HGS MathComp Upcoming Events

Aim:
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

Contents & 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.

1. Self-study via an online platform (6-8 hours of engaging video content & a useful assignment):
1.1. Communicating with scientific vs non-scientific audiences
1.2. Visual perception and what humans find intuitive
1.3. Layout: simplifying comprehension through structured layout
1.4. Eye-flow: effortlessly guide the audience through the design
1.5. Colors: how to amplify, not ‘fancify’
1.6. Typography for legibility, structure and aesthetics
1.7. Digital images in science: the optimal use of vector and raster images
1.8. Slides that amplify messages and don't distract when presenting
1.9. Posters: strategy and process for creating posters that attract and explain
1.10. Homework: participants submit images and slides to the trainer to receive feedback
2. Live Online Workshop (April 10, 2025, 9 am – 1 pm via Zoom, interactive and hands-on)
2.1. Recap of fundamentals and Q&A: trainer facilitates an effective recap of lessons learned in self-study module and answers all further questions.
2.2. Exercises & group work: participants draw a graphical abstract of their research and share their posters and we form groups so everyone gives and receives informed feedback.

Discussion on pre-submitted materials: participants receive actionable suggestions on how to improve their own images and slides from the trainer and on posters from fellow researchers.

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.

Requirements: No previous programming knowledge is required! We will use our own server platform for the course, therefore no additional installation of software is needed.

Objectives:
How can I make an impact in professional contexts such as small-talk situations at conferences? What can help me in situations where I face an international crowd and how can I connect to an interdisciplinary audience? The workshop Make an Impact! offers the opportunity to enhance overall effective communication (verbal and non-verbal), as well as interpersonal settings, in order to improve networking and self-promotion opportunities.

Description:
Throughout the workshop, participants will be guided through interactive exercises to improve their communication, as well as focus on the quality of their language content and physical expression. The aim is to develop strategies to make a lasting and positive impact on groups, colleagues, and significant contact persons (networking). 
Trainer input will provide the opportunity to gain new insights in effective communication and learn how to present themselves more effectively and to make the best impression and strongest impact.

Contents:
• Self-marketing: effectively promoting oneself (verbal business cards)
• Spontaneous small talk: informal chatting with a purpose
• Developing awareness skills
• Concise and effective introductions: make an impact!
• Body language focus

Methods:
• Theoretical sessions to highlight key aspects and strategies
• Role-play scenarios
• Hands-on exercises for practicing
• Both group and individual feedback

This course introduces Ito processes, a fundamental class of continuous-time stochastic processes widely used in quantitative finance to model stock price dynamics. The course will cover two key methodologies for pricing financial derivatives:
1. Monte Carlo simulations, a probabilistic approach to estimating derivative prices.
2. The partial differential equation (PDE) approach, including applications of the Feynman-Kac theorem in pricing futures and options.
The course will focus on the Black-Scholes model and the Heston stochastic volatility model, which are essential for understanding modern financial markets.

Course Topics
• Introduction to Ito processes and their applications
• Stochastic differential equations (SDEs)
• Numerical methods for solving SDEs
• Monte Carlo (MC) simulations for financial modeling
• The Black-Scholes and Heston stochastic volatility models
• Computing futures and option prices using MC simulations
• Pricing derivatives using the PDE approach and the Feynman-Kac theorem
• Comparison of Monte Carlo simulations and the PDE approach
This course provides both theoretical foundations and practical implementations, making it ideal for students interested in stochastic processes, numerical methods, and financial mathematics.

Welcome to the "Between Models and Reality" Ph.D. school on Machine Learning in physics. The focus of this school is the application of Machine Learning to physics research and the many challenges and solutions that this entails. The focus is mainly on application, and will not only consist of lectures.

So if you are a Ph.D. student in a field of physics (or a related "large research data" science), who would like to learn more about both basic but also more advanced Machine Learning approaches and ways of thinking, don't hesitate to apply.

Lectures will be run by a diverse group of physicists and computer scientists, led by the world-class researchers Tilman Plehn (Univerity of Heidelberg) and Thea Aarrestad (ETH Zurich), on topics such as:

- Bayesian neural networks
- Uncertainty quantification & learning
- Generative machine learning
- Representation learning
- Fast machine learning
- Physics-informed neural networks

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.

In particular, the course will cover the following topics:

Requirements on research data handling from universities, research funders and scientific journals
Short-term and long-term preservation: formats, metadata, documentation, standards
Open Research data, data publication and data citations: Where? How? Why and why not?
Creation of data management plans for research projects

Support for researchers at Heidelberg University: the Competence Centre for Research Data (http://www.data.uni-heidelberg.de/index.en.html)

Machine Learning is transforming science, especially the way we do research in medicine. It can analyze non-linear dependencies of structured clinical data, and it is starting to support in the huge amount of existing text and other unstructured information to extract useful information using recent techniques based on large language models. There is also an increasing amount of specific omics data for each patient, which makes it hard to manually inspect all the details. This is where multimodal data analysis comes in, which is the focus of this year's AI in Medicine workshop. Researchers from Sorbonne and Heidelberg will give keynote speeches to provide insight into their research field, which will fuel discussions.

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
- Introduction to polyspherical coordinates
- Mode combination and multilayer trees
- Vibronic Hamiltonians for photophysics and photochemistry
- Scattering and molecular dissociation
- High-dimensional problems
- Hands-on exercises with the Quantics / Heidelberg MCTDH package, including
- Spectroscopy & quantum control
- Reactive scattering
- Direct dynamics / GWP
- Practical use of the TANA program for deriving analytical kinetic operators
- Potential energy operators in sum-of-products form

Machine Learning is here to stay and is shaping the future of fundamental physics research. From optimal inference, over theory-inspired network architectures, to anomaly detection, representation learning and foundation models, a new generation of scientists is driving these exciting developments. This school aims to further strengthen technical expertise and foster new connections.

Central themes of the school are:

- Modern network architectures
- Precision and uncertainties
- Scientific foundation models
- Generative Networks
- Representation learning
- Optimal inference
- Quantum field theory and networks

2025 lecturers:

- Thea Aarrestad (ETH Zurich) -- Particle experiments, anomalies
- Jim Halverson (Northeastern University) -- Particle theory, KANs
- Michael Kagan (SLAC) -- Representation learning, foundation models
- Siddharth Mishra-Sharma (Anthropic, Boston University) -- Cosmological analyses
- Veronica Sanz (University of Valencia) -- ML for data mining
- David Shih (Rutgers University) -- Linking particle and astrophysics
- Ramon Winterhalder (University of Milan) -- Generative Networks

For more see the abstract_file: