Keynote Speakers

Prof. Dr. Max Welling is a research chair in Machine Learning at the University of Amsterdam and a Distinguished Scientist at MSR. He is a fellow at the Canadian Institute for Advanced Research (CIFAR) and the European Lab for Learning and Intelligent Systems (ELLIS) where he also serves on the founding board. His previous appointments include VP at Qualcomm Technologies, professor at UC Irvine, postdoc at U. Toronto and UCL under supervision of prof. Geoffrey Hinton, and postdoc at Caltech under supervision of prof. Pietro Perona. He finished his PhD in theoretical high energy physics under supervision of Nobel laureate prof. Gerard ‘t Hooft.

Max Welling has served as associate editor in chief of IEEE TPAMI from 2011-2015, he serves on the advisory board of the Neurips foundation since 2015 and has been program chair and general chair of Neurips in 2013 and 2014 respectively. He was also program chair of AISTATS in 2009 and ECCV in 2016 and general chair of MIDL 2018. Max Welling is recipient of the ECCV Koenderink Prize in 2010 and the ICML Test of Time award in 2021. He directs the Amsterdam Machine Learning Lab (AMLAB) and co-directs the Qualcomm-UvA deep learning lab (QUVA) and the Bosch-UvA Deep Learning lab (DELTA).

Good neural architectures are rooted in good inductive biases (a.k.a. priors). Equivariance under symmetries is a prime example of a successful physics inspired prior which sometimes dramatically reduces the number of examples needed to learn predictive models. In this work we will try to extend this thinking to more flexible priors in the hidden variables of a neural network. In particular, we will impose wavelike dynamics in hidden variables under transformations of the inputs, which relaxes the stricter notion of equivariance. We find that under certain conditions, wavelike dynamics naturally arises in these hidden representations. We formalize this idea in a VAE-over-time architecture where the hidden dynamics is described by a Fokker-Planck (a.k.a. drift-diffusion) equation. This in turn leads to a new definition of a disentangled hidden representation of input states that can easily be manipulated to undergo transformations. If time allows, I will discuss very preliminary work on how the Schrodinger equation can also be used to move information in the hidden representations. 

Joint work with Andy T. Keller and Yue Song.

Michael Bronstein is the DeepMind Professor of AI at the University of Oxford and Head of Graph Learning Research at Twitter. He was previously a professor at Imperial College London and held visiting appointments at Stanford, MIT, and Harvard, and has also been affiliated with three Institutes for Advanced Study (at TUM as a Rudolf Diesel Fellow (2017-2019), at Harvard as a Radcliffe fellow (2017-2018), and at Princeton as a short-time scholar (2020)). Michael received his PhD from the Technion in 2007. He is the recipient of the Royal Society Wolfson Research Merit Award, Royal Academy of Engineering Silver Medal, five ERC grants, two Google Faculty Research Awards, and two Amazon AWS ML Research Awards. He is a Member of the Academia Europaea, Fellow of IEEE, IAPR, BCS, and ELLIS, ACM Distinguished Speaker, and World Economic Forum Young Scientist. In addition to his academic career, Michael is a serial entrepreneur and founder of multiple startup companies, including Novafora, Invision (acquired by Intel in 2012), Videocites, and Fabula AI (acquired by Twitter in 2019). 

The message-passing paradigm has been the “battle horse” of deep learning on graphs for several years, making graph neural networks a big success in a wide range of applications, from particle physics to protein design. From a theoretical viewpoint, it established the link to the Weisfeiler-Lehman hierarchy, allowing to analyse the expressive power of GNNs. We argue that the very “node-and-edge”-centric mindset of current graph deep learning schemes may hinder future progress in the field. As an alternative, we propose physics-inspired “continuous” learning models that open up a new trove of tools from the fields of differential geometry, algebraic topology, and differential equations so far largely unexplored in graph ML.

Professor Kate Crawford is a leading international scholar of the social implications of artificial intelligence. She is a Research Professor at USC Annenberg in Los Angeles, a Senior Principal Researcher at MSR in New York, an Honorary Professor at the University of Sydney, and the inaugural Visiting Chair for AI and Justice at the École Normale Supérieure in Paris.  Her latest book,  Atlas of AI (Yale, 2021) won the Sally Hacker Prize from the Society for the History of Technology, the ASSI&T Best Information Science Book Award, and was named one of the best books in 2021 by New Scientist and the Financial Times. Over her twenty-year research career, she has also produced groundbreaking creative collaborations and visual investigations. Her project Anatomy of an AI System with Vladan Joler is in the permanent collection of the Museum of Modern Art in New York and the V&A in London, and was awarded with the Design of the Year Award in 2019 and included in the Design of the Decades by the Design Museum of London. Her collaboration with the artist Trevor Paglen, Excavating AI, won the Ayrton Prize from the British Society for the History of Science. She has advised policy makers in the United Nations, the White House, and the European Parliament, and she currently leads the Knowing Machines Project, an international research collaboration that investigates the foundations of machine learning. 

Training data is foundational to generative AI systems. From Common Crawl’s 3.1 billion web pages to LAION-5B’s corpus of almost 6 billion image-text pairs, these vast collections – scraped from the internet and treated as “ground truth” – play a critical role in shaping the epistemic boundaries that govern generative AI models. Yet training data is beset with complex social, political, and epistemological challenges. What happens when data is stripped of context, meaning, and provenance? How does training data limit what and how machine learning systems interpret the world? What are the copyright implications of these datasets? And most importantly, what forms of power do these approaches enhance and enable? This keynote is an invitation to reflect on the epistemic foundations of generative AI, and to consider the wide-ranging impacts of the current generative turn.