Seminarreihe des Exzellenzclusters "Maschinelles Lernen"

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Mittwoch, 14:00 - 15:00, mit anschliessendem Get Together

Hörsaal, AI Research Building, Maria von Linden-Str. 6 (Erdgeschoss), 72076 Tübingen
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PROGRAMM

•   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. Webseite
  
  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.  Webseite
  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 ML Systems   
                              Robert Williamson
                              Professur für "Foundations of Machine Learning Systems" an unserem
                              Exzellenzcluster "Maschinelles Lernen"
  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
                              Leiter der Unabhängigen Forschergruppe "Mathematical and Computational Population
                              Genetics", eine gemeinsame Arbeitsgruppe der beiden Tübinger Exzellenzclustern "Kontrolle
                              von Mikroorganismen zur Bekämpfung von Infektionen" und "Maschinelles Lernen" 
  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   
                            Leiter der Unabhängigen Forschergruppe "Maschinelles Lernen in der medizinischen Bildanalyse"
                            an unserem Exzellenzcluster "Maschinelles Lernen".
   

• 03.03.2021     Challenges in Renewable Energy Systems: A (mostly) Probabilistic Perspective
                              Nicole Ludwig
                             Leiterin der Early Career Forschergruppe "Maschinelles Lernen in Nachhaltigen
                             Energiesystemen" an unserem Exzellenzcluster "Maschinelles Lernen"    
   

• 03.02.2021    Scalable Bayesian Inference: New Tools for New Challenges - Robert Bamler
                           -ONLINE-
                            Professur für "Data Science und Maschinelles Lernen" an unserem 
                            Exzellenzcluster "Maschinelles Lernen" 
                            Abstract
   

• 13.01.2021    Generalizing from sparse data and learning from other people - Charley Wu
                            -ONLINE-
                            Leiter der Unabhängigen Forschergruppe "Human and Machine Cognition" an unserem 
                            Exzellenzcluster "Maschinelles Lernen"  
                            Abstract  
   

• 02.12.2020     Morals and Methodology - Konstantin Genin
                           -ONLINE-
                            Leiter der Unabhängigen Forschergruppe "Epistemologie und Ethik des maschinellen Lernens" an
                            unserem Exzellenzcluster "Maschinelles Lernen"
   

• 14.10.2020    ABGESAGT
                      

• 01.04.2020    FÄLLT CORONA-BEDINGT AUS
                        How to be fair - The concept of fairness from a Computational Social Choice perspective
                        Britta Dorn (Host: Fabian Sinz)
                        Mathematische Strukturen in der Informatik, Fachbereich Informatik, Universität Tübingen
                         Webseite
                         Abstract
                       

• 06.05.2020    FÄLLT CORONA-BEDINGT AUS
                        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  -- FÄLLT AUS

• 05.02.2020    Learning and artificial intelligence in the quantum domain
                        Hans Briegel (Host: Eric Raidl)
                        Institut für Theoretische Physik , Universität Innsbruck & Fachbereich Philosophie,
                        Universität Konstanz    Webseite
                        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)
                         Institut für Atmosphäre und Klima, ETH Zürich, Webseite
                         Abstract
   

• 04.12.2019    Explaining and Representing Novel Concepts With Minimal Supervision
                             Zeynep Akata (Host: Fabian Sinz) 
                        Exzellenzcluster Maschinelles Lernen, Erklärbares Machinelles Lernen, Universität Tübingen
                        Webseite
   

• 21.11.2019    Biomarker Discovery in Clinical Time Series
                             Karsten Borgwardt (Host: Fabian Sinz)                   
                         Department für Biosystems Science and Engineering, ETH Zuerich. Webseite
                         Abstract
        

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

• 03.07.2019      Machine Learning, Neuroscience, and Spiking Neural Networks
                               Robert Legenstein (Host: Harald Baayen)
                          Institut für Grundlagen der Informationsverarbeitung, TU Graz, Österreich
                          Webpage 
                          Abstract

Präsentation unseres Kunstexponats
IN-ML-OUT – Windenergie und Maschinelles Lernen

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  Freitag 27. Oktober 2023 um 17:00 Uhr

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

Eintritt kostenfrei

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Mit maschinellem Lernen kann Windenergie in Zeiten des Klimawandels bestmöglich nutzbar gemacht werden. Wie das geht, verraten Nina Effenberger und Nicole Ludwig, die im Exzellenzcluster zu nachhaltigen Energien forschen, am 27. Oktober 2023 um 17 Uhr im Tübinger swt-KulturWerk: Mithilfe des interaktiven Kunstexponats „IN-ML-OUT“ wollen sie den Dialog zwischen Forschung, Gesellschaft und Politik vorantreiben und dabei das Potential von maschinellem Lernen aufzeigen. Das Exponat macht erfahrbar, dass unser Handeln das Klima beeinflusst, welche Lösungsansätze Forschende mit Hilfe von Maschinellem Lernen unterstützen können und welche Initiativen und Projekte zu erneuerbaren Energien es bereits gibt.

Im Anschluss an die Präsentation des Exponats gibt es eine Diskussion zur Frage "Wie kann KI die Energiewende unterstützen?". Nicole Ludwig wird daran ebenso teilnehmen wie Peter Seimer (Sprecher für Digitalisierung, Grüne im Landtag von Baden-Württemberg) und Philipp Staudt (Digitalisierte Energiesysteme, Universität Oldenburg). Moderiert wird die Diskussion von Olaf Kramer (Forschungszentrum für Wissenschaftskommunikation, Universität Tübingen).

Mehr Informationen

"Artificial Intelligence, Trustworthiness and Explainability"
  Abschlusskonferenz

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  Dienstag 24. bis Donnerstag 26. Oktober 2023

Veranstaltungsort: Max-Planck-Institut für Intelligente Systeme (Hörsaal, Ergeschoss), Max-Planck-Ring 4, 72076 Tübingen

Konferenz-Organisatoren: Saeedeh Babaii, Sara Blanco, Oliver Buchholz, Eric Raidl

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Das Projekt „Artificial Intelligence, Trustworthiness and Explainability (AITE)“, gefördert von der Baden-Württemberg Stiftung, ist ein Gemeinschaftsprojekt des "Ethics and Philosophy Labs" unseres Clusters und dem "Internationalen Zentrum für Ethik in den Wissenschaften" (IZEW) der Universität Tübingen.

Gegenwärtig ist nicht klar, warum maschinelle Lernsysteme (ML) so entscheiden oder antworten, wie sie es tun. Wenn ein Bildklassifikator sagt: "Das ist ein Zug", erkennt er dann den Zug oder nur die Schienen oder etwas ganz anderes? Wie können wir sicher sein, dass er aus den richtigen Gründen so entscheidet? Dieses Problem steht im Mittelpunkt von mindestens zwei Debatten: Können wir künstlichen intelligenten Systemen (KI) vertrauen? Und wenn ja, auf welcher Grundlage? Würde eine Erklärung der Entscheidung zu unserem Verständnis beitragen und letztlich das Vertrauen fördern? Und wenn ja, welche Art von Erklärung? Dies sind die zentralen Fragen, mit denen sich das AITE-Projekt beschäftigt.

Auf der AITE-Abschlusskonferenz stellen die Wissenschaftlerinnen und Wissenschaftler des AITE-Projekts ihre Arbeit vor, tauschen sich mit Experten auf diesem Gebiet und anderen interessierten Forschenden aus und diskutieren in einer Podiumsdiskussion mit der Öffentlichkeit.

Mehr Informationen auf der Konferenz-Webseite

Podiumsdiskussion
„Wer kontrolliert KI?"

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  Freitag 20. Oktober 2023 um 20:00 Uhr

Veranstaltungsort: Uhlandsaal der Museumsgesellschaft, Wilhelmstr. 3, Tübingen

Eintritt kostenfrei

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Am Freitag, 20. Oktober, um 20 Uhr, veranstalten das Stadtmuseum Tübingen und unsere Universität eine Podiumsdiskussion zur Frage "Wer reguliert KI?".
Die vier PodiumsteilnehmerInnen sind alle Mitglieder unseres Clusters: Carsten Eickhoff, Michèle Finck, Moritz Hardt und Ulrike von Luxburg. Moderiert wird die Podiukmsdiskussion vom SWR.

Mehr Informationen
 

Philosophy of Science Meets Machine Learning (PhilML2023)

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12. - 14. September 2023

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

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

Registrierung: Wir bitten alle Teilnehmer und Referenten um eine Anmeldung bis zum 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.
 

|  Programm  |
 

Dienstag, 12. September

Mittwoch, 13. September

Donnerstag, 14. September

   Weitere Informationen auf der Veranstaltungs-Webseite.

5. Exzellenzcluster Konferenz
"Machine Learning in Science" 2023

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► Dienstag, 11. Juli | 09:00 - 19:00 |

► Mittwoch, 12. Juli | 09:00 - 17:00 |

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Tagungsort: Freistil, Wöhrdstraße 25, 72072 Tübingen

Für alle, die nicht persönlich an der Konferenz teilnehmen können, die Vortäge werden übertragen auf
Zoom (Kenncode: 145437)

Bitte beachten: 1. Anmeldung nur mit vollem Namen. 2. Es wird nicht möglich sein, über Zoom Fragen zu stellen.

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PROGRAMM

Alle Zeiten sind in MESZ, Mitteleuropäische Sommerzeit, angegeben.

Programm [PDF]

Dienstag, 11. Juli 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

WANN:   Mittwoch, 28.06.2023 um 14:00, mit anschliessendem Get Together
WO:         Hörsaal, AI Research Building, Maria von Linden-Str. 6 (Erdgeschoss), 72076 Tübingen
HOSTS:   Martin Butz und Matthias Karlbauer
 

| How to simulate a connectome? |
 

Srinivas Turaga

HHMI Janelia, USA

WANN:  Dienstag, 21. Juni, 15 - 16 Uhr
WO:        Hörsaal, 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

WANN:   Mittwoch, 14.06.2023 um 14:15
WO:         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.

| The Ethical Significance of Pattern |

Markus Ahlers

Institute for Philosophy, Gottfried Wilhelm Leibniz University Hannover, Germany

WANN:    Montag, 12.06.2023 um 11:00
WO:         Seminarraum Erdgeschoss, Raum 00-10/A12, AI Research Building,
                Maria von Linden-Str. 6, 72076 Tübingen

Kunstexponat „IN ML OUT“

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► Eröffnung am 15. Mai 2023, 18:00

Veranstaltungsort: Staatliche Akademie der Bildenden Künste Stuttgart, Am Weißenhof 1, Neubau 2, 70191 Stuttgart
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Maschinelles Lernen birgt ein großes Potential für die zukünftige Versorgung mit erneuerbaren Energien. Um in einen aktiven Dialog mit Bürger*innen und Entscheidungsträger*innen zu treten, ist das Ausstellungsstück „IN-ML-OUT“ entstanden. Dieses ist aus einer Kooperation unserer Forscherinnen Nicole Ludwig und Nina Effenberger mit Designstudierenden der "Staatlichen Akademie der Bildenden Künste" in Stuttgart und dem "Zentrum für rhetorische Wissenschaftskommunikationsforschung zur Künstlichen Intelligenz" (RHET AI) an der Universität Tübingen hervorgegangen.

Weitere Informationen

Workshop "Explainability in Machine Learning"

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  Dienstag 28. März und Mittwoch 29. März 2023

Veranstaltungsort: Alte Aula, Münzgasse 30, 72070 Tübingen

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

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Deep Learning hat große Fortschritte beim maschinellen Lernen ermöglicht. Der Einsatz von Deep-Learning-Systemen, die in sicherheitskritischen Bereichen eingesetzt werden oder die Auswirkungen auf die Zivilgesellschaft haben, setzt allerdings voraus, dass ihre Entscheidungen erklärbar sind. Für den Aufbau vertrauenswürdiger und nutzerorientierter maschineller Lernmodelle ist dies von grundlegender Bedeutung. Ziel dieses Workshops ist es, das Bewusstsein für die Erklärbarkeit von maschinellem Lernen zu fördern, da es ein Thema von wachsendem Interesse ist. Darüber hinaus wollen wir, insbesondere im Kontext des maschinellen Sehens, die interdisziplinäre Interaktion und Zusammenarbeit zwischen Forschenden an der Universität Tübingen und an anderen internationalen Institutionen fördern, die an verschiedenen Aspekten der Erklärbarkeit arbeiten.

  Programm: Der Workshop umfasst sowohl Keynotes von bekannten Forschern auf diesem Gebiet als auch eingeladene Vorträge und Spotlight-Präsentationen über die jüngsten Fortschritte auf dem Gebiet der Erklärbarkeit.

► Vorläufiges Programm und weitere Informationen: https://www.eml-unitue.de/eml-workshop

Spring School on Probabilistic Numerics

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Montag, 27. bis Mittwoch, 29. März 2023

Veranstaltungsort: 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.

Frühjahrstagung der Deutschen Physikalischen Gesellschaft (DPG), Sektion Kondensierte Materie

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Montag, 27. März 2023

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

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Vom 26. März bis 31. März 2023 findet auf dem Campus der Technischen Universität Dresden die DPG-Frühjahrstagung der Sektion Kondensierte Materie (SKM) statt.

In diesem Rahmen findet am 27. März 2023 eine Focus Session "Physics meets ML" statt, organisiert von Sabine Andergassen und Moritz Helas, Martin Gärttner und Markus Schmitt. Das dazugehörige Tutorial findet am 26. März 2023 statt.  

PROGRAMM
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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Mittwoch, 15. Februar 2023 | 13:00 bis 16:00

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

Organisatoren: Tobias Kaufmann, Jakob Macke

Anmeldung: Dies ist ein interner Workshop des Clusters.
Bei Interesse bitten wir um Anmeldung (E-Mail an Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de).

Programm [PDF]

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Workshop "Machine Learning meets Linguistics"

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Mittwoch, 08. Februar 2023 | 08:25 bis 16:00

Veranstaltungsort: AI Research Building, Hörsaal (Erdgeschoss), Maria-von-Linden-Straße 6

Organisatoren: Dominik Papies, Augustin Kelava

Anmeldung: Dies ist ein interner Workshop des Clusters. Bei Interesse bitten wir um Anmeldung (E-Mail an Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de) bis zum 3. Februar 2023.

Programm [PDF]

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Workshop "Machine Learning meets Physics"

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Mittwoch, 01. Februar 2023 | 13:00 bis 17:00

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

Organisatoren: Martin Oettel, Frank Schreiber

Anmeldung: Dies ist ein interner Workshop des Clusters.
Bei Interesse bitten wir um Anmeldung (E-Mail an Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de).

Programm [PDF]

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Workshop "Machine Learning meets Machine Learning“

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Mittwoch, 01. Februar 2023 | 11:00 bis 14:00

Veranstaltungsort: AI Research Building, Hörsaal (Erdgeschoss), Maria-von-Linden-Straße 6

Organisatoren: Philipp Hennig, Matthias Hein

Anmeldung: Dies ist ein interner Workshop des Clusters.
Bei Interesse bitten wir um Anmeldung (E-Mail an Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de) bis zum 27. Januar 2023.

Programm [PDF]

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Workshop "Machine Learning meets Qualitative Social Sciences"

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Mittwoch, 25. Januar 2023 | 09:00 bis 13:30

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

Organisatoren: Christoph Bareither, Thomas Thiemeyer

Anmeldung: Dies ist ein interner Workshop des Clusters.
Bei Interesse bitten wir um Anmeldung (E-Mail an Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de).

Programm [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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Mittwoch, 25. Januar 2023 | 14:00 bis 17:00

Veranstaltungsort: AI Research Building, Hörsaal (Erdgeschoss), Maria-von-Linden-Straße 6

Organisatoren: Thomas Scholten, Todd Ehlers

Anmeldung: Dies ist ein interner Workshop des Clusters.
Bei Interesse bitten wir um Anmeldung (E-Mail an Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de) bis 20. Januar 2023.

Programm [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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Mittwoch, 18. Januar 2023 | 13:00 bis 16:00

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

Organisatoren: Manfred Claassen, Sergios Gatidis

Anmeldung: Dies ist ein interner Workshop des Clusters.
Bei Interesse bitten wir um Anmeldung (E-Mail an Sebastian Schwenk sebastian.schwenkspam prevention@uni-tuebingen.de)

Programm [PDF]

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Workshop "Machine Learning meets Linguistics"

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Mittwoch, 11. Januar 2023 | 14:00 bis 18:00

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

Organisatoren: Michael Franke, Detmar Meurers

Anmeldung: Dies ist ein interner Workshop des Clusters.
Bei Interesse bitten wir um Anmeldung (E-Mail an 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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20. - 22. Oktober 2022

Hörsaal, MPI-IS, Max-Planck-Ring 6, 72076 Tübingen
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Mehr Informationen über die Veranstaltung sowie das Programm sind hier zu finden.

4. Exzellenzcluster Konferenz
"Machine Learning in Science" 2022
 

► Dienstag 12. Juli | 9:00 - 18:00 |

► Mittwoch 13. Juli | 9:00 - 17:00 |

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Tagungsort: Westspitze, Eisenbahnstraße 1, 72072 Tübingen

Für alle, die nicht persönlich an der Konferenz teilnehmen können, die Präsentationen werden übertragen auf
Zoom

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PROGRAMM

Alle Zeiten sind in MESZ, Mitteleuropäische Sommerzeit, angegeben.

Programm [PDF]

Dienstag, 12. Juli 2022

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Mittwoch, 13. Juli 2022

Science and Innovation Days

Vom 29. Juni bis zum 2. Juli 2022 öffnen die großen Tübinger Forschungseinrichtungen ihre Türen und präsentieren interessierten Bürgerinnen und Bürgern ihre Forschung. Die Science and Innovation Days finden über die gesamte Stadt verteilt statt, das Programm ist hier zu finden. Die Veranstaltungen sind auf Deutsch.

Die Mitglieder unseres Clusters beteiligen sich mit folgenden Beiträgen:
 

Mittwoch, 29. Juni 2022

Freitag, 1. Juli 2022

Samstag, 2. Juli 2022

PhD Workshop "Artificial Intelligence and Machine Learning Research and Democracy" mit öffentlicher Abschlussveranstaltung

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       2. - 4. April 2022 an der Universität 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“ am 4. April um 18:30

Zum Abschluss des Workshops findet am 4. April ab 18:30 Uhr eine öffentliche Diskussionsrunde zu "KI und Demokratie" in der "Westspitze" in Tübingen statt.
Zu dieser Veranstaltung sind alle Interessierten herzlich eingeladen.
Anmeldung über folgenden Link: ai-and-democracy-workshop.de/podium

Workshop "Introduction to Machine Learning"

Das Machine Learning ⇌ Science Colaboratory lädt zu einem praxisorientierten Präsenz-Workshop über die Einführung in das Maschinelle Lernen ein.

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Mittwoch, 30. März 2022, 9:00 bis 13:00

Die Veranstaltung findet vor Ort statt in der Maria-von-Linden-str. 6, 72076 Tübingen, Hörsaal (Erdgeschoss, Raum 00-28/A7)

Anmeldung:
Hier geht es zum  Anmeldeformular
Bitte beachten, dass die Teilnehmerzahl auf 12 Personen begrenzt ist! Teilnehmer werden etwa eine Woche vor der Veranstaltung von uns benachrichtigt.

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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"
 

  Der Workshop findet in Präsenz in Tübingen statt

      am 09.11. + 10.11. in der Alten Aula (Münzgasse 30)
      am 11.11. + 12.11. im Max Planck Institute for Intelligent Systems (Max-Planck-Ring 4)

Der Workshop wird organisiert von der Arbeitsgruppe 'Ethik und Philosophie der Künstlichen Intelligenz' des Exzellenzclusters 'Machine Learning: Neue Perspektiven für die Wissenschaft' an der Universität Tübingen (Workshop-Veranstalter: Thomas Grote, Thilo Hagendorff, Eric Raidl).

Anmeldung: Die Plätze sind begrenzt. Gäste melden sich bitte an über: 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.

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 Programm

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
 

WANN:  Donnerstag, 30.09.2021 um 9:00
WO:       Hybrid-Veranstaltung:
Claire hält den Vortrag im Hörsaal der Maria-von-Linden-Straße 6 (EG). Aufgrund der aktuellen Covid-Beschränkungen sind die Plätze begrenzt. Cluster-PIs können vor Ort im Hörsaal teilnehmen - bei Interesse bitte bei Elena Sizana anmelden. Es gilt die 3G-Regel (vollständig geimpft, getestet oder genesen).
Alle anderen laden wir ein, über Zoom teilzunehmen: 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"

Der Workshop findet virtuell auf Zoom statt.

Der Workshop wird gemeinsam organisiert vom ML⇌Science Colaboratory, der Machine Learning in Science Arbeitsgruppe (Jakob Macke) und Helmholtz AI.

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Anmeldung:
Deadline für die Anmeldung ist der 31. August, hier geht es zum Anmeldeformular

Please note, 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:

| Programm

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

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  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

Vortrag von Reinhard Diestel, Universität Hamburg, Fachbereich Mathematik

WANN: Freitag, 24.01.2020 um 10:00
WO: Hörsaal am MPI for Intelligente Systeme, Erdgeschoss
 

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.

Exzellenzcluster Konferenz "Machine Learning in Science"

Montag, 22.07.2019 | 09:00 Uhr - 19:00 Uhr | Alte Aula

Dienstag, 23.07.2019 | 09:00 Uhr - 16:00 Uhr | Pfleghofsaal

Anmeldung: Bitte melden Sie sich bis spätestens 15.07.2019 verbindlich zu der Tagung an, E-Mail an Sebastian Schwenk. Bitte geben sie an, an welchen Tagen Sie teilnehmen möchten.

Programm

Montag, 22.07.2019

Alte Aula, Münzgasse 30, 72070 Tübingen

Dienstag, 23.07.2019

Pfleghofsaal, Schulberg 2 (Pfleghof), 72070 Tübingen

Machine Learning meets Physics

AI Gebäude, Hörsaal (EG)

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: Kaffeepause

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: Diskussion

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
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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
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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