Events of the Cluster of Excellence

Due to the current situation associated with the corona crisis, almost all our currently planned face-to-face events will either take place in an online format or have been temporarily put on hold.


For all past events see our ARCHIVE below.

Cluster Colloquium "Machine Learning" -- 1° Wednesday of the month -- ONLINE

Seminar Series of the Cluster for Excellence
"Machine Learning"

Wednesday, 2:00 - 3:00 pm

The Colloq will take place virtually on Zoom
Link to Colloq


3rd Annual Conference "Machine Learning in Science", July 12 + 13, 2021

3rd Cluster Conference
"Machine Learning in Science" 2021

► Monday,  July 12  |
► Tuesday, July 13  |


Info on the conference format and program will be announced here soon.

Workshop "Philosophy of Science Meets ML", POSTPONEDto November 2021

Workshop "Philosophy of Science Meets Machine Learning"


The workshop planned for April 27 - 30, 2021, is now scheduled for

     November 2021

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

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.

Event Archive

Here you can find all past Cluster events.

Cluster Colloquium "Machine Learning" - 1° Wednesday of the month

Seminar Series of the Cluster for Excellence "Machine Learning"

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

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


Workshop on the "Philosophy of Medical AI", October 08-09, 2020 -- ONLINE

Virtual Workshop on the Philosophy of Medical AI

► Thursday, October 08  | 09:30 - 17:30
► Friday, October 09 | 10:00 - 16:00

The workshop is open to the public, no registration will be needed.

Thomas Grote; Ethics and Philosophy Lab; Cluster of Excellence “Machine Learning: New Perspectives for Science”

  The workshop will take place virtually on Zoom.
      Link to Meeting on THURSDAY Meeting-ID: 977 8903 0792
      Link to Meeting on FRIDAY Meeting-ID: 990 9618 3434

Recent advances in deep learning have fuelled the interest in applying AI-systems within healthcare. Indeed, a vast literature of high-profile studies indicates, that the opportunities that AI provides for different branches of medicine are manifold: From improving medical diagnosis, to the timely prediction of health-risks and the discovery of new drugs. At the same time, there are worries that the imperfections of current AI systems might perpetuate systemic ills in the healthcare system or even create new ethical problems. The aim of this workshop is to reflect on the opportunities and challenges of utilising AI in medicine. To this end, the workshop brings together philosophers of science, medical ethicists as well as researchers in machine learning or bioinformatics.

Thursday October 08, 2020

09:30 - 10:00  

Welcome address and brief introduction

10:00 - 10:50    

Sune Holm (University of Copenhagen)

Equality and Fair Algorithmic Decision Making

11:00 - 11:50     

Atoosa Kasirzadeh (Australian National University/University of Toronto)

The Use and Misuse of Counterfactuals in Fair Machine Learning



13:00 - 13:50

Georg Starke (University of Basel)

Does Trust Constitute an Adequate Epistemic Stance Towards Medical AI?

14:00 - 14:50  

Geoff Keeling (Stanford University)

Decision-Support Systems and Clinical Reasoning – A Peircian Approach



15:30 -16:20    

Manfred Claassen (University of Tübingen)

Challenges in machine learning driven translation of single-cell biology studies

16:30 - 17:30           

Alex London (Carnegie Mellon University, Pittsburgh)

Keynote: Ethics in Medical AI: Explaining Models vs Explaining the Warrant for Their Use


Friday October 09, 2020 




11:00 - 11:50

Chris Burr (Alan Turing Institute)

Responsible Innovation and Digital Psychiatry



13:00 - 13:50

Rune Nyrup (University of Cambridge)

Value Transparency in Science and Machine Learning

14:00 - 14:50 

Zeynep Akata (University of Tübingen)

Explaining Neural Network Decisions Via Natural Language

15:00 - 16:00

Alex Broadbent (University of Johannesburg)

Keynote: Why Robots Cannot Do Epidemiology

Machine Learning meets Environmental Science, September 25, 2020

Machine Learning meets Environmental Science

Friday, September 25, 2020

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

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

Registration is required by Email until Sept 22, 2020.



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.

2nd Annual Conference "Machine Learning in Science", July 21-23, 2020 -- ONLINE

2nd Cluster Conference
"Machine Learning in Science" 2020

► Tuesday, July 21  | 2:00 pm - 5:45 pm
► Wednesday, July 22  | 2:00 pm - 6:15 pm
► Thursday, July 23  | 2:00 pm - 6:30 pm


Change in program, Thursday July 23: Manfred Claassen's talk at 2 pm unfortunately needs to be cancelled. Phillip Berens kindly takes over the time slot at short notice with a talk on "Towards hybrid models of retinal circuits - integrating biophysical realism, anatomical constraints and predictive performance".

The conference will take place virtually on Crowdcast with a live stream on Youtube.
The conference is open to the public.
To participate in the discussions via chat you will have to register via Crowdcast for each conference day by clicking on the corresponding event on our Crowdcast profile - the participants' cameras and microphones will remain switched off. You need to enter your email address first and then your full name (first name and surname).
If you only want to follow the talks, you can watch them via Youtube. Here you  find the live streams of all conference talks.  

Each talk takes 30 minutes plus 15 minutes discussion, each spotlight presentation 5 minutes plus 5 minutes discussion.

Important: All times are given in CEST, Central European Summer Time.

Program [PDF]

Tuesday, July 21, 2020

14:00 - 14:15             

Ulrike von Luxburg, Philipp Berens
Speakers of the Cluster of Excellence “Machine Learning”, University of Tübingen

► Opening Remarks

Dr. Simone Schwanitz
Head of Section, State Ministry for Science, Research and Art, Baden-Württemberg

► Welcome Address

14:15 - 15:00      

Kyle Cranmer (Center for Cosmology and Particle Physics, New York University)

Keynote Lecture: How Machine Learning Can Help us Get the Most out of our Highest Fidelity Physical Models

15:00 - 15:45       

Zeynep Akata (Department of Computer Science and Cluster of Excellence “Machine Learning”, University of Tübingen)

► Learning Decision Trees Recurrently through Communication

15:45 - 16:00      



Spotlight Presentations
Innovation Fund Projects of the Cluster of Excellence “Machine Learning”

16:00 - 16:10      

David Künstle

Machine Learning Approaches for Psychophysics with Ordinal Comparisons

16:10 - 16:20      

Zohreh Ghaderi / Hassan Shahmohammadi

Enhancing Machine Learning of Lexical Semantics with Image Mining

16:20 - 16:30      

Matthias Karlbauer

Causal Inference with a Spatio-Temporal Generative Model

16:30 - 16:40      

Thomas Gläßle / Kerstin Rau

Interpretable Spatial Machine Learning for Environmental Modelling

16:40 - 17:00      


17:00 - 17:45      

Jakob Macke (Department of Computer Science and Cluster

of Excellence “Machine Learning”, University of Tübingen

► Training Neural Networks to Identify Mechanistic Models of Neural Networks


Wednesday, July 22, 2020 

14:00 - 14:45               

Peter Dayan (Max Planck Institute for Biological Cybernetics, Tübingen)

Modelling and Manipulating Behaviour Using Recurrent Networks

14:45 - 15:30      

Dominik Papies (Faculty of Economics and Social Sciences, University of Tübingen)

Machine Learning Applications in Business and Economics - Can it Help us Understand the Relevance of Visual Product Characteristics?

15:30 - 15:45     



Spotlight Presentations
Innovation Fund Projects of the Cluster of Excellence “Machine Learning”

15:45 - 15:55         

Eric Raidl / Thomas Grote

Artificial Intelligence, Trustworthiness and Explainability

15:55 - 16:05              

Thilo Hagendorff

The Big Picture: Ethical Considerations and Statistical Analysis of Industry Involvement in Machine Learning Research

16:05 - 16:15      

Daniel Weber

Human-robot Interface with Eye-tracking

16:15 - 16:25      

Pablo Sanchez Martin

Exploring Ambient Awareness in Twitter

16:25 - 16:30      


16:30 - 17:15     

Ingo Steinwart (Department for Stochastics and Applications, University of Stuttgart)

Some Thoughts towards a Statistical Understanding of Deep Neural Networks

17:15 - 17:30 BREAK
17:30 - 18:15

Claire Monteleoni (Department of Computer Science, University of Colorado Boulder)

► Deep Unsupervised Learning for Climate Informatics


Thursday, July 23, 2020 

14:00 - 14:45             

Philipp Berens (Cluster of Excellence “Machine Learning”, University of Tübingen)

Towards hybrid models of retinal circuits - integrating biophysical realism, anatomical constraints and predictive performance

14:45 - 14:50      



Spotlight Presentations
Innovation Fund Projects of the Cluster of Excellence “Machine Learning”

14:50 - 15:00      

Jonas Ditz

Extending Deep Kernel Approaches for Better Prediction and Understanding of ADME Phenotypes and Related Drug Response

15:00 - 15:10      

Susanne Zabel

Visualizing Uncertainty from Data, Model and Algorithm in Large-Scale Omics Data

15:10 - 15:20      

Paolo Mazza

Understanding Quantum Effects in Neural Network Models through Machine Learning

15:20 - 15:30      

Jonathan Fuhr

Applied Causal Inference in Social Sciences and Medicine

15:30 - 15:45      


15:45 - 16:30      

Stefanie Jegelka (Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology)

► Keynote Lecture: Representation and Learning in Graph Neural Networks

16:30 - 17:15      

Bedartha Goswami (Cluster of Excellence “Machine Learning”, University of Tübingen)

 Inferring Climate Variability from Patterns Hideen in Modern and Paleo Time Series Data

17:15 - 17:30      


17:30 - 18:15      

Igor Lesanovsky (Department of Physics, University of Tübingen )

Neural Network Dynamics in Quantum Many-Body Systems

18:15 - 18:30       

Ulrike von Luxburg, Philipp Berens

Speakers of the Cluster of Excellence “Machine Learning”, University of Tübingen

Closing Remarks

Symposium 'Machine Learning in Science', July 7-8, 2020 -- ONLINE

Symposium 'Machine Learning in Science'

on July 7 - 8, 2020

Zoom Videoconference

Each presentation takes 30 minutes and is followed by a discussion of 15 minutes.
The discussion is followed by a 45 minutes non-public session, which will be organised as break-out-group, hence ALL participants can stay in the online conference room for the entire duration of the symposium.

Registration is required for Non-Cluster members only. Please send an email to Sebastian Schwenk (sebastian.schwenkspam, the link to the Zoom conference can then be provided.

Important: Participants who do not provide their full names will be excluded from the video conference by the moderators.

Program [PDF]

09:00 – 09:45

Nicole Ludwig (Karlsruhe Institute of Technology)

How Machine Learning Changes Research in Energy

09:45 – 10:15

Non-public session

10:30 – 11:15

Michal Rolínek (Max Planck Institute for Intelligent Systems, Tübingen)
Machine Learning and Combinatorial Optimization

11:15 – 11:45

Non-public session

11:45 – 13:00

13:00 –  13:45

Thilo Wrona (GFZ Helmholtz-Zentrum, Potsdam)

How can Machine Learning Help Us Advance Solid Earth Science?

13:45 –  14:15

Non-public session

14:30 – 15:15

Niklas Wahl (German Cancer Research Center – DKFZ, Heidelberg)

How will Machine Learning change Radiotherapy?

15:15 – 15:45

Non-public session

16:00 – 16:45

Charley Wu (Harvard University, Cambridge, USA)

Bridging the Gap Between Human and Machine Learning

16:45 – 17:15

Non-public session

End of 1st Day


09:00 – 09:45

Christin Beck (University of Konstanz)
Learning the Language of the Past: Historical Linguistics,
Natural Language Processing and Machine Learning

09:45 – 10:15

Non-public session

Talk by Reinhard Diestel - January 24, 2020

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


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, November 12, 2019

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"

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

1st Mini-Conference "Machine Learning in Science", July 22-23, 2019

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.


Monady, July 22, 2019

Alte Aula, Münzgasse 30, 72070 Tübingen


Opening Remarks

Ulrike von Luxburg, Philipp Berens

Speakers of the Cluster of Excellence “Machine Learning”, University of Tübingen


Towards Neural Networks Which Probably Know When They Don't Know

Matthias Hein

Department of Computer Science, University of Tübingen


Inception Loops - Using Deep Learning to Control Biological Neurons

Fabian Sinz

Department of Computer Science, University of Tübingen


Coffee Break


Machine Learning for Heterogeneous and Partially Biased Data in Medicine

Nico Pfeifer

Department of Computer Science, University of Tübingen


The Art of Using t-SNE for Visualization of Very Large Data Sets

Dmitry Kobak

Institute for Ophthalmic Research, University of Tübingen





Machine Learning inside Scientific Methods and Procedures

Philipp Hennig

Department of Computer Science, University of Tübingen



Dynamic Structural Equation Models in the Social and Behavioral Sciences
and Some Estimation Problems

Augustin Kelava

Methods Center, University of Tübingen



Identifying Climate, Vegetation, and Plate Tectonic Controls on Earth’s Topography

Todd Ehlers

Department of Geosciences, University of Tübingen


Poster Session and Coffee

Poster contributions, see below *


General Assembly of the Cluster of Excellence (non-public)


Speaker’s Dinner (non-public)

Tuesday, July 23, 2019

Pfleghofsaal, Schulberg 2 (Pfleghof), 72070 Tübingen


Language Change as a Random Walk in Vector Space

Gerhard Jäger

Institute of Linguistics, University of Tübingen


Ethics and Explainability

Eric Raidl, Thomas Grote, Thilo Hagendorff

Ethics & Philosophy Lab, Cluster of Excellence Machine Learning, University of Tübingen


Coffee Break



Filter ranking for neural network compression

Mijung Park

Department of Computer Science, University of Tübingen



Fairness and Interpretability in ML for Consequential Decision Making

Isabel Valera

Max Planck Institute for Intelligent Systems, Tübingen




Statistical Limits of Hypothesis Testing: Do We Expect Too Much from ML?

Debarghya Ghoshdastibar

Department of Computer Science, University of Tübingen


How to Learn Predictive Conceptual Structures, including Causal Relationships, and Generate Goal-Directed Control with them? Achievements and Challenges

Martin Butz

Department of Computer Science, University of Tübingen


Machine Learning Algorithms as Tools and Models in Vision Science

Felix Wichmann

Department of Computer Science, University of Tübingen


Closing Remarks

Ulrike von Luxburg, Philipp Berens

Speakers of the Cluster of Excellence “Machine Learning”, University of Tübingen


   * Poster Session, July 22, 16:00 – 18:00 *

Weber, D, Kasneci E., Zell A. (Cluster Innovation Fund Project)
Human-robot interface with eye-tracking and augmented reality to teach mobile robots about the real-world.

             University of Tübingen, Department of Computer Science

Valera I.1, Utz S.² (Cluster Innovation Fund Project)
Extracting expertise from tweets: Exploring the boundary conditions of ambient awareness).

             1Max Planck Institute for Intelligent Systems Tübingen, ² Leibniz-Institut für Wissensmedien

Luxburg U., Wichmann F. (Cluster Innovation Fund Project)

Machine learning approaches for psychophysics with ordinal comparisons

             University of Tübingen, Department of Computer Science

Zabel S.1, Hennig P.2, Nieselt K.1 (Cluster Innovation Fund Project)

Visualizing Uncertainty from Data, Model and Algorithm in Large-Scale Omics Data

             University of Tübingen, 1Center for Bioinformatics Tübingen, ²Department of Computer Science

Karlbauer, M.1, Lensch H.1, Scholten T.², Butz M.1 (Cluster Innovation Fund Project)

Short-to-Mid Scale Weather Forecasting with a Distributed, Recurrent Convolutional ANN

             University of Tübingen, 1Department of Computer Science, ²Department of Geosciences

Behrens, T.1, Schmidt, K.1, Hennig, P.², Scholten, T.1 (Cluster Innovation Fund Project)
Feature engineering for spatial modelling.

             University of Tübingen, 1Department of Geosciences, ²Department of Computer Science

Baayen H.1, Lensch H.² (Cluster Innovation Fund Project)
Enhancing Machine Learning of Lexical Semantics with Image Mining

            University of Tübingen, 1Department of Linguistics, ²Department of Computer Science


Macke J.1, Hennig P.², Berens P.³, Oberlaender M.4
Automatic Data-driven Inference of Mechanistic Models

              1Technische Universität München, Computational Neuroengineering Group 1

               University of Tübingen, ²Department for Computer Science, ³Institute for Ophthalmic Research

              4Center of advanced european studies and research

Pawlowski, T.1, Berens, P.², Kelava, A.³
Emotional cues and alcohol use: evidence from football.

               University of Tübingen, 1Department Institute of Sport Science, ²Institute for Ophthalmic Research, ³ Methods Center

Kilian P.
Predicting math student college dropout with sparse information using approaches from statistical learning

              University of Tübingen, Methods Center

Klopotek M., Oettel M.
Variational autoencoders put up to the test in learning a statistical-mechanical model system

             University of Tübingen, Institut für Angewandte Physik

Lin SC, Oettel M.
Classical density functionals from machine learning

             University of Tübingen, Institut für Angewandte Physik

Greco A.1, Starostin V.1, Hinderhofer A.1, Gerlach A.1, Karapanagiotis C.², Liehr S.², Kowarik S.²,
Schreiber F.1
Fast Scattering Data Analysis Using Machine Learning.
                1University of Tübingen, Institut für Angewandte Physik, Uni Tübingen, ² Bundesanstalt für Materialforschung
                 und -prüfung (BAM), Berlin

Sümer Ö.1,2, Kasneci E.1
Attention Flow: End-to-End Joint Attention Estimation

             University of Tübingen, 1Department of Computer Science, ²Hector Research Institute of Education Sciences and 
           Psychology (HIB)

Fuhl W., Kasneci G., Rosenstiel W., Kasneci E.
Training decision trees as replacement for convolution layers

               University of Tübingen, Department of Computer Science

Zadaianchuk A., Martius G.
Equation Learning for Extrapolation and Control

             Max Planck Institute for Intelligent Systems Tübingen

Machine Learning meets Physics, July 10, 2019

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

TÜFFF - Tübinger Fenster für Forschung, 24. Mai 2019

TÜFFF - Tübinger Fenster für Forschung

Spitzenforschung zum Anfassen für alle Altersgruppen

WANN:  Freitag, 24. Mai 2019, 15 – 22 Uhr
WO:       Hörsaalzentrum der Naturwissenschaften, Auf der Morgenstelle 16
              Eintritt frei

Das „Tübinger Fenster für Forschung“ (TÜFFF) bietet allgemein verständliche und interaktive Einblicke in die Tübinger Spitzenforschung. Mitmach-aktionen, Demonstrationen, Laborführungen, Vorträge, eine Informations-messe sowie ein Science Slam erwarten die interessierte Öffentlichkeit beim 4. TÜFFF an der Universität Tübingen. Durch die Aufbereitung und Präsentation aktueller Forschungsthemen für ein fachfremdes Publikum richtet sich die Veranstaltung an alle Altersgruppen.

Das Exzellenzcluster „Maschinelles Lernen“ beteiligt sich mit 8 Ständen am „Markt der Möglichkeiten“:
  •  Deep Capturing - Computer Vision, Prof. Hendrik Lensch
  •  Deep Deblurring - Computer Vision, Prof. Hendrik Lensch
  •  Interaktive Karte zur Bodenqualität im Raum Tübingen - Geowissenschaften, Prof. Thomas Scholten
  •  Was ist ein neuronales Netzwerk? – Bioinformatik, Prof. Dr. Kay Nieselt
  •  Antizipatives Verhalten in künstlichen neuronalen Netzen - Kognitionswissenschaften, Prof. Martin Butz
  •  Wie kann ein Computer lernen, Wörter in Latein, Russisch, Estnisch und Hebräisch zu beugen?
     Linguistik, Prof. Harald   Baayen
  •  Vorhersage von Blickrichtungen - Neurowissenschaften,
     Prof. Matthias Bethge
  •  Briefumschlag-Computer – Theorie des maschinellen Lernens,
     Prof. Ulrike Luxburg

Weitere Informationen im Programmheft und auf der Veranstaltungsseite

Symposium 'Machine Learning in Science', May 22, 2019

Symposium 'Machine Learning in Science'

May 22, 2019

Max-Planck-Gästehaus – Lecture Hall (Hörsaal)
Max-Planck-Ring 6, 72076 Tübingen



Tropical circulation: Current challenges and potential for machine learning algorithms
Bedartha Goswami
-- Potsdam Institute for Climate Impact Research (PIK), Germany


High-throughput behavioral analysis for neural circuit understanding
Alexander Mathis
-- Department of Molecular and Cellular Biology, Harvard University, USA


Coffee Break


Reverse Engineering the Early Visual System with Artificial Neural Networks
Stéphane Deny
-- Department of Applied Physics at Stanford University, USA


Visualization of georeferenced open government data: benefits, issues, opportunities for machine learning research
Auriol Degbelo
-- Institute of Geography, University of Osnabrück, Deutschland

Symposium 'Ethics and Philosophy of Machine Learning in Science', May 15, 2019

Symposium 'Ethics and Philosophy of Machine Learning in Science'

May 15, 2019

Max-Planck-Gästehaus – Lecture Hall (Hörsaal)
Max-Planck-Ring 6, 72076 Tübingen



Simplicity and Scientific Progress: A Topological Perspective
Konstantin Genin -- Department of Philosophy, University of Toronto, Canada


Learning Through Creativity
Caterina Moruzzi
-- Department of Philosophy, University of Nottingham, UK


Coffee break


Black-Boxes, Understanding, and Machine Learning
Emily Sullivan-Mumm
-- Ethics and Philosophy of Technology, Delft Data Science, The Netherlands


Working at the margins of machine learning – the ethics of labeling
Thilo Hagendorff
-- International Center for Ethics in the Sciences and Humanities,
University of Tübingen, Germany


Inductive Bias and Adversarial Data
Tom Sterkenburg -- LMU München, Munich Center for Mathematical Philosophy, Germany


Lunch break

13:30 - 14:30

Invited Talk

Co-Opt AI! Charting the emerging field of AI, ethics and social justice

Mona Sloane, Institute for Public Knowledge, New York University, USA

17:10 - 17:55

Conference Room

ML from a DiscO viewpoint: Compressed Sensing, Dictionary Learning and beyond
(machine learning)

Andreas M. Tillmann -- Operations Research & Visual Computing Institute,
RWTH Aachen, Deutschland

Symposium 'Machine Learning in Science', March 18 and 25 - 27, 2019

Symposium „Machine Learning in Science“

March 18 and 25-26, 2018

Max-Planck-Gästehaus – Lecture hall (Hörsaal)
Max-Planck-Ring 6, 72076 Tübingen


Monday, March 18, 2019

09:30 – 10:30

Neutrino Cosmology - Weighing the Ghost Particle with the Universe

   Dr. Elena Giusarma -- Simons Foundation, Flatiron Institute Center for Computational
   Astrophysics, New York, USA


Monday March 25, 2019


Information Field Theory
   PD Dr. Torsten Enßlin -- MPI für Astrophysik, Garching


Active machine learning for automating scientific discovery
   Prof. Dr. Roman Garnett -- Washington University in St. Louis, USA


Coffee break


Bayesian optimisation: nano-machine-learning
   Assoc. Prof. Dr. Michael Osborne -- University of Oxford, UK


Robust and Scalable Learning with Graphs
   Prof. Dr. Stephan Günnemann  -- TU München




Representing and Explaining Novel Concepts with Minimal Supervision

   Asst. Prof. Dr. Zeynep Akata -- University of Amsterdam


Coffee Break


Cluster Member Meeting and General Assembly (non-public)


Joint Dinner (by invitation)


Tuesday, March 26, 2019


Expressive, Robust and Accountable Machine Learning for Real-world Data
   Dr. Isabel Valera -- MPI for Intelligent Systems, Tübingen


Algorithms of Vision: From Brains to Machines and Back
   Dr. Alexander Ecker -- Universität Tübingen


Coffee break


From Paired to Unpaired Image-to-Image Translation and Beyond
   Dr. Radu Timofte -- ETH Zürich, Schweiz


Face processing: Bridging Natural and Artificial Intelligence

   Assoc. Prof. Dr. Angela J. Yu -- University of California San Diego, USA



14:00 From statistics to mechanisms, and back
   Prof. Dr. Jakob Macke -- TU München


Machine Learning meets Law, March 19, 2019

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

Meeting of the Cluster 'Machine Learning in Science', November 12-13, 2018

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