UAI 2022 - Keynote Speakers


UAI 2022 is pleased to announce the following invited speakers:

Danilo J. Rezende,  DeepMind

Eric P. Xing,  Carnegie Mellon University & Mohamed bin Zayed University of Artificial Intelligence

Finale Doshi-Velez,  Harvard University

Mihaela van der Schaar,  University of Cambridge

Peter Spirtes,  Carnegie Mellon University

Zeynep Akata,  University of Tübingen


Danilo J. Rezende

DeepMind

Biographical details

Danilo J. Rezende is a Senior Staff Researcher and lead of the Generative Models and Inference group at DeepMind, London. For the last 12 years his research has focused on scalable inference and generative models applied to reinforcement learning, modelling of complex data such as medical images, videos, 3D scene geometry and complex physical systems. He has co-authored >90 papers and patents, amongst which a few highly-cited papers on approximate inference and modelling with neural networks (such as Deep Latent Gaussian models, Normalizing Flows and Interaction Networks). Highlights of his recent work at the intersection of AI and physics include equivariant normalizing flows for lattice-QCD and molecular dynamics. Danilo is engaged in promoting the alliance between ML/AI, Physics and Geometry. He holds a BA in Physics and an MSc in Theoretical Physics from Ecole Polytechnique (Palaiseau, France) and the Institute of Theoretical Physics (SP, Brazil). Once an aspiring PhD in theoretical physics at the Centre de Physique Théorique in Marseille, France he switched to a PhD in Computational Neuroscience at Ecole Polytechnique Federale de Lausanne (Lausanne, Switzerland), where he studied computational/statistical models of learning and sensory fusion.


Eric P. Xing

Carnegie Mellon University & Mohamed bin Zayed University of Artificial Intelligence

Biographical details

Eric P. Xing is a Professor of Computer Science at Carnegie Mellon University, and the Founder, CEO, and Chief Scientist of Petuum Inc., a 2018 World Economic Forum Technology Pioneer company that builds standardized artificial intelligence development platform and operating system for broad and general industrial AI applications. He completed his undergraduate study at Tsinghua University, and holds a PhD in Molecular Biology and Biochemistry from the State University of New Jersey, and a PhD in Computer Science from the University of California, Berkeley. His main research interests are the development of machine learning and statistical methodology, and large-scale computational system and architectures, for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in artificial, biological, and social systems. Prof. Xing currently serves or has served the following roles: associate editor of the Journal of the American Statistical Association (JASA), Annals of Applied Statistics (AOAS), IEEE Journal of Pattern Analysis and Machine Intelligence (PAMI) and the PLoS Journal of Computational Biology; action editor of the Machine Learning Journal (MLJ) and Journal of Machine Learning Research (JMLR); member of the United States Department of Defense Advanced Research Projects Agency (DARPA) Information Science and Technology (ISAT) advisory group. He is a recipient of the National Science Foundation (NSF) Career Award, the Alfred P. Sloan Research Fellowship in Computer Science, the United States Air Force Office of Scientific Research Young Investigator Award, the IBM Open Collaborative Research Faculty Award, as well as several best paper awards. Prof Xing is a board member of the International Machine Learning Society; he has served as the Program Chair (2014) and General Chair (2019) of the International Conference of Machine Learning (ICML); he is also the Associate Department Head of the Machine Learning Department, founding director of the Center for Machine Learning and Health at Carnegie Mellon University; he is a Fellow of the Association of Advancement of Artificial Intelligence (AAAI), and an IEEE Fellow.

According to csrankings.org, Professor Xing currently ranks among top computer science (CS) professors worldwide, whose papers (in the most selective CS conferences of all disciplines) collectively have the highest (weighted) acceptance among all computer scientists in the US and global universities during 2010-2020.


Finale Doshi-Velez

Harvard University

Biographical details

Finale Doshi-Velez is a Gordon McKay Professor in Computer Science at the Harvard Paulson School of Engineering and Applied Sciences. She completed her MSc from the University of Cambridge as a Marshall Scholar, her PhD from MIT, and her postdoc at Harvard Medical School. Her interests lie at the intersection of machine learning, healthcare, and interpretability.

Selected Additional Shinies: BECA recipient, AFOSR YIP and NSF CAREER recipient; Sloan Fellow; IEEE AI Top 10 to Watch


Mihaela van der Schaar

University of Cambridge

Biographical details

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute in London. In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM).

Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

Mihaela is personally credited as inventor on 35 USA patents (the majority of which are listed here), many of which are still frequently cited and adopted in standards. She has made over 45 contributions to international standards for which she received 3 ISO Awards. In 2019, a Nesta report determined that Mihaela was the most-cited female AI researcher in the U.K.


Peter Spirtes

Carnegie Mellon University

Biographical details

Peter Spirtes is the Marianna Brown Dietrich Professor and Head of the Department of Philosophy at Carnegie Mellon University. His research interests are interdisciplinary in nature, involving philosophy, statistics, graph theory, and computer science. His research has implications for the practices of a number of disciplines in which causal inferences from statistical data are made. Together with Prof. Clark Glymour, He has published one of the first algorithms for causal learning from observational data, called PC; as well as one of the widely used reference books in the field (Spirtes, Glymour, Scheines,2000). His work has shown that there are computer programs that can in some circumstances reliably draw useful causal conclusions under a reasonable set of assumptions from experimental or non-experimental data, or combinations of both. His current research centers on the extent to which these limiting assumptions can be relaxed, thereby extending the application of the results to a much wider class of phenomena and investigating the extent to which these search procedures scaled up to work with larger numbers of variables. This research program has important theoretical and practical implications for a number of different disciplines, including biology. Theoretically, it has helped us understand the relationship between probability and causality, and what the precise limits of reliable causal inference from various kinds of data under a variety of different assumptions are. Practically, it has provided a useful tool for scientists that helps them build causal models.


Zeynep Akata

University of Tübingen

Biographical details

Zeynep Akata is a professor of Computer Science (W3) within the Cluster of Excellence Machine Learning at the University of Tübingen. After completing her PhD at the INRIA Rhone Alpes with Prof Cordelia Schmid (2014), she worked as a post-doctoral researcher at the Max Planck Institute for Informatics with Prof Bernt Schiele (2014-17) and at University of California Berkeley with Prof Trevor Darrell (2016-17). Before moving to Tübingen in October 2019, she was an assistant professor at the University of Amsterdam with Prof Max Welling (2017-19). She received a Lise-Meitner Award for Excellent Women in Computer Science from Max Planck Society in 2014, a young scientist honour from the Werner-von-Siemens-Ring foundation in 2019 and an ERC-2019 Starting Grant from the European Commission. Her research interests include multimodal learning and explainable AI.





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