Heidelberg Graduate School HGS MathComp

HGS MathComp Curriculum


Workshops & Schools
info  Workshop on analyzing stresses in moleculesVarious Speakers Nov 4-5, 2019 ECTS-Points: not yet determined
Abstract, registration & information:
This workshop aims at discussing and devising new and unifying concepts to calculate and understand molecular stresses in complex materials. Such molecular stresses can stem from quantum chemical calculations, atomistic or coarse-grained Molecular Dynamics simulations. They could be inherent to the system, i.e. reflect intrinsic tension or pre-stress, or build up upon applying external mechanical perturbations.

In an informal workshop we will bring together experts from the relevant scientific areas, computational physics, materials science, and biological matter, and will leave room for a few contributed talks from participants as well as many discussions among speakers and participants. There is the possibility to participate in a small practical workshop on force distribution analysis (FDA).

The workshop is completely free of charge.
Link for more information
Institute for Theoretical Studies Heidelberg
not yet determined
info  Short Course "Goal-oriented adaptivity for PDEs with random data"Prof. David Silvester November 4-7, 2019 ECTS-Points: 2
Abstract, registration & information:
Please register here

Day 1 (Motivation) 09:00 - 12:00
I. Review of FEM error estimation and adaptivity for elliptic PDEs
II. Adaptive timestepping for parabolic PDES

Day 2 (Spatial adaptivity) 09:00 - 12:00
I. Error reduction estimates; marking strategies; proof of convergence
II. Goal-oriented adaptivity; dual problems; numerical experiments

Day 3 (Parametric enhancement) 09:00 - 12:00
I. Stochastic Galerkin approximation; solver ingredients
II. Combining spatial and parametric adaptivity; numerical experiments

Day 4 (Extensions) 09:00 - 10:00
I. Solutions to exercises; open issues; lessons learned

Tutorial Classes
Students will need to have access to a computer or laptop
with MATLAB or Octave installed. The exercises will be based on
the T-IFISS software package which can be downloaded from
Link for more information
Mathematikon, Seminar Room 12 (5th Floor), Im Neuenheimer Feld 205, 69120 Heidelberg
info  Agile management approaches for research groupsManuela Schmidt December 4, 2019, 9:00-15:15 ECTS-Points: 1
Abstract, registration & information:
Scrum is an agile framework within which people can address complex adaptive problems, while productively and creatively delivering products. Kanban uses a visual system for managing work as it moves through a process, allowing team members to see the state of every piece of work at any time.

Both of them are agile approaches to project management. Agile project management is one answer to the growing speed in which projects need to be delivered and the realization that many projects are not delivered as originally planned. This is especially true for projects with volatile or unclear requirements at project start.

Concepts like continuous improvement, fast feedback cycles, limiting work in progress and transparency can bring value to teams as well as to individuals.

During this workshop you will learn:

- What is Agile? Get an overview of agile principles, values, techniques and methods
- How to use Kanban for your personal use? Boost your productivity and get things done!
- How can Kanban be introduced to a team? Streamline work between team members and create transparency on status
- How can Scrum be adapted in an academic environment? Use a process framework to improve collaboration and knowledge-sharing between lab members and cut down your fixed meeting times



- Agile introduction - origins, mindset & term definition
- Introduction to Scrum Framework
- Introduction to Kanban
- Applying theory into practice (Part 1)
- Personal Kanban - how to gain focus and transparency in your daily work


- Applying theory to practice (Part 2)
- Kanban for teams - lightweight way to introduce an agile method to a team
- LabScrum - a process framework to manage work in academic scientific research

Please register here
Mathematikon, Conference Room, 5th Floor, Room 5/104, Im Neuenheimer Feld 205, 69120 Heidelberg
info  Graphendatenbanken, GIS und 3D-Modelle in der Bauforschung des Mittelalters (GG3D19)Verschiedene Vortragende 5. bis 7. Dezember 2019 / 14:00 Uhr ECTS-Points: not yet determined
Abstract, registration & information:
- Interdisziplinäre Verbindung von Mediävistik und Informatik
- Computergestützte Analyse von Burgen, Urkunden und Landkarten
- Dokumentation basierend auf 3D-Modellen, QGIS und Neo4j
- Neue Methoden für die digitale Bauforschung
- Informationsgewinnung mit Personennetzwerken

Registrierung notwendig!
Link for more information
Mathematikon, Konferenzraum / 5. OG, Im Neuenheimer Feld 205, 69120 Heidelberg
not yet determined
info  Requirements | Social Network Analysis with Google Cloud PlatformMithun Srindharan and Dr. Keyvan Sadri (KPMG) January 10, 2020, 14:00 - 18:00 ECTS-Points: 1
Abstract, registration & information:
Industry’s demand for using big data analysis tools is growing evermore. Public clouds developed in recent years have provided various data analysis tools for analysts, data scientists, researchers, and academics without the burden of permanently allocating and continuously maintaining high performance computing facilities. Google Cloud Platform (GCP) is one of the public cloud providers with a variety of state-of-the-art products addressing wide range of challenges in data engineering and data analytics.
The internet age and growth of social networks was a game changer for human sciences. If previously researchers and analysts had to carefully design data collection processes, now people are sharing their thoughts and concerns on a daily (or hourly) basis. In this workshop we are going to introduce GCP pipelines from streaming Twitter API’s data into a data warehouse. Afterwards, we will perform sentiment analysis and network analysis to find the patterns in user’s behavior. Finally, using visualization tools in python, we will prepare effective communication of the results.

Target Group:

- Students majoring in economics,
information systems and natural sciences.

Hardware/ Software Requirements (Student):

- Laptop

- Anaconda

installation instructions:

- A google account

Please register here
Mathematikon, 2th Floor, room 2.414, Im Neuenheimer Feld 205, 69120 Heidelberg

Further Studies
Block Lectures
info  Designing your AI-based StartupProf. Dr. Carsten Rother tba ECTS-Points: 4
Abstract, registration & information:
The students
Understand the process of Business Model Innovation to bring an idea to a monetizable business level including a financial planning. To execute and implement business ideas through Business Development using methods like Value Proposition Canvas, Business Model Canvas, Strategic Innovation Canvas and through Business Analysis using methods like SWOT, PEST and Balanced Scorecard.
Understand techniques for business problem-solving in the areas of ideation, prototyping and testing. Ideation based on problem definition, following rapid prototyping using different tools like LEGO, 3D-Printing and Software Mock Ups, and to get feedback through testing like split tests and iterative customer interviews.
Understand how to present a business idea to motivate customers, supporters, multiplicators, partners and investors through a meaningful pitch deck and agile business plan. An understanding of the framework EXIST Idea Paper will help to apply for potential future funding.
Understand user-centric problem identification such as Design Thinking. Observation of customer needs through interviews and persona creation as well as point-of-view definitions will help to prioritize relevant problem fields.
Understand the basic principles of machine learning and computer vision, such as deep neural networks, necessary to launch a start-up as a business person.
Understand the high-level concepts of the different fields of machine learning, such as reinforcement learning, active learning, supervised and unsupervised learning.
Understand the state-of-the art of computer vision and machine learning, such as object recognition and motion estimation, in order to create ideas for a business model.
Understand the application and connection of machine learning and computer vision techniques to related fields such as hardware design, camera design, robotics, medicine and biology.

What is the way from identifying a potential market need, until planning and executing a business idea in the area of AI?
This course is split in four parts:

a) Technical part. Short introduction to machine learning. Discussing the different areas in AI, especially machine learning and computer vision, such as deep neural networks, reinforcement learning, active learning, and unsupervised learning. Presenting the state of the art in computer vision and machine learning, such as object recognition, motion estimation, and domain adaption. State of the art in hardware design especially camera design. Discussing the connection of Machine learning and computer vision with related fields such as biology, medicine and robotics.
b) Business part. This part will provide the development from problem to solution using Design Thinking bridging to Business Model Innovation where the idea is formed, streamlined and scaled into a monetizable business idea. We will cover elements like Elevator Pitch, Story Telling, Team Introduction, Business Model (Core Business including Value Proposition, Customer Segment, Customer Relationship and Channels combined with Key Partners, Key Activities and Key Recourses as well as Revenue Streams and Cost Structure), Competition, Market Entry and Closing with Call-to-Action will be processed during the course.
c) We will Invite AI-based start-ups to talk about their expertise
d) There will hands-on sessions and a final project where you should come up with your own AI-based startup.
The block course is in general characterized by high interactivity and workshop character
Teilnahmevoraussetzungen: none.
Prüfungsmodalitäten: There will be no marks. In order to pass the course, the students must attend the course and pass the practical project.

Useful literature:
- Blank, S. & Dorf, B.: The Startup Owner_s Manual: The Step-By-Step Guide for Building a Great Company, K&S Ranch.
- Lewrick, M. et. al.: The Design Thinking Playbook: Mindful Digital Transformation of Teams, Products, Services, Businesses and Ecosystems, Wiley.
- Osterwalder, A. et. Al.: Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers, Wiley.
- Gassmann, O. et. al.: The Business Model Navigator: 55 Models That Will Revolutionize Your Business, Financial Times.
- Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville
- Online Course: https://www.coursera.org/learn/machine-learning.
(Since the field of machine Learning and Computer Vision is moving so rapidly there are no books which cover the latest trends. Good (but older) books are:
- Pattern Recognition and Machine Learning, Christopher Bishop
- Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig
Link for more information