Pierre Alquier is a research scientist at RIKEN AIP in Tokyo. He received his PhD degree from Université Paris 6 in 2006, and worked as a lecturer in Université Paris Diderot and later in UCD Dublin. He was a professor of statistics and ENSAE Paris from 2014 to 2019. He works on statistical learning theory, with an emphasis on the theory of Bayesian methods and aggregation of estimators. His more recent papers study the statistical properties of variational inference.

Full professor (W3) for Intelligent Autonomous Systems at the Computer Science Department of the Technische Universitaet Darmstadt and at the same time a senior research scientist and group leader at the Max-Planck Institute for Intelligent Systems, where he heads the interdepartmental Robot Learning Group. Jan Peters has received the Dick Volz Best 2007 US PhD Thesis Runner-Up Award, the Robotics: Science & Systems - Early Career Spotlight, the INNS Young Investigator Award, and the IEEE Robotics & Automation Society's Early Career Award as well as numerous best paper awards. In 2015, he received an ERC Starting Grant and in 2019, he was appointed as an IEEE Fellow.

Associate Professor in Department of Philosophy and an affiliated faculty member in the Machine Learning Department at Carnegie Mellon University. His research interests lie in machine learning and artificial intelligence, especially in causal discovery and causality-based learning. He develops methods for automated causal discovery from various kinds of data and investigate learning problems including transfer learning, concept learning, and deep learning from a causal view. On the application side, he is interested in neuroscience, computer vision, computational finance, and climate analysis. He has published more than 100 papers on causality, machine learning, and artificial intelligence. He coauthors a best student paper for UAI and a best finalist paper for CVPR, and received the best benchmark award of the causality challenge, and has served as an area chair or senior program committee member for major conferences in machine learning or artificial intelligence, including NeurIPS, UAI, ICML, AISTATS, AAAI, and IJCAI. He has organized various academic activities to foster interdisciplinary research in causality

Assistant professor at Carnegie Mellon University appointed in both the Machine Learning Department and Tepper School of Business. His research spans core machine learning methods and their social impact and addresses diverse application areas, including clinical medicine and natural language processing. Current research focuses include robustness under distribution shift, breast cancer screening, the effective and equitable allocation of organs, and the intersection of causal thinking with messy data. He is the founder of the Approximately Correct (approximatelycorrect.com) blog and the creator of Dive Into Deep Learning, an interactive open-source book drafted entirely through Jupyter notebooks. Find on Twitter (@zacharylipton) or GitHub (@zackchase).

Associate professor of computer science and data science at New York University, a research scientist at Facebook AI Research and a CIFAR Associate Fellow. He was a postdoctoral fellow at the University of Montreal until summer 2015 under the supervision of Prof. Yoshua Bengio, and received PhD and MSc degrees from Aalto University early 2014 under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so.

Machine learning (ML) researcher with a focus on reinforcement learning (RL). MDPs and their generalizations (POMDPs, games) are my main modeling tools and I am interested in improving algorithms for solving them. In particular, I believe that finding the right ways to quantify uncertainty in complex deep RL models is one of the most promising approaches to improving sample-efficiency. I am also interested in meta learning and imitation learning. An important part of my research is motivated by applying RL to computer games.

Research scientist at Facebook AI Research and a full professor in the School of Computer Science at Tel-Aviv University, Israel. He conducted postdoctoral research at prof. Poggio's lab at the Massachusetts Institute of Technology and received his PhD degree from the Hebrew University, under the supervision of Prof. Shashua.
He is an ERC grantee and has won the ICCV 2001 and ICCV 2019 honorable mention, and the best paper awards at ECCV 2000 and ICANN 2016. His research focuses on computer vision and deep learning.

Dimitrios Pantazis, who joined the McGovern Institute in 2010, oversees the operation of the Magnetoencephalography (MEG) Laboratory within the Martinos Imaging Center at MIT. Before moving to MIT, he was research assistant professor at the University of Southern California from 2008-2010. His research focuses on the development of novel MEG methods to holistically capture spatiotemporal brain activation and the study of visual brain representations. He has over 15 years of experience in developing methods for the analysis of MEG data and has published prominent articles in Nature Neuroscience, Proceedings of the National Academy of Sciences, Scientific Reports, Cerebral Cortex, and NeuroImage. His work has been featured in Science News; Scientific American Mind Magazine; Boston Magazine; MIT Technology Review; Elekta's Wavelength Magazine; MIT News; the front-page of the MIT website and several Greek media outlets.

Interested in developing flexible, interpretable, and scalable machine learning models, often involving deep learning, Gaussian processes, and kernel learning. I care about developing practically impactful methods, while at the same understanding why the methods work, and the foundations for building models that learn and generalize. I am particularly excited about loss surfaces, generalization, probabilistic generative models, and Bayesian methods in deep learning. My work has been applied to time series, vision, NLP, spatial statistics, public policy, medicine, and physics

Susan Athey is the Economics of Technology Professor at Stanford Graduate School of Business. She received her bachelor's degree from Duke University and her Ph.D. from Stanford, and she holds an honorary doctorate from Duke University. She previously taught at the economics departments at MIT, Stanford, and Harvard. Her current research focuses on the economics of digitization, marketplace design, and the intersection of econometrics and machine learning.

Suchi Saria directs the Machine Learning and Healthcare Lab at Johns Hopkins University and is the founding research director of the Malone Center for Engineering in Healthcare. She is interested in enabling new classes of diagnostic and treatment planning techniques for healthcare—tools that use statistical machine learning techniques to tease out subtle information from "messy" observational datasets and provide reliable inferences for individualizing care decisions.

Ruth Urner is an assistant professor at York University in Toronto, Canada. She is also a faculty affiliate at Toronto's Vector Institute. Previous to that she was a senior research scientist at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, and a postdoctoral fellow at Carnegie Mellon's Machine Learning department as well as at Georgia Tech. She received her PhD from the University of Waterloo for a thesis on statistical learning theory in 2013. She regularly serves as a senior program committee member of the major machine learning conferences, such as NeurIPS, ICML, AISTATS and COLT. Her research develops mathematical tools and frameworks for analyzing the possibilities and limitations of automated learning, with a focus on semi-supervised, active and transfer learning. Currently she is particularly interested in developing formal foundations for topics relating to societal impacts of machine learning, such as human interpretability and algorithmic fairness.

Tamara Broderick is an Associate Professor in the Department of Electrical Engineering and Computer Science at MIT. She is a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), the MIT Statistics and Data Science Center, and the Institute for Data, Systems, and Society (IDSS). She completed her Ph.D. in Statistics at the University of California, Berkeley in 2014. Her recent research has focused on developing and analyzing models for scalable Bayesian machine learning.

Sinead is an Assistant Professor of Statistics at the University of Texas at Austin, in the IROM Department and the Division of Statistics and Scientific Computation. She received her MEng from the University of Oxford, MSc from University College London, and PhD from the University of Cambridge. Her main research areas are Bayesian nonparametric statistics and machine learning. Before joining the faculty at UT Austin, Sinead was a Post Doc at Carnegie Mellon University.

Emtiyaz is a team leader at the RIKEN center for Advanced Intelligence Project (AIP) in Tokyo where he leads the Approximate Bayesian Inference (ABI) Team and a visiting professor at the EE department in Tokyo University of Agriculture and Technology (TUAT). He works on problems in several areas of machine learning, such as approximate inference, deep learning, reinforcement learning, active learning, online learning, and reasoning in computer vision.

Katharina Kann is an Assistant Professor of Computer Science at University of Colorado Boulder. Her research focus on developing models for languages besides English, including low-resource languages. She won SIGMORPHON 2016 shared task on morphological reinflection and task 2 of CoNLL-SIGMORPHON 2017 shared task on universal morphological reinflection. Since 2016, she has published more than 30 papers in top NLP conferences and journals

Anna Goldenberg is an associate professor at University of Toronto's Department of Computer Science and the Department of Statistics, and the Associate Research Director for health at the Vector Institute for Artificial Intelligence. Her main research focus is to develop machine learning methods that can help to decipher human disease heterogeneity. This involves combining data from multiple heterogeneous sources while addressing missing data and noise, simultaneous subtyping and feature selection in very sparse settings and more.

Since 2007 Evgeny Burnaev carried out a number of successful industrial projects with Airbus, SAFT, IHI, and Sahara Force India Formula 1 team among others. According to Airbus experts, application of the methods, developed by his team, "provides the reduction of up to 10% of lead time and cost in several areas of the aircraft design process". Since 2016 Evgeny Burnaev works as Associate Professor of Skoltech and head of the Advanced Data Analytics in Science and Engineering (ADASE) group. The main research interests are related to development of new deep learning architectures with applications in 3D computer vision and predictive analytics. For his scientific achievements in the year 2017 Evgeny Burnaev was honored with the Moscow Government Prize for Young Scientists in the category for the Transmission, Storage, Processing and Protection of Information for leading the project "The development of methods for predictive analytics for processing industrial, biomedical and financial data."

Maria is a PostDoc in the Quantum Research Group at the University of KwaZulu-Natal. She received her Ph.D. in Physics at the University of KwaZulu-Natal and an MSc degree in Physics from the Technical University of Berlin. She holds a German "Diplom" degree in political science from the Free University of Berlin. Maria's scientific research is driven by how quantum information processing can improve and extend methods in machine learning: How can we efficiently solve pattern recognition tasks on a quantum computer? What machine learning models can we develop from the working principles of quantum devices? Are there applications for near-term quantum technologies?