IWR Colloquium Winter Semester 2023 / 2024

"An Interdisciplinary Journey of Computational Mathematics in Theoretical Chemistry"

Prof. Benjamin Stamm • Institute of Applied Analysis and Numerical Simulation, University of Stuttgart
December 13, 2023 • 16:15

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.

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.

Location: Mathematikon • Conference Room, Room 5/104, 5th Floor • Im Neuenheimer Feld 205 • 69120 Heidelberg

"Frauenpower in Tech: Panel und Networking"

28. November 2023 • 18:00 - 21:00 Uhr

Ist die IT-Welt ein integraler Bestandteil deines Alltags? Hast du den Wunsch, die digitale Zukunft aktiv mitzugestalten und suchst nach Inspiration? Lerne beeindruckende Frauen kennen, die aus unterschiedlichsten Bereichen in die IT eingestiegen sind und dort spannende Projekte machen. Lass Dich durch ihre Geschichten, Herausforderungen und Erfahrungen inspirieren und dir helfen, den richtigen Weg für dich zu finden! Dafür gibt es nach dem Panel noch ein informelles Networking bei kleinen Snacks.

Moderation:

Prof. Dr. Barbara Paech, Software Engineering, Universität Heidelberg

SprecherInnen:

Quereinstieg

•  Kerstin Lambert, Digitalisierungsmanagerin, EPPLE GmbH
• Eda Özsahin, IT-Projektkeiterin
• Claire Zschiesche, Ausbildungsleitung Informatik, SAP SE (Panel)
• Sibylle Hofmeyer, PR-Beraterin, Hofmeyer PR

MINT-Studium, IT-Firma

• Dr. Hannah Abelein, Senior Manager, mm1 Consulting GmbH
• Franziska Bierer, Vorstand, andrena objects ag (Panel)
• Elisabeth Kohm, Leiterin Qualitätsmanagement und Qualitätssicherung, Datenschutzbeauftragte, HMS Analytical Software GmbH
• Kristin Sunnick, Softwareentwicklerin, andrena objects ag

Lehramt

• Lisa Galvagno, Studienrätin und Studierende im Erweiterungsfachmaster Informatik (Geographie, Politik, Wirtschaft, Informatik) (Panel)
• Dr. Elisabeth Kraus, Geschwister-Scholl-Gymnasium, Mannheim
• Prof. Dr. Claudia Hildebrandt, CZS Stiftungsprofessur für Informatik und ihre Didaktik, Pädagogische Hochschule Heidelberg, Heidelberg School of Education

Firmengründung

• Ines Korkowski, Gründerin und Geschäftsführerin KRAFTSTOFF Fashion GmbH (Panel)
• Dr. Monika Pobiruchin, Hochschule Heilbronn und Colorful Bytes UG

Wissenschaft

• Dr. Anne Hess, Expert Cross-Disciplinary Requirements Engineering, Fraunhofer Institut für experimentelles Software Engineering, Kaiserslautern (Panel)
• Prof. Dr. Timea Illes-Seifert, Intelligente Systeme, Wissensmanagemnet und Strukturwandel, Technische Hochschule Aschaffenburg
• Dr. Elisabeth Peinsipp-Byma, Abteilungsleiterin am Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung, Karlsruhe

Es laden ein: heiInnovation, Institut für Informatik, IWR, GI Fachgruppe Frauen und Informatik

Registriere Dich kostenlos und sei dabei!

Ort: Heidelberg Center for American Studies • Atrium • Hauptstrasse 120 • 69117 Heidelberg

"Machine learning galore!"

November 16, 2023 • 16:30 - 18:00

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

• 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

To help plan the catering, please register for free by clicking here. (Deadline: November 13, 2023)

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

Location: Mathematikon • Conference Room, Room 5/104, 5th Floor • Im Neuenheimer Feld 205 • 69120 Heidelberg

"Carl-Zeiss-Stiftung Summer School on Scientific Machine Learning"

August 14–18, 2023

• Interdisciplinary, highly interactive school on ML4Science with focus on astrophysics
• Collaborative research between AI developers and astrophysicists to kick-start new and fascinating science projects
• Well balanced mix of invited talks and plenty of free time for collaborative research, discussions and tutorials

Target Audience
Young researchers in the field of computer science, ML/AI, astrophysics or related

Science Keywords
Neural ODEs, invertible generative models, graph representation learning, information field theory

Invited Speakers
Nathan Kutz (U. of Washington, Seattle)
Torsten Enßlin (MPI for Astrophysics, Munich)
Patrick Kidger (Google X)
Laurence Perreault Levasseur (U. of Montreal)
Kaze Wong (Flatiron Institute, NY)
Andreea Deac (Mila, Montreal)
Christina Winkler (TU Munich)
Christopher Rackauckas (MIT)

Scientific Organizing Committee
Tobias Buck (IWR, Heidelberg)
Soledad Villar (Johns Hopkins U., Baltimore)
Aura Obreja (University Observatory Munich)
Morgan Fouesneau (MPI for Astronomy Heidelberg)

! APPLY ONLINE!
For more information please visit the website of the event: https://astroai-lab.de/conferences/czs-school-2023/

Location: Mathematikon • Im Neuenheimer Feld 205 • 69120 Heidelberg

IWR Colloquium Summer Semester 2023

"On Signal Processing Flaws in Convolutional Neural Networks"

Prof. Janis Keuper • Head of the Institute for Machine Learning and Analytics (IMLA), Offenburg University, Germany
July 26, 2023 • 16:15

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)

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.

Location: Mathematikon • Conference Room, Room 5/104, 5th Floor • Im Neuenheimer Feld 205 • 69120 Heidelberg

"Machine learning galore!"

June 29, 2023 • 16:30 - 18:00

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

• 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

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

Location: Mathematikon • Conference Room, Room 5/104, 5th Floor • Im Neuenheimer Feld 205 • 69120 Heidelberg

Talk: "Through Two Doors at Once"

Anil Ananthaswamy • Journalist in Residence 2023 at the Heidelberg Institute for Theoretical Studies (HITS)
June 21, 2023 • 16:00

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.

About the speaker:

Anil Ananthaswamy is an award-winning science journalist and former staff writer and deputy news editor at New Scientist magazine. Currently, as a freelance journalist, he writes for Quanta, Scientific American, New Scientist and Nature, among others. His most recent book, Through Two Doors at Once was named one of Smithsonian's Favorite Books of 2018 and one of Forbes's 2018 Best Books About Astronomy, Physics and Mathematics. In 2023 Anil Ananthaswamy was selected as “Journalist in Residence" at HITS.

Location: Mathematikon • Conference Room, Room 5/104, 5th Floor • Im Neuenheimer Feld 205 • 69120 Heidelberg

IWR Colloquium Summer Semester 2023 &
HGS MathComp Romberg Inauguration Lecture

"Exploring Multi-Million Compound Spaces With Chemical Accuracy Using Machine Learning"

Romberg Visiting Scholar - Prof. Heather J. Kulik • Chemistry and Chemical Engineering, Massachusetts Institute of Technology, USA
May 24, 2023 • 16:15

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.

Link HGS MathComp Romberg Program: www.mathcomp.uni-heidelberg.de/romberg-program

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.

Location: Mathematikon • Conference Room, Room 5/104, 5th Floor • Im Neuenheimer Feld 205 • 69120 Heidelberg

"Machine learning galore!"

April 27, 2023 • 16:30 - 18:00

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

Location: Mathematikon • Conference Room, Room 5/104, 5th Floor • Im Neuenheimer Feld 205 • 69120 Heidelberg

IWR Colloquium Summer Semester 2023

"MDM & OpenEDC: Next-Generation Study Databases in Medicine"

Prof. Martin Dugas • Director, Institute for Medical Informatics, Medical Faculty of Heidelberg University
April 26, 2023 • 16:15

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 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.

Location: Mathematikon • Conference Room, Room 5/104, 5th Floor • Im Neuenheimer Feld 205 • 69120 Heidelberg

International Symposium

"Machine Learning and Applications in the Humanities and Social Sciences"

February 13-15, 2023

Monday • February 13, 2023

09:00 Lisa Beinborn
10:15 Josif Grabocka

Tuesday • February 14, 2023

09:00 Pia Bideau
10:15 Georgia Koppe
11:30 Stefan Radev
14:00 Alina Roitberg

Wednesday • February 15, 2023

10:30 Fengxiang He
15:00 Viktor Bengs

To help plan the catering, please register for free:
https://www.mlai.uni-heidelberg.de/en/machine-learning-talks-on-campus

Location: Mathematikon • Conference Room, Room 5/104, 5th Floor • Im Neuenheimer Feld 205 • 69120 Heidelberg

IWR Colloquium Winter Semester 2022 / 2023

ONLINE EVENT: "Randomization techniques for solving large scale linear algebra problems"

Prof. Laura Grigori • Director of Research, INRIA Paris • Alpines group, joint with Laboratoire J.L. Lions, Sorbonne University, Paris, France
February 1, 2023 • 17:00

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.

"Machine learning galore!"

January 19, 2023 • 16:30 - 18:00

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

• Lab presentations:
  • Tilman Plehn, Rebecca Wade, Bernhard Höfle, Jakob Zech
• Science talks:
  • Stefan Radev (Köthe lab): Modeling Uncertainty with Neural Networks
  • Theo Heimel (Plehn lab): Normalizing Flows for LHC Physics
  • Annika Reinke (Maier-Hein lab): Pitfalls and recommendations for image analysis validation

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

Location: Mathematikon • Conference Room, Room 5/104, 5th Floor • Im Neuenheimer Feld 205 • 69120 Heidelberg

IWR Colloquium Winter Semester 2022 / 2023

"Accelerated Sampling and Improved Synthesis in Diffusion Models"

Tim Dockhorn • David R. Cheriton School of Computer Science • University of Waterloo, Canada
January 11, 2023 • 16:15

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.

Location: Mathematikon • Conference Room, Room 5/104, 5th Floor • Im Neuenheimer Feld 205 • 69120 Heidelberg