate Professor at Mahindra-Ecole Centrale, Hyderabad from 2016-2019. Since 2020, he has been working as a faculty at the Indian Institute of Science Education and Research (IISER), Thiruvananthapuram, India. His scientific interests include the numerical solution of partial differential equations, Optimal control of PDEs, applications in fluid flow, Computational biology, and High-performance computing.

The human brain is one of the most complex and fascinating structures. It is still far from understood and therefore considered one the last biological frontiers. Its function is the result of intricate signal transmission networks and reactions occurring within and between neurons. To model and simulate such complexity, multiscale models are required that are aimed at reproducing brain function through a bottom-up approach, from the molecular to the whole brain level. This workshop will provide training in some of the computational tools developed in the Human Brain Project that enable brain simulation and modelling at the molecular and subcellular levels. This workshop is intended to offer an overview of these tools and their application and provide students and young researchers with the necessary theoretical background and practical experience to enable them to apply them in their own research. There will be a mix of lectures and practical session as well as discussions of case studies and the participants' own research problems.

In the early 1800s, Thomas Young performed a homespun experiment with a sunbeam split into two, and challenged our understanding of the nature of light. With the birth of quantum mechanics, starting in the early 1900s, this "double-slit" experiment—now done with individual particles—started challenging our ideas about the nature of reality. How can a single particle behave both like a particle and a wave? Does a particle, or indeed reality, exist before we look at it, or does looking create reality? Is there a place where the quantum world ends and the familiar classical world of our daily lives begins, and if so, can we find it? In the 1960s, Richard Feynman said that the deceptively simple double-slit experiment contains the "central mystery" of the quantum world. Even he would have been amazed at just how far physicists have pushed this experiment, and how it still continues to confound and challenge our intuitions about the nature of reality.

The IWR Colloquium will be held as an in-person event at the Mathematikon. In addition it will be streamed via Zoom.
For more information please visit the website of the colloquium.

Abstract:

I will discuss our efforts to use machine learning (ML) to accelerate the computational tailoring and design of transition metal complexes for catalysis and metal-organic framework (MOF) materials in spaces of millions to tens of millions of materials. Traditionally, computational modeling with high-throughput screening based on density functional theory (DFT) has been employed for data generation either for direct discovery or when paired with regression ML models. I will describe how we have addressed some of the challenges of applying these workflows in regimes of scarce data. I will describe how we have achieved 1000-fold accelerated discovery of light-harvesting complexes, single-site catalysts, and energy storage materials. To accomplish this, we have quantified uncertainty using measures of the latent space to improve model confidence as well as to drive efficient global optimization workflows with multitask neural networks. I will also describe how we have leveraged natural language processing to extract, learn, and directly predict experimental measures of stability on heterogeneous MOF materials. I will also touch upon how we are beginning to overcome one chief limitation of accelerated catalyst discovery by using diffusion models to predict transition state structrues, the rate limiting step in conventional computational modeling of catalysts.

About the HGS MathComp Romberg Program:

With its founding in 2007 the HGS MathComp implemented the distinguished Romberg Guest Professorship. With this program the graduate school invites leading researchers working in a field relevant to the graduate school for a longer stay and to participate in the study training program.
The guests invited via the HGS MathComp Romberg Program make substantial contributions to the study program by complementing the teaching done by the members or by disseminating their latest research results at the school.

For more information please visit the website of the Romberg Program.

The lecture will be held in German and English.

The emergence and transmission of pathogens that cause infectious diseases is an increasing threat in Europe, fueled by climate change, globalization, increasing international mobility, amongst many other broad societal and environmental transformations. Since the 2000s, The Upper Rhine Valley in Southwestern Germany has emerged as a hotspot for invasive Asian tiger mosquitoes with their populations persisting despite control and eradication efforts by local and regional administrations.

To address the growing public health risk endemic Asian tiger mosquitoes populations pose in relation to the spread of infectious diseases, it is critical that efforts are undertaken to evaluate the conditions contributing to the establishment of Asian tiger mosquitoes populations as well as how efforts to mitigate and respond to climate change may actually make local environments more suitable to these disease vectors amongst others.

Join professors from Universität Heidelberg to learn about these threats and the impacts of climate change and mobility on disease vectors such as mosquitoes. Speakers will address current research at Universität Heidelberg and its partner institutions as well as the citizen science initiative Mosquito Alert which is working to tackle invasive mosquito populations across the globe.

Speakers:

Prof. Till Bärnighausen • Heidelberg Institute of Global Health (HIGH) & IWR
Dr. Norbert Becker • Centre for Organismal Studies (COS)
Prof. Joacim Rocklöv • IWR & HIGH
Prof. Carsten Wergin • Faculty of Behavioral and Cultural Studies
Prof. Alexander Zipf • Heidelberg Institute for Geoinformation Technology (HeiGIT) & GIScience

May 8, 2023 • 10:15 - 11:45
May 15, 2023 • 14:00 - 15:30

Machine learning holds great promise in accelerating chemical discovery and materials discovery. In the chemical sciences, data quality and availability are among the biggest challenges for predictive machine learning accelerated discovery. This includes whether electronic structure methods are sufficiently accurate for data generation or if experimental data of suitable quality is available. I will describe some techniques and approaches to overcome these limitations to design novel materials, including catalysts, photoactive complexes, and metal-organic frameworks

Datum: Do./Fr., 11./12.05.23. 09:00-15:00
Link Online-Registrierung [Workshop: „Fast am Ziel angekommen: Erfolgreich und gestärkt in die Disputation gehen]“

Beschreibung:

Eine lange Phase der wissenschaftlichen Ausbildung liegt nun hinter Ihnen. Nun liegt noch der letzte große Meilenstein, die mündliche Verteidigung, vor Ihnen. Wahrscheinlich schauen Sie diesem Tag nicht nur mit Gelassenheit und Freude entgegen, sondern es macht sich Zweifel und Nervosität breit.

Ausgehend von Ihren individuellen Erfahrungen werden Sie in diesem Workshop Präsentationstechniken und Verteidigungsstrategien kennenlernen, die es Ihnen ermöglichen, Ihre eigenen Forschungsergebnisse sicher und überzeugend darzustellen und auch für kritische Rückfragen gewappnet zu sein. Nur so können Sie souverän zwischen Leinwand und Publikum vermitteln.

Anhand von Ihrer zu Beginn durchgeführten kurzen Dissertations- Präsentation werden Sie ein differenziertes Videofeedback erhalten, um Ihre Stärken zu verdeutlichen sowie Möglichkeiten aufgezeigt, diese weiter auszubauen. Darüber hinaus werden der formale Ablauf und die spezielle Form der Prüfungssituation Teil des Workshops sein. Der Umgang mit Lampenfieber und Nervosität runden die inhaltliche Ausrichtung des Workshops ab, so dass Sie dem krönenden Abschluss Ihrer Dissertation gelassener entgegen sehen können.

Agenda (Themenauswahl):

- Kurz-Präsentation der eigenen Dissertation mit Videoaufnahme und professionellem Feedback

- Bausteine eines Diputationsvortrages

- Was muss ich bei der Vorbereitung meiner Disputation besonders beachten? (Do’s and Don’ts)

- Wie kann ich überzeugend und souverän meine Forschungsleistung vorstellen?

- Wie gehe ich mit Lampenfieber und Nervosität um?

- Welche Reaktions- und Verteidigungsstrategien gibt es und wie kann ich diese einsetzen?

- Wie ist der formale Ablauf in dieser speziellen Prüfungssituation?

Zur Trainerin:

Dr. Angelika Wolf, MethoDactics

Frau Dr. Wolf hat durch mehrjährige und vielfältige Tätigkeiten zahlreiche Erfahrungswerte im Bereich der Erwachsenenbildung, Hochschulforschung, des Wissenschaftsmanagement und in der Wirtschaft gesammelt, die die Inhalte ihrer Workshops prägen. Ihre Promotion hat sie an der Pädagogischen Hochschule Heidelberg an der Schnittstelle von Psychologie und Physikdidaktik abgelegt. Seit 2017 arbeitet sie als freie Trainerin & Karriereberaterin in Deutschland und in der Schweiz (MethoDactics).

Arbeitsschwerpunkte: Persönlichkeitsentwicklung - Gestaltung individueller Karriere- und Lebenswege - Netzwerken in der Wissenschaft; Professionelle Weiterbildung - Wissenschaftliches Arbeiten- quantitative Forschungsmethoden - Disputationstraining

Organized by the Scientific Software Center (SSC).

Summary:
The course will give a brief overview of best practices for the sustainable development of research software. Areas covered are e.g. version control, development workflows, licensing, documentation, software testing, CI/CD, packaging, static code analysis, clean code. The course will emphasize the importance of these topics for the development process, give opinionated tooling recommendations and share experiences. The primary goal of the course is to enable the participants to formulate a roadmap of courses and self-learning activities to further improve their software development skills.

Registration required!

For more information please visit the website of the SSC:
https://ssc.iwr.uni-heidelberg.de/events

May 2-4, 2023

SIMPLAIX is a cooperation between the Heidelberg Institute for Theoretical Studies (HITS), the Karlsruhe Institute of Technology (KIT) and Heidelberg University, focussed on bridging scales from molecules to molecular materials by multiscale simulation and machine learning (www.simplaix.org). The aim of the workshop is to bring together scientists working in the field to share their research and discuss current challenges.

Registration is now open. Registration deadline: 15 March, 2023.

For further information please visit the website of the workshop: https://simplaix-workshop2023.h-its.org/

Organizers:
Rebecca Wade (HITS) • Andreas Dreuw (IWR, Heidelberg University) • Frauke Gräter (HITS) • Fred Hampreccht (IWR, Heidelberg University) • Ganna Gryn'ova (HITS) • Marcus Elstner (KIT) • Pascal Friedrich (KIT) • Ullrich Köthe (IWR, Heidelberg University)

Machine learning galore will feature lab presentations by PIs as well as scientific talks by junior scientists.

Lab presentations:
• Ralf Klessen
• Johannes Schemmel
• Michael Strube

Science talks:
• Victor Ksoll (Klessen lab): 3D reconstruction of interstellar dust distributions with invertible neural networks
• Luca Blessing (Schemmel lab): Event-based Backpropagation for Analog Neuromorphic Hardware
• Wei Zhao (Strube lab): Graph Neural Networks on the Manifold of Symmetric Positive Definite Matrices

To help plan the catering, please register for free by clicking here.

Scientific Machine Learning is a joint initiative from STRUCTURES and IWR aimed at fostering interactions within and development of the local machine learning community. Its portal, http://mlai.uni-heidelberg.de summarizes the many relevant events and news from across campus that would otherwise remain scattered across single institutions or fields.

The goals of the MLAI platform align with the STRUCTURES Cluster of Excellence's objective of driving research into the fundamental understanding of current and future machine learning, and with IWR’s aim to leverage machine learning to enable the solution of long-standing problems in the natural and life sciences, the engineering sciences, as well as the humanities.

Further information and links:

MLAI homepage
Machine Learning Talks on Campus – Information service and mailing list
STRUCTURES Cluster of Excellence

The IWR Colloquium will be held as an in-person event at the Mathematikon. In addition it will be streamed via Zoom.
For more information please visit the website of the colloquium.

Abstract:

The Portal of Medical Data Models (medical-data-models.org) is a multilingual information infrastructure for medical research based on FAIR principles. It provides Europe’s largest collection of medical forms with semantic annotation. Medical metadata can be reused efficiently to build medical databases compliant with standards from regulatory authorities (model-driven software development). Contents can be exported in 20+ formats and used directly in an integrated open source EDC system, which provides an interface to electronic health record (EHR) systems used in routine patient care.

The talk will be held as an in-person event at the Mathematikon. In addition it will be streamed via Zoom.

Zoom Link:
https://eu02web.zoom.us/j/62842665374?pwd=M21aaStjZWRndUdrTzBaM2hRRnJjdz09

Abstract:
The world is changing rapidly, accelerating our need to understand how factors like climate, land use, and social structure affect the ecology of disease on a global stage. Interrogating these processes requires applying advanced analyses across broad datasets to ensure that results are generalisable and complexities are well-understood. However, often, disease ecology analyses take relatively coarse species-level approaches that are fraught with sampling biases and are restricted in their ability to inform finer-scale processes. Further, when these models are used to predict the disease consequences of global change they can be difficult to validate, reducing our confidence in their predictions.

In this talk, I present a series of analyses that we have used to identify macroecological trends of host-pathogen ecology and the changing structure of the global host-virus network, and our efforts to use them to predict elements of global change. I compare and contrast them with other finer-scale studies of disease dynamics that comprise most of the field of disease ecology, and I outline the ways we are attempting to move between these scales of investigation. Ultimately, I discuss the need for “bottom-up” mechanistic prediction of disease dynamics in novel systems, using fine-scale analyses to inform the most important global-scale problems.

“Mathematics of Life” is a special interest group organized by doctoral students of the HGS MathComp.

Link Mathematics of Life:
www.mathcomp.uni-heidelberg.de/mathematics-of-life

March 27-30, 2023 • 10:00-12:00 & 13:00-15:00

This 4-day course offers a compact introduction to mathematical optimization. We invite researchers in non-mathematical fields as well as in mathematics to attend the classes.
The goal of the course is to enable the participants to recognize the characteristics of a given optimization problem, to understand its difficulties and limitations, and to choose suitable solution methods as well as to develop ideas on how to model problems from their own field as optimization problems.

We will focus on the following four categories of optimization problems:
day 1) unconstrained optimization
day 2) convex optimization
day 3) nonlinear optimization
day 4) infinite-dimensional optimization

For each of these problem classes, we will study meaningful examples, the relevant theory, and prominent solution algorithms. Every day consists of a 90-minute lecture part in the morning and a 90-minute hands-on exercise session in the afternoon.

REGISTRATION REQUIRED !
Please register via the office of the graduate school: hgs@iwr.uni-heidelberg.de

For further information please visit the website:
https://scoop.iwr.uni-heidelberg.de/teaching/2022ws/short-course-optimization/

Target audience: BSc students in Mathematics and Computer Science

This event is a double seminar between MATHEMATIKON, Heidelberg and AIMS, South Africa on the occasion of the International Day of Mathematics. In two short and interactive lectures we celebrate the day of mathematics and bring together students from the two institutions on a virtual platform.

The event is organized by "heiAIMS, the Heidelberg - Cape Town Network for Applied Mathematics and Scientific Computing" which is funded by the Baden-Württemberg-STIPENDIUM for University Students, a program established by the Baden-Württemberg Stiftung.

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Title: Mathematics and African Arts
Speaker: Dr. Mafoya Landry Dassoundo • Department of Mathematical Sciences, African Institute for Mathematical Sciences (AIMS)

Abstract:
First of all, we will define, and recall basic the properties of braids groups and connect them naturally with knot theory. Some of its applications in the hairstyles of African women will be presented. Secondly, we will share a cultural experience around one of the oldest African games well known as "African stones game".

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Title: What Exactly is Infinity?
Speaker: Dr. Michael Winckler • HGS MathComp

Abstract:
The concept of infinity is hard to understand for humans. Counting finite sets is a natural concept, but understand what countably infinite sets are and that we can even investigate uncountably infinite sets is difficult to comprehend.

In this lecture we will start by comparing finite sets and establishing a rigorous method to compare sets in their size. Expanding this to infinite sets will help us to move forward and show that different levels of infinity exist. We will conclude the lecture by trying to visualize our results in drawing space-filling curves, thus experiencing that our notion of size does not easily extend to infinity.

Remark: Please bring paper and a pencil to class

This will be a short presentation about the (Julius) Springer publishing house and contemporary academic publishing. We will begin by briefly touching upon Springer’s historical connections to the University of Heidelberg before providing some insights into the modern publishing process. In particular, we will offer hints as to how to successfully publish journal articles and books of various types, and take questions from the audience. This should be especially useful for graduate students and early-career researchers.

Following the lecture, there will be a get-together. It will take place in the adjoining Common Room at 17:00. Snacks and beverages will be provided.

This event is jointly organized by HGS MathComp and the Faculty of Mathematics and Computer Science.

In this talk we discuss randomization techniques for solving large scale linear algebra problems. We focus in particular on solving linear systems of equations and eigenvalue problems. We first introduce a randomized Gram-Schmidt process for orthogonalizing a set of vectors and its block version. We discuss its efficiency and its numerical stability while also using mixed precision. Further randomized GMRES and randomized FOM methods are discussed for solving linear systems of equations as well as randomized Rayleigh-Ritz procedure for solving eigenvalue problems.

The IWR Colloquium will be streamed via Zoom. For more information please visit the website of the colloquium.

Link: www.iwr.uni-heidelberg.de/events/iwr-colloquium

HGS MathComp Members will receive 1 ECTS credit for every 4 talks attended. Please make sure to include them in your BlueSheet.

Having access to a powerful generative model allows for a wide range of downstream applications, such as probabilistic inference, sampling, data completion, density evaluation, outlier detection, etc. Diffusion models (DMs) are an emerging class of deep generative models that have demonstrated remarkable synthesis quality. DMs rely on a diffusion process that gradually perturbs the data towards white noise, while the generative model learns to denoise. A major drawback of DMs, compared to, for example, Generative Adversarial Networks, is that sampling can be relatively slow.

In this seminar, I will give an accessible introduction to DMs and present our work on critically-damped Langevin DMs (CLD) which is based on ideas from statistical mechanics. CLD can be interpreted as running a joint diffusion in an extended space, where the auxiliary variables can be considered "velocities" that are coupled to the data variables as in Hamiltonian dynamics. CLD significantly accelerates sampling compared to the original DM formulation, however, many further improvements can be made by borrowing ideas from the ODE solver literature.

The IWR Colloquium will be held as an in-person event at the Mathematikon. In addition it will be streamed via Zoom.
For more information please visit the website of the colloquium.

Link: www.iwr.uni-heidelberg.de/events/iwr-colloquium

HGS MathComp Members will receive 1 ECTS credit for every 4 talks attended. Please make sure to include them in your BlueSheet.

Our brains are comprised of billions of neurons that form a complex network. This network is a result of both evolutionary optimization (fostering a modular arrangement including highly specialized areas) and our own experience (storing memories and skills by adapting connection strengths) and determines how we process sensory input to produce meaningful responses. Since neurons communicate with short electrical pulses only when necessary, they are extremely energy efficient. Given our worldwide increase in computing demand, there is thus a strong incentive to develop low-energy neuromorphic computing paradigms that mimic the working principles of the brain. But what are the relevant working principles of the brain? How does a neural network develop useful dynamics? In this seminar, I will present minimal principles to ensure stable collective neural dynamics from a statistical physics perspective, discuss how these can be used to tune network states to task requirements and show how they can be applied to neuromorphic computing. While I mainly focus on experience-driven self-organization, I will finish with some ideas to include evolutionary-driven architectures in the future.

The IWR Colloquium will be held as an in-person event at the Mathematikon. In addition it will be streamed via Zoom.
For more information please visit the website of the colloquium.

Link: www.iwr.uni-heidelberg.de/events/iwr-colloquium

“Mathematics of Life” is a special interest group organized by doctoral students of the HGS MathComp.

HGS MathComp Members will receive 1 ECTS credit for every 4 talks attended. Please make sure to include them in your BlueSheet.

The MBO scheme is an efficient algorithm for data clustering, the task of partitioning a given dataset into several meaningful clusters. In this talk, I will present the first rigorous analysis of this scheme in the large-data limit.
The starting point for the first part of the talk is that each iteration of the MBO scheme corresponds to one step of implicit gradient descent for the thresholding energy on the similarity graph of the dataset. It is then natural to think that outcomes of the MBO scheme are (local) minimizers of this energy. We prove that the algorithm is consistent, in the sense that these (local) minimizers converge to (local) minimizers of a suitably weighted optimal partition problem.
To study the dynamics of the scheme, we use the theory of viscosity solutions. The main ingredients are (i) a new abstract convergence result based on quantitative estimates for heat operators and (ii) the derivation of these estimates in the setting of random geometric graphs.
To implement the scheme in practice, two important parameters are the number of eigenvalues for computing the heat operator and the step size of the scheme. Our results give a theoretical justification for the choice of these parameters in relation to sample size and interaction width.
This is joint work with Jona Lelmi (University of Bonn).

The lecture will be also streamed online: https://us02web.zoom.us/j/4889309058

Minicourse: Introduction to Mathematics of Deep Learning
Leonid Berlyand • Pennsylvania State Universtiy, USA
October 26-27, 2022 • 14:30 - 16:30

Abstract:
The goal of this minicourse of four lectures is to introduce basic concepts from deep learning in a rigorous mathematical fashion, e.g introduce mathematical definitions of deep neural networks (DNNs), loss functions, the backpropagation algorithm, etc. We attempt to identify for each concept the simplest setting that minimizes technicalities but still contains the key mathematics. This minicourse follows the upcoming book “Mathematics of Deep Learning: an introduction” by L. Berlyand and P.-E. Jabin. Publisher: De Gruyter (to appear).

Lecture 1. History, general perspective and basic notions of deep learning

In this lecture, we briefly discuss the general perspective of machine learning:
what is it and why study it? Next, we introduce the classification problem in a supervised learning context and then introduce the key concept of artificial neural networks (ANNs) as the composition of linear maps and nonlinear activation function followed by other basic definitions describing ANNs.

Lecture 2. DNNs and approximation theory

In this lecture, we discuss the universal approximation theorem describing the wide class of continuous functions which DNNs can be used to approximate. This theorem explains the extensive use of DNNs in classification problems. Next, we introduce the concept of training via the gradient descent algorithm which improves the approximate classifier by iteratively __learning__ from the dataset.

Lecture 3. Backpropagation & CNNs

We begin from introducing the notion of computational complexity. Next, we introduce the backpropagation algorithm which significantly reduces the computational cost of optimizing the loss function. This is done in the simplest one neuron per layer setting, which while not practical allows us to explain the concept without many technicalities. Finally, if time permits, we briefly discuss convolutional neural networks and their properties.

Lecture 4. Implementing DNNs and Training: a brief overview of Pytorch

This lecture will be presented by my co-author P.-E. Jabin (Penn State).

We present here a short and very basic introduction to Pytorch, in the context of classification of images. We assume passing familiarity with coding and with python in particular. Many further tutorials exist online and several can be found at https://pytorch.org/tutorials/
For more see the abstract_file:

From the vision “Stability, Prosperity and Sustainability,” Thailand has adopted “Thailand 4.0” as a policy vision for economic development which aims at driving the country out of a middle-income towards a high-income economy. Underlying this is no other than an attempt to change from the current economic model towards an “innovation-driven economy”—i.e., transitioning from an industry-driven development to a technology-, creativity-, and innovation-driven development by consolidating as well as integrating various fields of knowledge. This, thus, necessitates deepening mathematical research on optimization and numerical methods as it plays a considerable role in providing solutions for a variety of problems including those in such areas as industry, finance, engineering, economics and agriculture.

Realizing the importance of research on optimization and numerical methods, the Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi—in cooperation with the Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, the Federal Republic of Germany, and along with the nationwide as well as the Southeast Asian regionwide network of universities including Walailak University, Prince of Songkla University (Pattani Campus), Chulalongkorn University, Silpakorn University, Mahidol University, Chiang Mai University, and King Mongkut’s University of Technology North Bangkok—will organize an international “Workshop on Computational Science 2022: Webinar on Optimization and Numerical Methods in Industries.”

This year, the Department will co-host the event with the Department of Mathematics, School of Science, Walailak University, and the Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University.

Scientific progress in machine learning is driven by empirical studies that evaluate the relative quality of models. The goal of such an evaluation is to compare machine learning methods themselves, not to reproduce single test-set evaluations of particular optimized instances of trained models. The practice of reporting performance scores of single best models is particularly inadequate for deep learning because of a strong dependence of their performance on various sources of randomness. Such an evaluation practice raises methodological questions of whether a model predicts what it purports to predict (validity), whether a models performance is consistent across replications of the training process (reliability), and whether a performance difference between two models is due to chance (significance). The goal of this tutorial is to provide answers to these questions by concrete statistical tests.

The tutorial is hands-on and accompanied by a textbook (Riezler & Hagmann: Validity, Reliability, and Significance: Empirical Methods for NLP and Data Science Synthesis Lectures on Human Language Technologies, Morgan & Claypool Publishers, 2022) and a webpage including R and Python code: https://www.cl.uni-heidelberg.de/statnlpgroup/empirical_methods/

! REGISTRATION REQUIRED !
Please register via the office of the graduate school: hgs@iwr.uni-heidelberg.de

In this talk I will present a geometric multilevel optimization approach choosing as case study a regularised inverse problem. In particular, the approach is motivated by variational models that arise as the discretization of some underlying infinite dimensional problem. Such problems naturally lead to a hierarchy of discretized models. We employ multilevel optimization to take advantage of this hierarchy: while working at the fine level we compute the search direction based on a coarse model. By utilising concepts of information geometry in our formulation, we propose a smoothing operator that only uses first-order information and incorporates constraints smoothly. We show that the proposed algorithm is well suited for ill-posed reconstruction problems and demonstrate its efficiency on several large-scale examples.

The IWR Colloquium will be held as an in-person event at the Mathematikon. In addition it will be streamed via Zoom.
For more information please visit the website of the colloquium.

Link: www.iwr.uni-heidelberg.de/events/iwr-colloquium

HGS MathComp Members will receive 1 ECTS credit for every 4 talks attended. Please make sure to include them in your BlueSheet.

In statistics, stochastic processes are the main tool for modelling spatial and temporal data, and the most commonly used type are the Gaussian processes. A popular approach to define Gaussian processes is through linear stochastic differential equations driven by white noise. This methodology is often denoted the SPDE approach. By utilising methods from numerical analysis one has been able solve several of the computational bottlenecks that have hampered the usage of Gaussian processes for real world data sets, in particular for spatial statistics. I will focus on how one can apply the same methodology to create non-Gaussian processes, in space and time. I will also discuss what practical properties these processes have over their Gaussian counterpart. Further, I will present very recent work where we have extended the SPDE approach for Gaussian process, to Euclidean graphs (like street networks). Here the linear differential operators are obtained using Quantum graphs, which opens these methods for a new wide range of applications.

The IWR Colloquium will be held as an in-person event at the Mathematikon. In addition it will be streamed via Zoom.
For more information please visit the website of the colloquium.

Link: www.iwr.uni-heidelberg.de/events/iwr-colloquium

HGS MathComp Members will receive 1 ECTS credit for every 4 talks attended. Please make sure to include them in your BlueSheet.

The graduate school HGS MathComp will organize an Integrative Think Tank (ITT) from July 4-8, 2022 in Heidelberg.

What is an "Integrative Think Tank" (ITT)?
ITT is a 1-week-challenge workshop with two industrial partners. Students and lecturers work together with experts from the companies to investigate R&D topics related to current developments at these companies.

What is the goal of the event?
The goal of the event is to formulate actual research topics as thesis projects outlines (master or PhD). After the event the University and the partner companies join forces to convert the project outlines into actual research projects.

Who can participate?
The event is open to all master and PhD students at Heidelberg University. ITT is most successful if different students from various fields work together to analyze the research questions of our partners.

What is the reward?

• All participants get a certificate of attendence that certifies 2 ECTS as key competence training.
• Attending students can also apply to participate in the projects defined at ITT as their master or PhD project.
• On top you get direct contacts to the participating companies for internships and practicals (or later employment ...).

OK, you got me! How do I register?
Scroll to the end of the page and find all details regarding registration. Get some more information along the way! And: Tell your friends about it! The more, the merrier!

For more information please visit the website:

www.mathcomp.uni-heidelberg.de/itt-2022

Predicting the emergent properties of a material from a microscopic description is a scientific challenge. Machine learning and reverse-engineering have opened new paradigms in the understanding and design of materials. However, the soft-matter field has lagged far behind in embracing this approach for materials design. The main difficulty stems from the importance of entropy, the ubiquity of multi-scale and many-body interactions, and the prevalence of non-equilibrium and active matter systems. The abundance of exotic soft-matter phases with (partial) orientation and positional order like liquid crystals, quasicrystals, plastic crystals, along with the omnipresent thermal noise, makes the classification of these states of matter using ML tools highly non-trivial. In this talk, I will address questions like: Can we use machine learning to autonomously identify local structures, detect phase transitions, classify phases and find the corresponding order parameters in soft-matter systems, can we identify the kinetic pathways for phase transformations, and can we use machine learning to coarse-grain our models? Finally, I will show how one can use machine learning to reverse-engineer the particle interactions to stabilize nature’s impossible phase of matter, namely quasicrystals?

The IWR Colloquium will be held as an in-person event at the Mathematikon. In addition it will be streamed via Zoom.
For more information please visit the website of the colloquium.

Link: www.iwr.uni-heidelberg.de/events/iwr-colloquium

HGS MathComp Members will receive 1 ECTS credit for every 4 talks attended. Please make sure to include them in your BlueSheet.

Deep learning-based approaches have revolutionized virtually all domains of computer vision, including the field of microscopy image analysis. In image reconstruction and classification, in segmentation and artificial labelling, they have pushed both flagship projects and bread-and-butter everyday tasks, allowing image analysis to keep pace with the recent advancements in imaging technology and instrumentation. I will talk about the recent work of my group that has enabled the first segmentation of cells in a whole animal imaged with electron microscopy, our collaboration with microscope developers for trusted reconstruction of light field microscopy and our current efforts to reduce the annotation budget for training of segmentation algorithms. I will also show how we strive to make our methods accessible to biologists without computational expertise through our software ilastik and through the emerging community network collection at the BioImage Model Zoo.

The IWR Colloquium will be held as an in-person event at the Mathematikon. In addition it will be streamed via Zoom.
For more information please visit the website of the colloquium.

Link: www.iwr.uni-heidelberg.de/events/iwr-colloquium

HGS MathComp Members will receive 1 ECTS credit for every 4 talks attended. Please make sure to include them in your BlueSheet.

Datum:
10.05.2022 und 12.05.2022 (14:00-16:30 Uhr)

Beschreibung:
Vielfalt und Pluralität sind mittlerweile Alltag in unserer Gesellschaft, im Privat- und Berufsleben: Menschen mit unterschiedlichsten Lebensentwürfen, Hintergründen, Glauben oder Herkunft treffen aufeinander.

In dem zweiteiligen Workshop werden wir uns mit verschiedenen theoretischen Ansätzen im Themenfeld Diskriminierung beschäftigen und anhand praktischer Übungen einen eigenen erfahrungsbasierten Zugang dazu finden. Ziel ist es, Selbstverständlichkeiten und gängige Narrative diskriminierungskritisch zu hinterfragen sowie Handlungsoptionen aufzuzeigen, die tradierte Verhaltensmuster aufbrechen können. Ein besonderes Augenmerk soll dabei auf die Geschlechtergleichberechtigung gelegt werden.

Dozentinnen:
Halszka ?liwa-Ohnesorge verantwortet die Bildungsstelle Plurales Heidelberg bei Mosaik Deutschland e.V. Sie studierte Religionswissenschaft und Politikwissenschaft Südasiens an der Universität Heidelberg. Nachdem Studium arbeitete sie u.a. für ein ziviles, indisch-pakistanisches Friedensprojekt, bei dem die gleichberechtigte Förderung von Jungen und Mädchen ein zentrales Element war. Heute liegt der Schwerpunkt ihrer Arbeit auf der vorurteilsbewussten und diskriminierungskritischen politischen Bildung.

Lara Track leitet das Antidiskriminierungsbüro Heidelberg (ADB) bei Mosaik Deutschland e. V. Sie berät Menschen, die Diskriminierung erfahren haben und setzt sich in Netzwerken auf kommunaler, Landes- und Bundesebene gegen Diskriminierung ein. Im Rahmen ihrer wissenschaftlichen Mitarbeit am Historischen Seminar der Universität Heidelberg erforscht sie Verbindungen zwischen Frauenfriedensaktivismus und Feminismus in den USA. In der Auseinandersetzung mit US-Feminismen gründet ihr Interesse am Konzept der Intersektionalität, das in der mehrdimensionalen Beratung im ADB zum Tragen kommt.

Zielpublikum:
Mitarbeiterinnen und Mitarbeiter in der Verwaltung, PostDocs, Junior Forschungsruppenleiterinnen und -leiter

! ANMELDUNG ERFORDERLICH !
Anmeldungen für den Workshop bitte an: hgs@iwr.uni-heidelberg.de
[Anmeldeschluss: 18.04.2022]

Netzwerken ist ein wesentlicher Bestandteil bei der Entwicklung einer wissenschaftlichen Karriere. Die Analyse, der Aufbau und die Pflege von (wissenschaftlichen) Netzwerken unterstützen auf dem Weg zur Professur aber auch bei außeruniversitären Karrierezielen. Dabei gilt es die eigene komplexe Forschungstätigkeit in sich bietenden Situationen innerhalb (z.B. wissenschaftliche Gemeinschaft, Konferenz) und außerhalb (z.B.: Fördereinrichtungen, Wirtschaft) der Wissenschaft überzeugend und verständlich darzustellen.

In diesem Workshop geht es darum, sich auf Netzwerksituationen wie bspw. den Besuch einer wissenschaftlichen Konferenz vor- und nachzubereiten. Dazu werden verschiedene Situationen vorgestellt und in Übungen und Rollenspielen mit Videoaufzeichnungen (falls gewünscht) ausprobiert und im Detail besprochen.

Des Weiteren wird das eigene Netzwerk analysiert, um Potentiale für dessen Weiterentwicklung zu erkennen und diese bei entsprechenden Netzwerkgelegenheiten zu nutzen. Überdies werden die besten Tricks und Kniffe für erfolgreiches Smalltalken vermittelt, um die eigene Kommunikation in Alltag und Beruf zu optimieren.

Themenauswahl:
• Netzwerkpotenziale: Was ist Netzwerken und warum brauche ich es?
• Netzwerkanalyse im Kontext Wissenschaft – die Rolle von Mentoren
• Analyse, Aufbau und Pflege von (wissenschaftlichen) Netzwerken – wie sieht mein eigenes Netzwerk aus?
• Methodentools: Elevator Pitch zur (überzeugenden und verständlichen) Darstellung von komplexen Forschungstätigkeiten
• Methodentool: „Conference Dinner/Conference Break“
• Methodentool: „Erfolgreiche Selbst-Präsentation“: Story Telling und Grundregeln für erfolgreiches Smalltalken

Zielpublikum:
Dieser Workshop ist Teil des Upstream Programms und nur für weibliche Mitglieder zugänglich.

! ANMELDUNG ERFORDERLICH !
Anmeldungen für den Workshop bitte an: upstream@iwr.uni-heidelberg.de

Der Datenbank- und Softwareriese SAS öffnet seine Türen für Mathematikerinnen und Frauen* aus den STEM-Feldern! Euch erwarten Vorträge und Hands-On-Erfahrungen mit den SAS-Toolsets sowie die Möglichkeit zur Vernetzung mit dem Women´s Initiative Network von SAS.

Zielpublikum:
Diese Exkursion ist Teil des Upstream Programms und nur für weibliche Mitglieder zugänglich.

! ANMELDUNG ERFORDERLICH !
Anmeldungen für den Workshop bitte an: upstream@iwr.uni-heidelberg.de

Topic:
Natural or engineered materials often contain two or more key constituents, arranged in a heterogeneous structure varying at different scales. Such materials are desirable because their macroscopic properties can be superior to the properties of the individual constituents. It is even possible to explicitly design them for a particular purpose by changing the composition of the constituents.

An example are carbon fibre composites for lightweight structures and vehicles. The mathematical modelling of such heterogeneous or composite materials typically leads to PDEs with highly oscillating coefficients. Direct numerical solution of such problems with traditional methods, such as finite elements is computationally expensive. Just to compute the correct qualitative behaviour, the mesh resolution would need to be sufficiently high to capture all the fine scale variation.

In this seminar, we will study multiscale numerical methods that address various aspects of this challenge. This includes the generation of low-dimensional yet high-quality approximation spaces, acceleration of fine-scale solutions and efficient uncertainty quantification for multiscale problems.

Each student will present a key publication on multiscale numerical methods with an aim to cover most aspects of the field.

Prerequisites:
Basic knowledge of partial differential equations, Sobolev spaces and Finite Element methods is required. For the uncertainty quantification topics, basics of probability theory are needed.

Registration and Schedule:
• First meeting: April 22, 2022, 13:00. Room: TBA
• Since we use MÜSLI for email communication, please register at: https://muesli.mathi.uni-heidelberg.de/lecture/view/1517
• Schedule: Will be chosen on first meeting to maximize attendance

Contact:
Main contact - Linus Seelinger: linus.seelinger at iwr.uni-heidelberg.de
Secondary contact - Robert Scheichl: r.scheichl at uni-heidelberg.de

Talks:
The length of each talk is 40 min. + 10 min. for questions and discussion.
Before your talk you should meet with one of us to discuss your presentation.

Have you sat in front of an empty sheet of paper or an empty file before, trying to start a report, a paper or your PhD Thesis, but struggling to make progress? Or have you had to read a research article relevant to your PhD project that was so incomprehensible or dry that you were wishing to be somewhere else for the day to end? Good scientific writing is an absolutely essential part of our work as researchers; only through good communication will we be able to captivate the audience and truly enthuse them for our achievements and results. It is as important as pure academic ability for a successful professional career in academia, but also in industry, but typically we are not trained formally in good writing in a mathematics or science degree. In this crash course I aim to give you some useful pointers on good writing in the mathematical sciences, and through some hands-on exercises we will try to immediately put them in practice.

! REGISTRATION REQUIRED !
Please register via the office of the graduate school: hgs@iwr.uni-heidelberg.de

Prof. Morteza Fotouhi • Sharif University of Technology

Dates:
April 20, 2022 / 09:00-12:00
April 21, 2022 / 14:00-17:00

The term moving interfaces and free boundary refers to a class of problems in which the domain of the problem itself is also an a priori unknown, as well as the basic unknown solution to governing equations. Hence, finding the domain is part of the problem.
In this lecture I shall present some basic models in moving interfaces and free boundary problems. These includes obstacle problem, Stefan problem, Hele-Shaw flow and Muskat problem.
I will also review the state of art for obstacle problem. The lectures will be at elementary level for both advanced master and Ph.D. students.

! REGISTRATION REQUIRED !
Please register via the office of the graduate school: hgs@iwr.uni-heidelberg.de

! REGISTRATION REQUIRED ! Please register via hgs@iwr.uni-heidelberg.de (Deadline: March 22, 2022)

The workshop will held as an in-person event. All regulations regarding the current Coronavirus Ordinance of Heidelberg University apply. If the situation changes the workshop will be held as a virtual event.

March 29-30, 2022 • 09:00
May 2, 2022 • 14:00-18:00

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Working as a PhD student you have the challenging task of developing research findings and write you doctoral thesis within three years. This alone is a demanding job. In addition, it is vital to the scientific process that your findings are presented to the scientific community. For most PhD students this is the first big project in their professional life and it could have a crucial impact on their future professional career. PhD students are highly motivated when they start their PhD studies but may underestimate the need for professional management for this three-year project "doctoral thesis".

This seminar demonstrates how to approach the doctoral thesis in a professional way. Project management tools and techniques are used, tailored to the specific situation of PhD students. You will learn how to set a project vision, define clear objectives, gain buy-in from your supervisor and other colleagues in your group, and how to develop a project plan, which is structured and at the same time flexible enough to easily adjust to unexpected findings. You will establish a "controlling cycle" which helps you to recognise risks and problems as early as possible, and you will learn how to manage critical situations and deal with ups and downs. Furthermore, networking with colleagues, supervisors and other people are important topics of this seminar.

Throughout the seminar, you will work on your own doctoral thesis and share your experience with others. This seminar is most beneficial for PhD students who are in the early phases of their doctoral thesis. At the end of the seminar you will have established a strategy on how to approach your own doctoral thesis. During the follow-up REVIEW we will share experience and best practices and deal with open questions from the first module.

This seminar will help you to make the most effective use of your three years and finish your doctoral thesis on time.
You will also learn and practise the basic concepts of project management – a prerequisite in industries and research institutions.

Uncertainty quantification plays an important role in many engineering applications. In this talk we discuss some recent advancements in the approximation of high-dimensional functions appearing in this context. We provide new results for surrogate modeling and parameter estimation in cases where the uncertainty is described by a Gaussian random field. Additionally we address how the usage of neural networks to learn linear and nonlinear operators may help in solving such tasks, and what possible challenges might lie ahead.

The IWR Colloquium will be held as an in-person event at the Mathematikon. In addition it will be streamed via Zoom.
If your are planing to attend in person you have to follow the 3G-rule set (vaccinated / recovered / tested).
For more information please visit the website of the colloquium.

Link: www.iwr.uni-heidelberg.de/events/iwr-colloquium

We all have different networks - no matter whether we call them "network" or not. These networks provide support, advice and help in different fields. To better use and understand them it is crucial to be aware of the fact that the purpose influences the network itself. If our purpose (or aim or goal) remains unclear efficient networking is impossible. The workshop "Purposeful Networking" provides basic information on networking and gives space to clarify personal aims - to create a link between network and purpose.

Registration: hgs@iwr.uni-heidelberg.de
Registration Deadline: January 24, 2022