Speakers and Talks

Aaditya Ramdas (PhD, 2015) is an assistant professor at Carnegie Mellon University, in the Departments of Statistics and Machine Learning. He was a postdoc at UC Berkeley (2015–2018) mentored by Michael Jordan and Martin Wainwright, and obtained his PhD at CMU (2010–2015) under Aarti Singh and Larry Wasserman, receiving the Umesh K. Gavaskar Memorial Thesis Award.

Aaditya received the IMS Peter Gavin Hall Early Career Prize (2023), was an inaugural recipient of the COPSS Emerging Leader Award (2021), and a recipient of the Bernoulli New Researcher Award (2021). His work is supported by an NSF CAREER Award, an Adobe Faculty Research Award (2019), a Google Research Scholar award (2022), amongst others. He was a CUSO lecturer in 2022 and will be a Lunteren lecturer in 2023.

Arthur Gretton is a Professor with the Gatsby Computational Neuroscience Unit; director of the Centre for Computational Statistics and Machine Learning (CSML) at UCL; and Research Scientist at Google Deepmind. He received degrees in Physics and Systems Engineering from the Australian National University, and a PhD with Microsoft Research and the Signal Processing and Communications Laboratory at the University of Cambridge. He previously worked at the MPI for Biological Cybernetics, and at the Machine Learning Department, Carnegie Mellon University.

Arthur's recent research interests in machine learning include causal inference and representation learning, design and training of generative models (implicit: Wasserstein gradient flows, GANs; and explicit: energy-based models), and nonparametric hypothesis testing.

Chris Russel is a Research Scientist at Amazon. Chris Russell is also a research affiliate at Oxford Internet Institute and a member of the Governance of Emerging Technology programme since 2019,  where he works primarily on algorithmic fairness and Explainable AI. His work on explainability (with Sandra Wachter and Brent Mittelstadt of the OII) is cited in the guidelines to the GDPR and forms part of the TensorFlow “What-if tool”. He was one of the first researchers to propose the use of causal models in reasoning about fairness (namely Counterfactual Fairness), and continues to work extensively on computer vision. Prior to this, he was a Group Leader in Safe and Ethical AI at the Alan Turing Institute, and a Reader in Computer Vision and Machine Learning at the University of Surrey.

Chris Williams did Physics at Cambridge, graduating in 1982 (BA in Physics and Theoretical Physics, Class I), and then did a further year of study known as Part III Maths (Distinction, 1983). Chris was interested in the "neural networks" field at that time, but it was difficult to find anyone to work with. Chris then switched directions for a while, doing an MSc in Water Resources at the University of Newcastle upon Tyne and going on to work in Lesotho, Southern Africa in low-cost sanitation. In 1988 Chris returned to academia, studing neural networks/AI with Geoff Hinton at the University of Toronto (MSc 1990, PhD 1994). In September 1994 Chris moved to Aston University as a Research Fellow and was made a Lecturer in August 1995. Chris moved to the Department of Artificial Inteligence at the University of Edinburgh in July 1998, was promoted to Reader in the School of Informatics in October 2000, and Professor of Machine Learning in October 2005. Chris was Director of the Institute for Adaptive and Neural Computation (ANC) 2005-2012, founding Director of the CDT in Data Science 2013-2016, University Liaison Director for UoE to the Alan Turing Institute (2016-2018), and Director of Research for the School of InformatIcs (2018-2021).

Chris was elected a Fellow of the Royal Society of Edinburgh in 2021. Chris is a Fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS), a Turing Fellow at the Alan Turing Institute (UK), and was program co-chair of the NeurIPS conference in 2009.

Emmanuel Candès is the Barnum-Simons Chair in Mathematics and Statistics, a professor of electrical engineering (by courtesy) and a member of the Institute of Computational and Mathematical Engineering at Stanford University. Earlier, Candès was the Ronald and Maxine Linde Professor of Applied and Computational Mathematics at the California Institute of Technology. His research interests are in computational harmonic analysis, statistics, information theory, signal processing and mathematical optimization with applications to the imaging sciences, scientific computing and inverse problems. He received his Ph.D. in statistics from Stanford University in 1998.

Candès has received several awards including the Alan T. Waterman Award from NSF, which is the highest honor bestowed by the National Science Foundation, and which recognizes the achievements of early-career scientists. He has given over 60 plenary lectures at major international conferences, not only in mathematics and statistics but in many other areas as well including biomedical imaging and solid-state physics. He was elected to the National Academy of Sciences and to the American Academy of Arts and Sciences in 2014.

Eric Nalisnick is an assistant professor at the University of Amsterdam. He is also an ELLIS scholar and NWO Veni fellow. His research interests span statistical machine learning and probabilistic modeling, with an emphasis on human-in-the-loop learning, specifying prior knowledge, detecting distribution shift, and quantifying uncertainty in deep learning. He previously was a postdoctoral researcher at the University of Cambridge and a PhD student at the University of California, Irvine. Eric has also held research positions at DeepMind, Microsoft, Twitter, and Amazon.

Isabel Valera is a full Professor at the Department of Computer Science of Saarland University, Saarbrücken (Germany) and an independent research group leader at the MPI for Intelligent Systems in Tübingen (Germany). Prior to this, she worked as postdoctoral researcher at the MPI for Software Systems (Germany) and at the University of Cambridge (UK), and obtained her PhD and MSc degrees from the University Carlos III in Madrid (Spain).  She has held a German Humboldt Post-Doctoral Fellowship, and a “Minerva fast track” fellowship from the Max Planck Society.  She is regularly Area Chair for the main conferences on machine learning (NeurIPS, ICML, AISTAST, AAAI and ICLR) and has been Program Co-Chair of ECML-PKDD 2020. 

Kevin Murphy got his BA from U. Cambridge, his MEng from U. Pennsylvania, and his PhD from UC Berkeley. He then did a postdoc at MIT, and was an associate professor of computer science and statistics at the University of British Columbia in Vancouver, Canada, from 2004 to 2012. After getting tenure, he went to Google in California on his sabbatical and then ended up staying. He currently runs a team of 6  researchers inside of Google Brain; the team works on generative models, Bayesian inference, and various other topics. 

Kevin has published over 125 papers in refereed conferences and journals, as well 3 textbooks on machine learning published in 2012, 2022 and 2023 by MIT Press. (The 2012 book was awarded the  DeGroot Prize for best book in the field of Statistical Science.) Kevin was also the (co) Editor-in-Chief of JMLR 2014--2017.

Maneesh Sahani is Professor of Theoretical Neuroscience and Machine Learning at the Gatsby Computational Neuroscience Unit at University College London (UCL). Graduating with a B.S. in physics from Caltech, he stayed to earn his Ph.D. in the Computation and Neural Systems program, supervised by Richard Andersen and John Hopfield. After periods of postdoctoral work at the Gatsby Unit and the University of California, San Francisco, he returned to the faculty at Gatsby in 2004 and was elected to a personal chair at UCL in 2013.

His work spans the interface of the fields of machine learning and neuroscience, with particular emphasis on the types of computation achieved within the sensory and motor cortical systems. He has helped to pioneer analytic methods which seek to characterize and visualize the dynamical computational processes that underlie the measured joint activity of populations of neurons. He has also worked on the link between the statistics of the environment and neural computation, machine-learning based signal processing, and neural implementations of Bayesian and approximate inference.