Seminar Series of the Cluster for Excellence "Machine Learning"

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Wednesday, 2:00 - 3:00 pm, followed by get-together

Lecture hall, AI Research Building, Maria von Linden-Str. 6 (ground floor), 72076 Tübingen
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PROGRAM

• 16.11.2023    Neural Flow Operators: Learning dynamical systems from data
                               (and what you can do with them)
                               Prof. Daniel DURSTEWITZ
                               Central Institute of Mental Health (CIMH)
                              Professor in Theoretical Neuroscience / Department Head Website

  Most systems we encounter in science and engineering, at one level or the other could be cast in the form of systems of differential equations, whose short- and long-term behavior is the subject of dynamical systems (DS) theory. Can we infer generative DS models directly from time series observations without explicit knowledge of the underlying governing equations? In my talk I will first provide some necessary background in DS theory, and then discuss recent deep learning approaches for approximating the flow of unknown DS from data. I will in particular focus on challenges in training, recent teacher forcing techniques to overcome these, and how to map systems evolving on multiple time scales and observed through multiple data modalities. I will also discuss how to analyze and interpret trained systems. Concepts will be illustrated on various examples from neuroscience with DS models trained to recreate neurophysiological recordings.

• 01.03.2023   Reverse-engineering Drosophila action selection and movement control
                           Prof. Pavan Ramdya
                           Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
                           Director of the Neuroengineering Laboratory Website

  A shared goal of neuroscience and robotics is to understand how systems can be built to move effectively and autonomously through the world. However, state-of-the-art algorithms for selecting and executing limb behaviors in robots are still quite primitive compared with those used by animals. To inform the design of artificial systems, we are investigating how the fly, Drosophila melanogaster, selects and controls its behaviors and how this process can be modulated by learning. I will discuss how we are pursuing this research program using a combination of machine learning, microscopy, and modeling.

• 26.10.2022   Machine learning for improved understanding and projections of climate change
                              Veronika Eyring
                              Head of Earth System Model Evaluation and Analysis Department at the German Aerospace Center
                              (Deutsches Zentrum für Luft- und Raumfahrt) and Professor and Chair of Climate Modeling at the
                              University of Bremen. Website

  Climate models are fundamental to understanding and projecting climate change. The models have continued to improve over the years, but considerable biases and uncertainties in their projections remain. A large contribution to this uncertainty stems from differences in the representation of phenomena such as clouds and convection that occur at scales smaller than the resolved model grid. This impacts the models’ ability to accurately project global and regional climate change, climate variability, and extremes. High-resolution, cloud resolving models with horizontal resolution of a few kilometers alleviate many biases of coarse-resolution models for deep clouds and convection, but they cannot be run at climate timescales for multiple decades or longer due to high computational costs. In this talk I will present work from my group and collaborators where we use short regional and global cloud resolving simulations to develop machine learning based atmospheric parametrizations for a reduction of long-standing biases in climate models. While unconstrained neural networks often learn spurious relationships that can lead to instabilities in climate simulations, causally informed deep learning can mitigate this problem by identifying direct physical drivers of subgrid-scale processes. Trust and generalizability of the ML models can be further improved by introducing climate invariant variables, physical constraints, or equation discovery. Our approach can drive a paradigm shift in current climate and Earth system modelling towards a new data-driven, yet still physics-aware, ML-based hybrid climate model for improved understanding and projections of climate change.

  About Veronika Eyring

  Veronika Eyring is Head of the Earth System Model Evaluation and Analysis Department at the German Aerospace Center (DLR) Institute of Atmospheric Physics and Professor of Climate Modelling at the University of Bremen. She maintains a strong collaboration with the National Center for Atmospheric Research (NCAR, USA) as Affiliate Scientist, with the DLR Causal Inference Group in Jena that she founded in 2017, and with the team of the European Research Council (ERC) Synergy Grant on "Understanding and Modelling the Earth System with Machine Learning (USMILE)". Veronika's research focuses on understanding and modelling the Earth system with machine learning to improve climate projections and technology assessments for actionable climate science.

  She has extensive experience in the coordination of large international research projects, e.g. in her role as Chair of the World Climate Research Programme (WCRP) Coupled Model Intercomparison Project (CMIP) Panel (2014-2020). She has authored many peer-reviewed journal articles and has contributed to the Intergovernmental Panel on Climate Change (IPCC) climate assessments since many years, including her role as Coordinating Lead Author for Chapter 3 “Human influence on the climate system” in the IPCC Sixth Assessment Report of Working Group I. Veronika is Fellow of the European Lab for Learning & Intelligent Systems (ELLIS), Member of the Scientific Advisory Board of Worldfund, and actively involved in WCRP activities for many years. Veronika received the Gottfried Wilhelm Leibniz Prize in 2021 for her significant contributions to improving the understanding and accuracy of climate projections through process-oriented modeling and model evaluation.

• 18.05.2022  Learning compositional, structured, and interpretable models of the world
                             Hedvig Kjellström
                             Professor in the Division of Robotics, Perception and Learning, Department of Intelligent Systems,
                             at KTH Royal Institute of Technology, Sweden, and Principal AI  Scientist at Silo AI, Sweden.  Website

  Despite their fantastic achievements in fields such as computer vision and natural language processing, state-of-the-art deep learning approaches differ from human cognition in fundamental ways. While humans can learn new concepts from just a single or few examples, and effortlessly extrapolate new knowledge from concepts learned in other contexts, deep learning methods generally rely on large amounts of data for their learning. Moreover, while humans can make use of contextual knowledge of e.g. laws of nature and insights into how others reason, such information is generally hard to exploit in deep learning methods.

  Current deep learning approaches are indeed purposeful for a wide range of applications where there are large volumes of training data and/or well defined problem settings. However, models that learn in a more human-like manner have the potential to be more adaptable to new situations, be more data efficient and also more interpretable to humans - a desirable property e.g. for intelligence augmentation applications with a human in the loop, e.g. medical decision support systems or social robots.

  In this talk I will describe a number of projects in my group where we explore disentanglement, temporality, multimodality, and cause-effect representations to accomplish compositional, structured, and interpretable models of the world.

  About Hedvig Kjellström
  Hedvig Kjellström is a Professor in the Division of Robotics, Perception and Learning at KTH Royal Institute of Technology, Sweden. She is also a Principal AI Scientist at Silo AI, Sweden and an affiliated researcher in the Max Planck Institute for Intelligent Systems, Germany. She received an MSc in Engineering Physics and a PhD in Computer Science from KTH in 1997 and 2001, respectively, and thereafter worked at the Swedish Defence Research Agency, before returning to a faculty position at KTH. Her present research focuses on methods for enabling artificial agents to interpret human and animal behavior. These ideas are applied in the study of human aesthetic bodily expressions such as in music and dance, modeling and interpreting human communicative behavior, the understanding of animal behavior and cognition, and intelligence amplification - AI systems that collaborate with and help humans.

  Hedvig has received several prizes for her research, including the 2010 Koenderink Prize for fundamental contributions in computer vision. She has written around 130 papers in the fields of computer vision, machine learning, robotics, information fusion, cognitive science, speech, and human-computer interaction. She is mostly active within computer vision, where she is an Associate Editor for IEEE TPAMI and regularly serves as Area Chair for the major conferences.
   

• 01.12.2021   Beyond the First Portrait of a Black Hole  
                              Katherine L. (Katie) Bouman
                              Assistant Professor of Computing and Mathematical Sciences, Electrical Engineering and Astronomy at
                             California Institute of Technology Pasadena, CA, USA.  Website
  As imaging requirements become more demanding, we must rely on increasingly sparse and/or noisy measurements that fail to paint a complete picture. Computational imaging pipelines, which replace optics with computation, have enabled image formation in situations that are impossible for conventional optical imaging. For instance, the first black hole image, published in 2019, was only made possible through the development of computational imaging pipelines that worked alongside an Earth-sized distributed telescope. However, remaining scientific questions motivate us to improve this computational telescope to see black hole phenomena still invisible to us and to meaningfully interpret the collected data. This talk will discuss how we are leveraging and building upon recent advances in machine learning in order to achieve more efficient uncertainty quantification of reconstructed images as well as to develop techniques that allow us to extract the evolving structure of our own Milky Way's black hole over the course of a night.
   

• 06.10.2021   Employing Immersive Virtual Reality and Machine Learning to Reveal Geometric Principles
                              of Individual and Collective Decision-Making   
                              Iain Couzin
                              Director of the Max Planck Institute of Animal Behavior, Department of Collective Behaviour
                              and the Chair of Biodiversity and Collective Behaviour at the University of Konstanz  Webseite
  I will discuss our development and application of new machine learning technologies for automated tracking and identification of unmarked individuals, computational sensory reconstruction, bio-mimetic robotics, and ‘holographic’ virtual reality (VR) for animals, and demonstrate how these quantitative methodologies provide new insights into individual and collective sensing and decision-making. For example, they allow us to reconstruct (automatically) the dynamic, time-varying sensory networks in large animal collectives, to identify at any instant in time the most socially-influential individuals within groups, and to predict cascades of social contagion (behavioural change) before they actually occur. Furthermore, such methodologies enable us to study phenomena across multiple scales of biological organisation, from neural interactions to individual and collectives decision-making. Specifically, we have recently investigated how animals choose among spatially-discrete options, a central challenge in their lives. Employing an integrated theoretical and experimental approach (using immersive VR), with both invertebrate and vertebrate models—the fruit fly, desert locust and zebrafish—we find that the brain spontaneously reduces multi-choice decisions into a series of abrupt (critical) binary decisions in space-time, a process that repeats until only one option—the one ultimately selected by the individual—remains. This mechanism facilitates highly effective decision-making, and is shown to be robust both to the number of options available, and to context, such as whether options are static (e.g. refuges) or mobile (e.g. other animals). In addition, we find evidence that the same geometric principles of decision-making occur across scales of biological organisation, from neural dynamics to animal collectives, suggesting they are fundamental features of spatiotemporal computation.
   

• 07.07.2021     Beyond Expectations & Better than Average: Foundations of Machine Learning Systems
                              Robert Williamson
                              Professorship for 'Foundations of Machine Learning Systems'
                              at our Cluster of Excellence  "Machine Learning"
  Machine Learning (ML) technology provides value by its integration into a system, either technological or socio-technical. This requires an external outward-looking view of ML, rather than the usual internal inward-looking view of much ML research. I will illustrate the external view by considering some foundational concerns regarding  ML which motivates the research I propose to conduct in Tübingen.  The starting point turns out to be a more careful analysis of expectation or averages. Somewhat astonishingly, there is a lot to be said, and a lot to be yet discovered, about this apparently elementary building block of ML systems, and about its natural generalisations. I will argue why one should consider moving beyond expectations, and explore some of the intriguing technical problems that naturally arise. I will also explain how replacing expectation by something else offers an attractive alternate approach to questions of fairness in ML, but how this choice, like other approaches,  is inherently value-laden in a strong sense.
   

• 09.06.2021      Learning from Trees - Modeling, Simulation, and Inference of Microbial Genome Evolution
                              Franz Baumdicker
                              Head of the Independent Research Group 'Mathematical and Computational Population
                              Genetics', a joint group of Tübingen's Excellence Clusters "Controlling Microbes to Fight
                              Infections " and "Machine Learning" 
  Population genetics aims to understand how the observed genetic diversity emerged. In population genetics, many theoretical results have been developed in times where not much genomic and genetic data were available. These theory-driven results are still essential for our research, but data-driven discoveries have meanwhile dramatically changed our view of evolution and ecology, in particular for bacteria.
  The vast amount of newly sequenced genetic data leads to a multitude of interesting applications in the emerging field of machine learning in population genetics. The main challenge is that sequence data are not independent of one another, but rather are linked by their phylogenetic relationship, often represented by a tree sequence. Thus independent training data generation relies heavily on simulation procedures. I will present some of our approaches to develop, analyze, and apply supervised machine learning tools that can use this phylogenetic relationship to improve our understanding of bacterial genome evolution.
   

• 14.04.2021  Machine learning for medical image analysis and why clinicians are not using it
                             Christian Baumgartner   
                            Head of the Independent Research Group 'Machine Learning in Medical Image Analysis' at our  
                            Cluster of Excellence  "Machine Learning"
   

• 03.03.2021    Challenges in Renewable Energy Systems: A (mostly) Probabilistic Perspective
                             Nicole Ludwig
                            Head of the Early Career Research Group 'Machine Learning in Sustainable Energy Systems
                            (MSES)' at our Cluster of Excellence  "Machine Learning"
   

• 03.02.2021   Scalable Bayesian Inference: New Tools for New Challenges
                            Robert Bamler
                            Professorship for "Data Science and Machine Learning"
                            at our Cluster of Excellence  "Machine Learning"
                            Abstract
   

• 13.01.2021    Generalizing from sparse data and learning from other people - Charley Wu
                            -ONLINE-
                            Head of the Independent Research Group "Human and Machine Cognition"
                            at our Cluster of Excellence  "Machine Learning"
                            Abstract
   

• 02.12.2020     Morals and Methodology - Konstantin Genin
                            -ONLINE-
                            Head of the Independent Research Group "Epistemology and Ethics of Machine Learning"
                            at our Cluster of Excellence  "Machine Learning"
   

• 14.10.2020      CANCELED
   

• 01.04.2020    CANCELLED DUE TO CORONA CRISIS
                         How to be fair - The concept of fairness from a Computational Social Choice perspective
                         Britta Dorn (Host: Fabian Sinz)
                         Mathematical Structures in Computer Science, Department of Computer Science,
                         University of Tübingen.  Webpage
                         Abstract
   

• 06.05.2020      CANCELLED DUE TO CORONA CRISIS
                        Bob Williamson (Host: Ulrike von Luxburg)
                        Distinguished Researcher in Data61 and Professor at Research School of Computer Science,
                        Australian National University, Canberra
   

• 04.03.2020    Ian Couzin  -- CANCELLED
   

• 05.02.2020    Learning and artificial intelligence in the quantum domain
                         Hans Briegel (Host: Eric Raidl)
                          Institute for Theoretical Physics, University Innsbruck & Department of Philosophy,
                          University Konstanz    Webpage
                          Abstract
   

• 08.01.2020    Mining observations and climate models for detecting and attributing anthropogenic
                              climate change in the world’s water cycle
                              Lukas Gudmundsson (Host: Fabian Sinz)
                         Institute for Atmospheric and Climate Science, ETH Zürich  Webpage
                         Abstract
   

• 04.12.2019    Explaining and Representing Novel Concepts With Minimal Supervision
                              Zeynep Akata (Host: Fabian Sinz)
                        Cluster of Excellence "Machine Learning", Explainable Machine Learning, University Tübingen
                        Webpage
   

• 21.10.2019        Biomarker Discovery in Clinical Time Series
                                Karsten Borgwardt (Host: Fabian Sinz)                     
                        Department of Biosystems Science and Engineering, ETH Zuerich. Webpage
                       Abstract
   

• 06.11.2019       Gaussian Process emulation of tsunami and climate models                            
                               Serge Guillas (Host: Motonobo Kanagawa)                   
                         University College London / The Alan Turing Institute, UK.  Webpage 
                         Abstract
   

• 03.07.2019      Machine Learning, Neuroscience, and Spiking Neural Networks
                               Robert Legenstein (Host: Harald Baayen)
                          Inst. for Theoretical Computer Science, Graz University of Technology, Austria 
                          Webpage
                          Abstract

Philosophy of Science Meets Machine Learning (PhilML'2024)

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September 11 - 13, 2024

Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6, Tübingen

Organizers: Markus Ahlers, Raysa Benatti, Heather Champion, Timo Freiesleben, Konstantin Genin, Thomas Grote, Sebastian Zezulka

Registration: Online registartion here.                                                                               
                          Please note: there is a modest registration fee.

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Machine learning methods have become a mainstay in the toolkit of various scientific disciplines. For the fourth time, the PhilML'24 conference offers the opportunity to explore how recent developments in machine learning change the process of scientific research. The main conference will take place from September 11-13 and will be preceded by a one-day graduate workshop on September 10. We invite submissions of extended abstracts both for the conference and the graduate workshop. 

The PhilML conference and workshop are organized jointly by the Ethics and Philosophy Lab and the Epistemology and Ethics of Machine Learning group at the Tübingen Cluster of Excellence "Machine Learning".

The workshop’s central topics include

1. Reflections on key topics such as learning, reliability, causal inference, robustness, explanation, trust, transparency, and understanding.
2. Implications of machine learning for the sciences, e.g. physics, cognitive science, biology, psychology, social science, or medicine.
3. Implications of machine learning for scientific methodology, e.g. model-building and model selection, design of experiments, conceptual engineering.
4. Issues arising at the intersection of machine learning and public policy, e.g. risk assessment, resource allocation, climate and energy policy, and the provision of public services.
5. Novel considerations raised by foundation models e.g., authorship, latent representation, or nativism/empiricism.

|  Program  |
 

Wednesday, September 11

Thursday, September 12

Friday, September 13

   More information on the event website.

6th Cluster Conference
"Machine Learning in Science" 2024

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► Tuesday, July 9

► Wednesday, July 10

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Venue: Neckawa (Freistil), Wöhrdstraße 25, 72072 Tübingen

Please note: Registration is closed.

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PROGRAM

All times are provided in CEST, Central European Summer Time.

Program [PDF]

Tuesday, July 9, 2024

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Wednesday, July 10, 2024

Healthy Minds in Academia

The talk and workshop series Healthy Minds leads you through interactive sessions and expert talks presenting tools and knowledge to learn together how to better take care of our mental health in academia and beyond. It will take place regularly over the upcoming months and is funded by the Cluster of Excellence and the AI Center.

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How can I boost my mental health? A resource oriented approach. 

 Wednesday, March 27, 2024 at 1:30pm

Venue: Zoom

Speaker: Suzanne Jones

This workshop offers you the chance to reflect on your inner and outer resources. You will find suggestions on how to re-load your personal battery and strengthen your mental health through positive emotions and an optimistic focus.

Suzanne Jones is a psychologist and systemic therapist and a counsellor for health promotion and stress prevention: http://www.suzanne-jones.de

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Stress management skills - Increasing mental health and well-being.

 Wednesday, April 24, 2024 at 12.30pm

Venue: AI Research Building, Lecture Hall, Maria-von-Linden-Straße 6, Tübingen und Zoom

Speaker: Fanny Kählke

The workshop provides an overview of strategies to promote positive mental health under stressful work conditions. You will learn about different (instrumental, regenerative, and mental) ways to cope with stress and reflect on your own coping strategies and future needs.

Mehr Informationen hier.

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Study psychologically fit.

 Wednesday, May 29, 2024 at 1.30pm

Venue: Zoom

Organizers: Beate Schulze and Hendrik Hutthoff

In the two hours of the workshop, students can expect an authentic, intensive forum in which psychological crises can be discussed. In the last half hour, students can ask any questions they have about the topic.

More information here.

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Decolonizing Academia - How (not) to Fail a Decolonial Lecture Series

 Wednesday, June 26, 2024 at 1.30pm

Venue: AI Research Building, Lecture Hall, Maria-von-Linden-Straße 6, Tübingen und Zoom

Organizer: Sharon Nathan

Mehr Informationen here.

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Workshop: Intercultural and diversity competences and inclusion in diverse, interdisciplinary research groups

 Wednesday, July 24, 2024 at 1.30pm

Venue: AI Research Building, Lecture Hall, Maria-von-Linden-Straße 6, Tübingen and Zoom

Organizer: Imke Lode 

Imke Lode is a certified trainer for intercultural competences, see https://lindengruen.de/dr-imke-lode/.

More information here.

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Healthy Minds in Academia

Kick-off event

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 Tuesday, March 26, 2024 at 3pm

Venue: AI Research Building, Lecture Hall, Maria-von-Linden-Straße 6, Tübingen

Organizers: Nina Effenberger, Janne Lappalainen

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The talk and workshop series Healthy Minds leads you through interactive sessions and expert talks presenting tools and knowledge to learn together how to better take care of our mental health in academia and beyond. It will take place regularly over the upcoming months and is funded by the Cluster of Excellence and the AI Center.

In this kick-off event, two PhD students will present research on the mental health situation of PhD students at the University of Tübingen itself! In addition, representatives of the mental health support from the AI Center and the psychosocial counseling of the university will introduce themselves as persons of contact. We will present the goals of our talk series and conclude with an outlook on the next events before we invite you to snacks and drinks!

This event is targeted towards employees at the University of Tübingen and the MPIs—including PIs, postdocs, PhD students, and administrative staff.

Machine Learning from Theory to Application

Workshop for International Women's Day

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 Friday, March 8, 2024 | 9am - 1.30pm

Venue: AI Research Building, Lecture Hall, Maria-von-Linden-Straße 6, Tübingen

Organizers: Tübingen Women in ML (TWiML)

Registration: We kindly ask all participants to register here.

Program [PDF]

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Program

Bridging Industry and Academia

2nd Workshop of the Tübingen Women in Machine Learning

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 Friday, October 11, 2024 | 9 am -- 4:30 pm

Venue: Max-Planck-Insitute for Intelligent Systems, Tübingen, lecture hall ground floor

Organizers: Tübingen Women in ML (TWiML)

Registration: We kindly ask all participants to register here until October 1st.

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PROGRAM

The event is supported by WiML, Cluster of Excellence 'Machine Learning' and IMPRS-IS.
We will assign a NeurIPS travel award for the best poster.
All participants must abide the WiML CoC.

Workshop on Bandits and Statistical Tests

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Thursday, November 23 & Friday, November 24, 2023

Venue: Neues Palais, Am Neuen Palais 10, 14469 Potsdam

Hosts: Claire Vernade (Cluster Machine Learning, Tübingen) and Alexandra Carpentie (Maths department, University of Potsdam)

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We are pleased to invite you to our upcoming Workshop on Statistical Tests in Machine Learning, with a specific emphasis on Online Learning and Bandit Algorithms. The workshop aims to provide participants with a comprehensive understanding of statistical testing methodologies in the context of machine learning, particularly in the dynamic and ever-changing landscape of online and bandit settings.

This workshop is intended for researchers, practitioners, and students interested in the intersection of machine learning, statistics, and algorithm design.

Registration is closed now.

Tutorials:

• Bandit algorithms and testing: Prof. Aurelien Garivier, ENS Lyon, France

• Minimax tests: Dr. Nicolas Verzelen, INRAE, Montpellier, France

Keynote speakers: 

• Prof. Thomas Berrett, University of Warwick

• Prof. Rui Castro, TU Eindhoven

• Prof. Cristina Butucea, CNRS, France

• Ariane Marandon, Sorbonne University, France

• Prof. Peter Grünwald, CWI and Leiden University, Amsterdam

• Dr. Remy Degenne, INRIA, Lille, France

• Dr. Emilie Kaufmann, CNRS, INRIA, Lille, France

• Prof. David Preinerstorfer, University of St Gallen, Switzerland

For more Information, please check here.

Presenting our art exhibit
IN-ML-OUT - Wind Energy and Machine Learning

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  Friday October 27, 2023 at 5:00 pm

Venue: swt-KulturWerk (Werkstraße 4, 72074 Tübingen)

Admission free.

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Machine learning can be used to make the best possible use of wind energy in times of climate change. Nina Effenberger and Nicole Ludwig, who are engaged in research on sustainable energies in the Cluster of Excellence, will demonstrate how this can be achieved on October 27, 2023 at 5 pm in Tübingen's swt-KulturWerk: They want to use the interactive art exhibit "IN-ML-OUT " to advance the dialog between research, society and politics and demonstrate the potential of machine learning. The exhibit allows visitors to experience that our actions influence the climate, which solutions researchers can support with the help of machine learning, and which initiatives and projects on renewable energies already exist.

Following the exhibit presentation, there will be a discussion on the question “How can AI support the transformation of energy systems?”. Nicole Ludwig will participate as well as Peter Seimer (Spokesperson for Digitization, Green Party in the State Parliament of Baden-Württemberg) and Philipp Staudt (Digitized Energy Systems, University of Oldenburg). It will be moderated by Olaf Kramer (Research Center for Science Communication, University of Tübingen).

More information
 

"Artificial Intelligence, Trustworthiness and Explainability"
Closing Conference

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  Tuesday October 24 to Thursday October 26, 2023

Venue: Max-Planck-Institute for Intelligent Systems (lecture hall, ground floor), Max-Planck-Ring 4, 72076 Tübingen

Organizers: Saeedeh Babaii, Sara Blanco, Oliver Buchholz, Eric Raidl

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The project „Artificial Intelligence, Trustworthiness and Explainability (AITE)“, funded by the Baden-Württemberg Stiftung, is a joint project of our Cluster "Ethics and Philosophy Lab" and the "International Centre for Ethics in the Sciences and Humanities" (IZEW) at the University of Tübingen.

Presently, it remains opaque why machine learning systems (ML) decide or answer as they do. When an image classifier says "this is a train", does it 'recognise' the train or only the rails, or something totally different? How can we be sure that it decides as it does for the right reasons? This problem is at the heart of at least two debates: Can we trust artificial intelligent (AI) systems? And if so, on which basis? Would an explanation of the decision help our understanding and ultimately foster trust? And if so, what kind of explanation? These are the central questions being adressed in the AITE project.

At the AITE closing conference scientists working on this project present their work, exchange with experts in the field and other interested scholars, and engage with the wider public in a panel discussion.

More information on the  conference website
 

Panel discussion
„Who regulates AI?"

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  Friday October 20, 2023, at 08:00 pm

Venue: Uhlandsaal der Museumsgesellschaft, Wilhelmstr. 3, Tübingen

Admission is free.

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On Friday, October 20, at 8 pm, the Stadtmuseum Tübingen and our University will host a panel discussion on the question "Who regulates AI?".
The four panelists are all members of our cluster: Moritz Hardt, Ulrike von Luxburg, Michèle Finck and Carsten Eickhoff. The panel discussion will be moderated by SWR.

More information
 

Philosophy of Science Meets Machine Learning (PhilML2023)

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September 12-14, 2023

Location: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6, Tübingen

Organizers: Timo Freiesleben, Konstantin Genin, Thomas Grote, Sebastian Zezulka

Registration: We kindly ask all participants and speakers to register by 31.07.2023.

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Machine learning methods have become a mainstay in the tool-kit of various scientific disciplines. PhilML’23 offers an opportunity to explore whether and how exactly recent developments in the field of machine learning potentially transform the process of scientific inquiry. For this purpose, it sets out to analyse the field of machine learning through the lens of philosophy of science, including cognate fields such as epistemology and ethics. In addition, we are also interested in contributions from machine learning researchers/scientists, addressing foundational issues of their research. Similar to the previous workshops, we try to bring together philosophers from different backgrounds (from formal epistemology to the study of the social dimensions of science) and machine learning researchers.

The workshop`s central topics are:
(i) A critical reflection on key-concepts, such as ‘learning’, ‘causal inference’, ‘robustness’, ‘explanation’ or ‘understanding’.
(ii) The implications of machine learning for the special sciences, e.g. cognitive science, biology, social science or medicine.
(iii) The ethics of machine learning-driven science, e.g. the moral responsibilities of researchers, ethical issues in model evaluation, or issues at the intersection of science and policy.
(iv) Social aspects of machine learning-driven science, e.g. the impact of funding structures on research.
 

|  Program  |
 

Tuesday, September 12

Wednesday, Sept 13

Thursday, Sept 14

  For more information, please check out the event website.

5th Cluster Conference
"Machine Learning in Science" 2023

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► Tuesday, July 11 | 9am - 7pm |

► Wednesday, July 12 | 9am - 5pm |

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Venue: Freistil, Wöhrdstraße 25, 72072 Tübingen

For those who cannot attend the conference in person, the presentations will be broadcast on
Zoom (identification code: 145437).

Please note: (1) Registration with full name only. (2) It will not be possible to ask questions via Zoom.

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PROGRAM

All times are provided in CEST, Central European Summer Time.

Program [PDF]

Tuesday, July 11, 2023

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Mittwoch, 12. Juli 2023

| Weather forecasting and climate simulation:
   What are the challenges for AI and numerical weather prediction |

Dale Durran

Professor of Atmospheric Sciences
University of Washington, Seattle, USA

WHEN:     Wednesday, 28.06.2023 at 2:00 pm, followed by a get-together
WHERE:   Lecture hall, AI Research Building, Maria von Linden-Str. 6 (ground floor), 72076 Tübingen
HOSTS:    Martin Butz und Matthias Karlbauer
 

| How to simulate a connectome? |
 

Srinivas Turaga

HHMI Janelia, USA

WHEN:    Tuesday, June 21, 3 - 4 pm
WHERE:  Lecture Hall, AI Research Building, Maria von Linden-Str. 6, 72076 Tübingen
HOST:      Jakob Macke

ABSTRACT
We can now measure the connectivity of every neuron in a neural circuit, but we are still blind to other biological details, including the dynamical characteristics of each neuron. The degree to which connectivity measurements alone can inform understanding of neural computation is an open question. We show that with only measurements of the connectivity of a biological neural network, we can predict the neural activity underlying neural computation. Our mechanistic model makes detailed experimentally testable predictions for each neuron in the connectome. We found that model predictions agreed with experimental measurements of neural activity across 24 studies. Our work demonstrates a strategy for generating detailed hypotheses about the mechanisms of neural circuit function from connectivity measurements.

Paper: https://www.biorxiv.org/content/10.1101/2023.03.11.532232

| Machine learning in soft matter |

Laura Filion

Physics Department, Soft Condensed Matter Group, Universiteit Utrecht

WHEN:    Wednesday, 14.06.2023 at 2:15 pm
WHERE:  N3, Hörsaalzentrum Morgenstelle, Universität Tübingen
HOST:     Martin Oettel

Abstract
Developments in machine learning (ML) have opened the door to fully new methods for studying phase transitions due to their ability to extremely efficiently identify complex patterns in systems of many particles. Applications of machine learning techniques vary from the use of developing new ML-based methods for detecting complex crystal structures, to locating phase transitions, to speeding up simulations. The rapid emergence of multiple applications of machine learning to statistical mechanics and materials science demonstrates that these techniques are becoming an important tool for studying soft matter systems. In this talk, I will briefly present an overview of the work my group is doing on using machine learning to study soft matter systems with a focus on how machine learning might help us to understand the structure – or lack thereof – in glasses.

Guided tours around the Max Planck Campus

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  June 14, 2023, 4:30pm - 6:00pm

Venue: Max-Planck-Campus, Tübingen

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Which institutions and facilities belong to the Cyber Valley ecosystem? What is their research focused on? The tour is part of the accompanying program of the exhibition "Cyber and the City".

The tour will be in German and therefore the event description is only available in German.

In den letzten Jahren entstanden viele neue Gebäude bei der Sternwarte in der Tübinger Nordstadt sowie auf dem Max-Planck-Campus. Welche Institutionen gehören zum Cyber Valley Ökosystem? Cyber Valley Eventmanagerin Tabea Brietzke erklärt in dieser kostenfreien Führung die Zusammenhänge und zeigt, wo an künstlicher Intelligenz (KI) geforscht wird. Die Führung macht Halt an verschiedenen Gebäuden, unter anderem dem Tübinger Max-Planck-Institut für intelligente Systeme. Sie endet am Tübingen AI Center, wo ein KI-Kunstprojekt zu erneuerbaren Energien aus dem Exzellenzcluster „Maschinelles Lernen. Neue Perspektiven für die Wissenschaft“ vorgestellt und ein Einblick in die Arbeit des Tübingen AI Centers gegeben wird.

Das Angebot ist Teil des Begleitprogramms der KI-Ausstellung „Cyber and the City – Künstliche Intelligenz bewegt Tübingen“ im Tübinger Stadtmuseum. Die Führung ist kostenfrei und richtet sich an alle Interessierten, Vorwissen zu KI ist nicht notwendig. Die Tour eignet sich auch für Rollstühle und Kinderwägen. Die Plätze sind auf 15 Personen begrenzt. Eine Anmeldung ist notwendig; der Anmeldelink wird zeitnah auf dieser Seite eingestellt. Die Details zum Treffpunkt bekommen Sie nach der Anmeldung per Mail zugeschickt.

Zur Anmeldung geht es hier.

| The Ethical Significance of Pattern |

Markus Ahlers

Institute for Philosophy, Gottfried Wilhelm Leibniz University Hannover, Germany

WHEN:      Monday, 12.06.2023 at 11:00
WHERE:    Meeting Room ground floor, room 00-10/A12, AI Research Building,
                  Maria von Linden-Str. 6, 72076 Tübingen
 

Art exhibit „IN ML OUT“

______________________________________________________________________________________________________________________________________________________________

► Opening event on May 15, 2023, 6:00 pm

Venue: Staatliche Akademie der Bildenden Künste Stuttgart, Am Weißenhof 1, Neubau 2, 70191 Stuttgart
______________________________________________________________________________________________________________________________________________________________

Machine learning holds great potential for future renewable energy supply. In order to enter into an active dialog with citizens and decision makers, the exhibit "IN-ML-OUT" was initiated. It is the result of a cooperation of our researchers Nicole Ludwig and Nina Effenberger with design students of the "Staatliche Akademie der Bildenden Künste" in Stuttgart and the "Zentrum für rhetorische Wissenschaftskommunikationsforschung zur Künstlichen Intelligenz" (RHET AI) at the University of Tübingen.

Further information

Workshop on "Explainability in Machine Learning"

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  Tuesday March 28 and Wednesday March 29, 2023

Venue: Alte Aula, Münzgasse 30, 72070 Tübingen

Workshop organizers: Zeynep Akata, Stephan Alaniz, Christian Baumgartner, Almut Sophia Koepke, Massimiliano Mancini, Seong Joon Oh

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Deep learning has enabled major advances in machine learning. However, the deployment of deep learning frameworks in settings that are safety-critical or that impact society requires their decision-making to be explainable. This is fundamental for building trustworthy and user-oriented machine learning models. The aim of this workshop is to generate awareness around explainability in machine learning which is a topic of growing interest. Furthermore, we aim to encourage interdisciplinary interaction and collaboration between researchers from the University of Tübingen and other international institutions that work on different aspects of explainability, in particular in the context of computer vision.

  Program: The workshop will contain both keynote talks from known researchers in the field as well as invited talks and spotlight presentations of recent advancements in the field of explainability.

► Preliminary program and more information: https://www.eml-unitue.de/eml-workshop

Spring School on Probabilistic Numerics

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Monday, March 27 - Wednesday, March 29, 2023

Venue: Audimax, Neue Aula, Geschwister-Scholl-Platz, 72074 Tübingen

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The first ever Probabilistic Numerics Spring School and Research Workshop consists of two days of (in-person) lectures, keynotes, and tutorial sessions, from the 27th to the 28th of March. It will be held in English, and is aimed at graduate students, researchers, and professionals interested in probabilistic numerical methods.The school features lectures and keynotes by leading experts, and hands-on code tutorials.

The school will be followed by a workshop on the 29th of March, 2023. The workshop offers a stage for advances in the probabilistic computation, by researchers working in the field.

For more information visit the Workshop webpage

Spring Meeting of the German Physical Society (DPG), Section Condensed Matter Section

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Tuesday, March 27, 2023

Venue: Technische Universität Dresden - Campus Südvorstadt, Bergstraße 64, 01069 Dresden

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From 26 March to 31 March 2023 the DPG Spring Meeting of the Condensed Matter Section (SKM) will take place on the campus of the Technical University Dresden.

A Focus Session "Physics meets ML", organized by Sabine Andergassen and Moritz Helas, Martin Gärttner and Markus Schmitt, will be held on March 27, 2023. The corresponding tutorial will take place on March 26, 2023. 

PROGRAM

Machine learning for complex quantum systems
Stefanie Czischek, University of Ottawa
Marcello Dalmonte, ICTP Trieste
Florian Marquardt, MPI & University of Erlangen-Nürnberg*
Christof Weitenberg, University of Hamburg

Understanding machine learning as complex interacting systems
Elena Agliari, Roma “La Sapienza”
Marc Mezard, Bocconi University
Manfred Opper, TU Berlin*
Cengiz Pehlevan, Harvard University
Zohar Ringel, Hebrew University of Jerusalem*

*Tutorial Lecturer

Workshop "Machine Learning meets Neurosciences"

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Wednesday, February 15, 2023 | 1:00 - 4:00 pm

Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6, Tübingen

Organizers: Tobias Kaufmann, Jakob Macke

Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk (sebastian.schwenkspam prevention@uni-tuebingen.de).

Program [PDF]

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Workshop "Machine Learning meets Quantitative Social Sciences"

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Wednesday, February 8, 2023 | 08:25 - 04:00 pm

Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6

Organizers: Dominik Papies, Augustin Kelava

Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de) until February 3, 2023.

Programm [PDF]

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Workshop "Machine Learning meets Machine Learning“

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Wednesday, February 1, 2023 | 11:00 am - 02:00 pm

Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6

Organizers: Philipp Hennig, Matthias Hein

Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de) until January 27, 2023.

Program [PDF]

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Workshop "Machine Learning meets Physics"

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Wednesday, February 01, 2023 | 01:00 - 05:00 pm

Venue: Auf der Morgenstelle 10, Building C, Room 7E02

Organizers: Martin Oettel, Frank Schreiber

Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de).

Program [PDF]

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Workshop "Machine Learning meets Qualitative Social Sciences"

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Wednesday, January 25, 2023 | 09:00 - 1:30 pm

Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6

Organizers: Christoph Bareither, Thomas Thiemeyer

Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de) until January 20, 2023.

Program [PDF]

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Panel 1: Human-ML-Relationships in Scientific Practice

Panel 2: Machine Learning and Society

Workshop "Machine Learning meets Geosciences"

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Wednesday, January 25, 2023 | 02:00 - 5:00 pm

Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6

Organizers: Thomas Scholten, Todd Ehlers

Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de) until 20. Januar 2023

Program [PDF]

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Panel 1: Current Research Topics in Geosciences

Panel 2: Current Research Topics in Machine Learning

Workshop "Machine Learning meets Medicine"

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Wednesday, January 18, 2023 | 1:00 - 4:00

Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6

Organizers: Manfred Claassen, Sergios Gatidis

Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de)

Program [PDF]

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Workshop "Machine Learning meets Linguistics"

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Wednesday, January 11, 2023 | 2:00 - 5:00 pm

Venue: AI Research Building, Lecture Hall (Ground Floor), Maria-von-Linden-Straße 6

Organizers: Michael Franke, Detmar Meurers

Registration: This is an internal workshop of the Cluster.
If you are interested, please register (email to Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de)

Programm [PDF]

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Philosophy of Science Meets Machine Learning (PhilML)

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October 20 - 22, 2022

Lecture Hall, Max-Planck-Guest House, Max-Planck-Ring 6, 72076 Tübingen
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More information about the event as well as the program can be found here.

4th Cluster Conference
"Machine Learning in Science" 2022
 

► Tuesday, July 12 | 9:00 am - 6:00 pm |

► Wednesday, July 13 | 9:00 am - 5:00 pm |

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Venue: Westspitze, Eisenbahnstraße 1, 72072 Tübingen

For all those who cannot attend the conference in person, we will stream the presentations on
Zoom

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PROGRAM

All times are provided in CEST, Central European Summer Time.

Program [PDF]

Tuesday, July 12, 2022

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Wednesday, July 13, 2022

Science and Innovation Days

From June 29 to July 2, 2022, Tübingen's largest research institutions will open their doors and present their research to interested public. The Science and Innovation Days will take place all over the city, the program can be found  here. The events are in German.

The members of our cluster participate with the following contributions:
 

Wednesday, June 29, 2022

Friday, July 1, 2022

Saturday, July 2, 2022

PhD Workshop "Artificial Intelligence and Machine Learning Research and Democracy" followed by public panel discussion

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      April 2 - 4, 2022, at the University of Tübingen

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About the workshop:
As the use of Machine Learning algorithms and Artificial Intelligence pervades all areas of our lives and societies, it also affects fundamental pillars of democracy, such as public debate, political decision making and (dis)information through media. In our PhD workshop “Artificial Intelligence and Machine Learning research and democracy – an interdisciplinary perspective” we will provide a platform for young researchers from all relevant disciplines – from machine learning research to political science and every nuance in between – to engage in exchange of methods and current state of the art research. Our guiding question will be: How can we shape the co-development of AI/ ML research and democracy?

More information: ai-and-democracy-workshop.de

„KI gestaltet Demokratie – Demokratie gestaltet KI“ on April 4, starting 6:30 pm

At the end of the workshop, a public discussion on "AI and Democracy" will take place on April 4, starting 6:30 p.m., at the "Westspitze" in Tübingen.
Everyone interested is cordially invited to this event.
Registration via the following link: ai-and-democracy-workshop.de/podium
 

Workshop on Introduction to Machine Learning

The Machine Learning ⇌ Science Colaboratory is glad to invite you to its in-person, hands-on Workshop on Introduction to Machine Learning.

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Wednesday, March 30, 2022, 9:00 to 13:00

The event will take place on site at Maria-von-Linden-str. 6, 72076 Tübingen, Lecture Hall (ground floor, room 00-28/A7).

Registration:
Apply for the workshop by filling out the  Registration Form
Please note that attendance is limited to 12 participants! You will hear from us approximately one week before the event.

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What? We’ll present first the fundamentals of ML with the aid of interactive demonstrations. Equipped with the necessary concepts, we will then jointly look at your specific research problems and data, and discuss how to design a ML project for them.

Who? Researchers from MSc student to PI level at the University of Tübingen who would like to know more about machine learning and how it can help with their research. Scholars from all disciplines are invited to join, no math or programming skills will be assumed.

How to prepare? Think about the characteristics of relevant datasets that you have access to, and be ready to discuss your use-case with the group. Bring your own laptop.

Covid regulations: 3G-rules apply (proof of vaccination or past infections or negative test) + FFP2 mask

For more information, please visit the Machine Learning ⇌ Science Colaboratory Website

Workshop "Philosophy of Science Meets Machine Learning"
 

 The Workshop takes place in person in Tübingen

    on Nov.   9 - 10 in the Alte Aula (Münzgasse 30)

    on Nov. 11 - 12 in the Max Planck Institute for Intelligent Systems (Max-Planck-Ring 4)

The workshop is organised by the ‘Ethics and Philosophy Lab’ of the Cluster of Excellence ‘Machine Learning: New Perspectives for Science’ at the University of Tübingen (Workshop Convenors: Thomas Grote, Thilo Hagendorff, Eric Raidl).

Registration: Space is limited. Guests please register via: rebigtimspam prevention@googlemail.com

Machine learning does not only transform businesses and the social sphere, it also fundamentally transforms science and scientific practice. The workshop focuses on that latter issue. It aims to discuss whether and how exactly recent developments in the field of machine learning potentially transform the process of scientific inquiry. For this, it sets out to analyse the field of machine learning through the lenses of philosophy of science, epistemology, research ethics and cognate fields such as sociology of science. The workshop will bring together philosophers from different backgrounds (from formal epistemology to the study of the social dimensions of science) and machine learning researchers. The workshop`s central topics are:

1. A critical reflection on key-concepts, such as ‘learning’, ‘inference’, ‘explanation’ or ‘understanding’.
2. The implications of machine learning for the special sciences, e.g. cognitive science, social science or medicine.
3. The ethics of machine learning-driven science, e.g. the moral responsibilities of researchers.
4. Social aspects of machine learning-driven science, e.g. the impact of funding structures on research.

 Program

DAY 1: November 9 - Alte Aula

DAY 2: November 10 - Alte Aula

DAY 3: November 11 - MPI-IS

DAY 4: November 12 - MPI-IS

| Bandit learning with delays in Non-stationary environments |

Claire Vernade

DeepMind, London, UK
 

WHEN:      Thursday, 30. September 2021, 9:00 am
WHERE:     Hybrid-Event:
Claire will give her talk in the Lecture Hall, AI Building, Maria-von-Linden-Straße 6. Due to the current Covid restrictions, seats are limited. If you are a PI of our Cluster and want to join in person, please contact Elena Sizana to register. You need to be fully vaccinated, tested or recovered.
We invite everyone else to participate via Zoom: https://zoom.us/j/99906352769

ABSTRACT

We consider the problem of learning with delayed bandit feedback, meaning by trial and error, in changing environments. This problem is ubiquitous in many online recommender systems that aim at showing content, which is ultimately evaluated by long-term metrics like a purchase, or a watching time.  Mitigating the effects of delays in stationary environments is well-understood, but the problem becomes much more challenging when the environment changes. In fact, if the timescale of the change is comparable to the delay, it is impossible to learn about the environment, since the available observations are already obsolete. However, the arising issues can be addressed if relevant intermediate signals are available without delay, such that given those signals, the long-term behavior of the system is stationary. To model this situation, we introduce the problem of stochastic, non-stationary and delayed bandits with intermediate observations. We develop a computationally efficient algorithm based on UCRL, and prove sublinear regret guarantees for its performance.

Workshop "Simulation-based Inference for scientific discovery"

This workshop will be taking place online on Zoom.

The workshop is jointly organized by the ML⇌Science Colaboratory, the MLS Chair (Jakob Macke) and Helmholtz AI.

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Registration:
Apply for the workshop by filling out the Registration Form  Deadlin for application is Aug 31, 2021.

We can only select 20 participants for the workshop. To come up with that list and make a final decision, we will form a small selection board. We will inform successful applicants subsequently.

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You use simulation in physics, economics, archaeology or any other domain of your choice? You want to find the simulator's parameters that best fits the observations? Then simulation-based inference is something for you!

We teach a practical simulation-based inference workshop to help you understand recent machine learning techniques and apply them to your problem.

Apply to learn, have fun, and participate in a supportive and inclusive community. The workshop will combine lectures and practical hands-on sessions by experts in the field. We strive to provide a seamless computing environment for you to focus on the content rather than in import errors.

We plan to cover the following topics:

| Program

Machine Learning meets Environmental Science

Friday, September 25, 2020
 

Meeting Venue
Neue Aula, Audimax, Geschwister-Scholl-Platz

Organizers
Prof. Martin Butz (ML Cluster, Dep. Of Computer Science)
Prof. Christiane Zarfl (Center for Applied Geoscience)

Registration
Registration is required by Email until Sept 22, 2020.

___________________________________________________________________________________________________________________________________________

PROGRAM

09:00    Welcome and Introduction: Cluster of Excellence – Machine Learning for Science
              Philipp Berens
09:10     ML Transfer Center and simulation based inference
              Álvaro Tejero-Cantero & Jakob Macke
09:40     Day-ahead optimization of production schedules for saving electrical energy costs
              Thomas Stüber & Michael Menth
10:00     Uncovering hidden structure in climate data
               Bedartha Goswami
10:20    Coffee Break
10:40    Learning spatiotemporal distributed generative graph neural networks
              Martin Butz
11:00    Modeling environmental processes in rivers
             Christiane Zarfl
11:20    Can plants learn? Coupling models and data in eco-evolutionary research
             Sara Tomiolo & Maximiliane Herberich (from Katja Tielbörger’s group)
11:40    Improving the spatial prediction of soil organic carbon content in two contrasting climatic regions by
             stacking machine learning models and rescanning covariate space
             Ruhollah Taghizadeh-Mehrjardi (from Thomas Scholten’s group)
12:00    The climate situation: Facts and Necessities
             Harald Baayen
12:05    Snack Break
12:30    ML and mobile robots in environmental science
             Andreas Zell
12:50    Turbulent transport of energy, momentum and matter by large data sets
              obtained from airborne probing of the lower atmosphere
             Jens Bange
13:20    Status of the CRC 1253 CAMPOS - Catchments as Reactors:
             Metabolism of Pollutants on the Landscape Scale
             Christiane Zarfl
13:40    Funding options in the ML Cluster of Excellence and beyond
             Tilman Gocht
14:00   End

_______________________________________________________________________________________________________________________________________________________________________________________________

  Aim of the day

Environmental science studies spatio-temporal dynamics of various processes and on different topics, including climate and weather, geology, hydrology, vegetation and agriculture, various forms of pollution (e.g. of organic pollutants), to name just a few. In all these cases, multiple, often interrelated data sources are available at varying degrees of spatial and temporal granularity. Moreover, human activities, such as river dam building, CO2 release, plantations, industry etc., strongly influence the unfolding dynamics. Critical principles – such as basic laws in physics – apply universally in such systems. Environmental science has strong expertise in modeling the underlying processes – typically by systems of partial or ordinary differential equations.

As a result, besides the expertise about the underlying processes, environmental science offers two types of data – real-world data as well as data from the respectively available models of the considered environmental system. This offers essentially the perfect basis for a meaningful, science-driven application of ML algorithms. On the one hand, the parameters of the differential equations may be optimized more effectively by means of state-of-the-art gradient-based approximation approaches from ML. On the other hand, the available models may be augmented or fully substituted by distributed spatio-temporal, generative neural network approaches, such as convolutional networks, graph networks, autoencoders, recurrent neural networks, and combinations thereof.

Seeing that models are available to pre-train and analyze potentially applicable ML architectures, expertise is available to tune these models to the actual underlying processes, and that real world data is available to further train and test the generalizability of these ML architectures, it is time that ML meets Environmental Science! The aim is to foster collaboration with a focus on two main potential strands. First, available models of differential equations and involved prior situation assumptions may be optimized by means of state-of-the-art ML techniques. Second, ML techniques and particularly distributed, generative artificial neural networks may be designed to infer the processes and structures that generate particular data patterns, thus enabling (i) the fast, efficient, and accurate simulation of environmental processes and (ii) the consideration of impacts of human actions, including the potential to derive optimal actions for steering the environmental system towards a desired (stable / homeostatic) direction.

Tangles: from graph minors to identifying political mindsets

Talk by Reinhard Diestel, University of Hamburg, Department of Mathematics

WHEN: Friday, 24.01.2020 at 10:00
WHERE: Lecture hall ground floor, MPI for Intelligent Systems
 

ABSTRACT

Traditional clustering identifies groups of objects that share certain qualities. Tangles do the converse: they identify groups of qualities that often occur together. They can thereby discover, relate, and structure types of phenomena: of behaviour, political views, texts, or bacteria. Tangles can identify key phenomena that allow predictions of others. Tangles also offer a new paradigm for clustering in large data sets. Tangle clusters are, by necessity, fuzzy: they tell us where in a large structure a cluster lies, which key properties of data points identify it, and how the overall data set is structured with respect to these clusters. But they do this without needing, or attempting, to assign individual points to any cluster. Tangles of graphs are central to the theory of graph minors developed by Robertson and Seymour for their celebrated proof of the graph minor theorem. For many years, however, algorithmic applications of graph minor theory were largely confined to applications of tree-decompositions, an overall structure dual to the existence of large tangles. Very recently, tangles have been axiomatised in a way that makes them directly applicable to a much wider range of contexts than graphs, even outside mathematics. This talk will outline how this works, with an emphasis on the basic concepts of abstract tangle theory and how these are applicable in real-world scenarios. No knowledge of graph minor theory will be needed.

Machine Learning meets Social Science

Max Planck Institute for Intelligent Systems, Lecture Hall ground floor
Max-Planck-Ring 4, 72076 Tübingen
 

Registration: If you plan to attend the meeting, please register by sending an email to Sebastian Schwenk.
 

  09:00 - 10:30  Session "ML Cluster" contenteditable="false"
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09:00 - 09:20    Introduction  &  Fairness in Machine Learning
                          Ulrike von Luxburg
09:20 - 09:40   On the Integration of Machine Learning into Healthcare
                          Thomas Grote
09:40 - 10:00   Explaining Neural Network Decisions with
                          Minimal Supervision         
                          Zeynep Akata
10:00 - 10:20   Explainability & Explanation
                          Eric Raidl

10:30 - 11:00 Coffee Break

  11:00 - 12:30  Session "Sociology"
--------------------------------------------------------

11:00 - 11:20   What does ML do? Key questions from a sociology of
                          technology and science perspective
                          Renate Baumgartner
11:20 - 11:40    (How) does ML affect the shop floor and the
                          sociology of work?
                          Werner Schmidt
11:40 - 12:00   Three Worlds of AI – How political strategies differ
                          Daniel Buhr
12:00 - 12:20   Classifying scientific texts with supervised learning algorithms
                          Steffen Hillmert

12:30 Discussion and End

Cluster Conference "Machine Learning in Science"

Monday, July 22, 2019 | 09:00 am - 07:00 pm | Alte Aula

Tuesday, July 23, 2019 | 09:00 am - 04:00 pm | Pfleghofsaal

Registration: If you would like to attend the meeting, please register by July 15 latest by sending an email to Sebastian Schwenk. Please indicate on which day(s) you would like to participate.

Programm

Monady, July 22, 2019

Alte Aula, Münzgasse 30, 72070 Tübingen

Tuesday, July 23, 2019

Pfleghofsaal, Schulberg 2 (Pfleghof), 72070 Tübingen

Machine Learning meets Physics

AI Building, Lecture Hall (ground floor)

Maria von Linden Str. 6, 72076 Tübingen
 

9:00 – 9:25:   Frank Schreiber et al: “Analysis of X-ray Scattering Data Using Artificial Neural Networks”
9:25 – 9:50:   Hendrik Lensch: “Deep Learning on Unstructured Point Clouds”
9:50 – 10:15: Martin Oettel: “Density functionals from machine learning”

10:15 – 10:45: Coffee break

10:45 – 11:10: Miriam Klopotek: “Variational autoencoders put up to the test in learning a statistical-mechanical model system”
11:10 – 11:35: Georg Martius: “Machine Learning for Equation Identification”
11:35 – 12:00: Andreas Zell: “ML, Physics and Robotics”

12:00 – 13:00: Discussion

Machine Learning meets Law, Neue Aula
 

9:00 Stefan Thomas: Algorithms and Antitrust: How can the law make sure that machine learning does not impede competitive freedom?

9:15 Thilo Hagendorff: Regularory Needs in the Field of AI - From Ethics to Policies

9:30 Thomas Grote: The ethics of (expert-level) algorithmic decision-making

9:45 Isabel Valera: Fairness in Machine Learning

10:00 Oliver Kohlbacher: Legal issues related to AI in medicine

10:15 Discussion as long as we want

Internal Meeting of the Cluster "Machine Learning in Science": November 12-13, 2018

Meeting location: Ground floor lecture hall at the Max-Planck Institute for Intelligent Systems (directions)

Preliminary schedule:

Nov 12th
=========
9:00-10:00   Welcome, information & organisation
                     Ulrike von Luxburg and Philipp Berens
10:00-12:15   Short introductory talks of new group leaders
10:00-10:15   Jörg Stückler
10:15-10:30   Falk Lieder

10:30-11:00   Coffee break

11:00-11:15   Georg Martius
11:15-11:30   Britta Dorn
11:30-11:45   Fabian Sinz
11:45-12:00   Zhaoping Li
12:00-12:15   Gabriele Schweikert
12:15-12:30   Augustin Kelava
12:30-12:45   Michael Krone

12.45 -14:00   Lunch

14:00-15:00   Spotlights for open questions
                       (all PIs: please prepare exactly 1 slide (3 minutes) and send it to Alla at latest Nov 11)
15:00-15:30   Coffee break
15:30-18:00   Work phase for project teams

18:30   Dinner at Hofgut Rosenau

Nov 13th
========
9:00-10:30   Discussion of open questions, directions, ideas for how
                     the Excellence Cluster should start and work

10:30-11:00   Coffee break

11:00-12:00   Discussion and work phase

12:00-14:00   Lunch

14:00-15:00   Presentations of project ideas and discussion

15:00-15:30   Coffee break

Download program