Maria-Florina Balcan is an Associate Professor in the School of Computer Science at Carnegie Mellon University. Her main research interests are machine learning and theoretical computer science. Her honors include the CMU SCS Distinguished Dissertation Award, an NSF CAREER Award, a Microsoft Faculty Research Fellowship, a Sloan Research Fellowship, and several paper awards. She has served as a Program Committee Co-chair for COLT 2014, a Program Committee Co-chair for ICML 2016, and a board member of the International Machine Learning Society.

Tong Zhang is a professor of Computer Science and Mathematics at the Hong Kong University of Science and Technology. His research interests are machine learning, big data and their applications. He obtained a BA in Mathematics and Computer Science from Cornell University, and a PhD in Computer Science from Stanford University. Before joining HKUST, Tong Zhang was a professor at Rutgers University, and worked previously at IBM, Yahoo as research scientists, Baidu as the director of Big Data Lab, and Tencent as the founding director of AI Lab. Tong Zhang was an ASA fellow and IMS fellow, and has served as the chair or area-chair in major machine learning conferences such as NIPS, ICML, and COLT, and has served as associate editors in top machine learning journals such as PAMI, JMLR, and Machine Learning Journal.

Raman Arora received his M.S. and Ph.D. degrees in Electrical and Computer Engineering from the University of Wisconsin-Madison in 2005 and 2009, respectively. From 2009-2011, he was a Postdoctoral Research Associate at the University of Washington in Seattle and a Visiting Researcher at Microsoft Research Redmond. Since 2011, he has been with Toyota Technological Institute at Chicago (TTIC). His research interests include machine learning, speech recognition and statistical signal processing.

Caroline Uhler joined the MIT faculty in 2015 as the Henry L. and Grace Doherty assistant professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society. She holds an MSc in mathematics, a BSc in biology, and an MEd in high school mathematics education from the University of Zurich. She obtained her PhD in statistics, with a designated emphasis in computational and genomic biology, from the University of California, Berkeley. Before joining MIT, she spent a semester as a research fellow in the program on Theoretical Foundations of Big Data Analysis at the Simons Institute at UC Berkeley, postdoctoral positions at the Institute for Mathematics and its Applications at the University of Minnesota and at ETH Zurich, and 3 years as an assistant professor at IST Austria. She is an elected member of the International Statistical Institute, a Sloan Research Fellow, and she received an NSF Career Award, a Sofja Kovalevskaja Award from the Humboldt Foundation and a START Award from the Austrian Science Foundation. Her research focuses on mathematical statistics and computational biology, in particular on graphical models and causal inference.

Barbara E. Engelhardt is a professor at Stanford University. She joined from Princeton, where she was a professor from 2014-2022, and previously Duke University, where she was an assistant professor from 2011-2014. She graduated from Stanford University and received her Ph.D. from the University of California, Berkeley, advised by Professor Michael Jordan. She did postdoctoral research at the University of Chicago, working with Professor Matthew Stephens, and three years at Duke University as an assistant professor. Interspersed among her academic experiences, she spent two years working at the Jet Propulsion Laboratory, a summer at Google Research, and a year at 23andMe, a DNA ancestry service. Professor Engelhardt received an NSF Graduate Research Fellowship, the Google Anita Borg Memorial Scholarship, and the Walter M. Fitch Prize from the Society for Molecular Biology and Evolution. As a faculty member, she received the NIH NHGRI K99/R00 Pathway to Independence Award, a Sloan Faculty Fellowship, and an NSF CAREER Award. Professor Engelhardt’s research interests involve developing statistical models and methods for the analysis of high-dimensional biomedical data, with a goal of understanding the underlying biological mechanisms of complex phenotypes and human disease.

Miroslav Dudík is a Senior Principal Researcher in machine learning at Microsoft Research, NYC. His research focuses on combining theoretical and applied aspects of machine learning, statistics, convex optimization, and algorithms. Most recently he has worked on contextual bandits, reinforcement learning, and algorithmic fairness. He received his PhD from Princeton in 2007. He is a co-creator of the Fairlearn toolkit for assessing and improving the fairness of machine learning models and of the Maxent package for modeling species distributions, which is used by biologists around the world to design national parks, model the impacts of climate change, and discover new species.

Simon Lacoste-Julien is an associate professor at Mila and DIRO from Université de Montréal, and Canada CIFAR AI Chair holder. He also heads part time the SAIT AI Lab Montreal from Samsung. His research interests are machine learning and applied math, with applications in related fields like computer vision and natural language processing. He obtained a B.Sc. in math., physics and computer science from McGill, a PhD in computer science from UC Berkeley and a post-doc from the University of Cambridge. He spent a few years as a research faculty at INRIA and École normale supérieure in Paris before coming back to his roots in Montreal in 2016 to answer the call from Yoshua Bengio in growing the Montreal AI ecosystem.

Currently, I am working as a Researcher in Information Visualisation, Visual Analytics, and Machine Learning at IBM Research AI in Cambridge, MA. I am interested in Visualization of large data sets of unstructured/semi-structured data, biological data, and neural network models. I enjoy advising students and enable them to do great work.

I try to be a good citizen in the community by reviewing regularly (InfoVis 2010-2018, BioVis 2012/13, CHI 2011/2014/2017-18, VAST 2010-2018, EuroVis,...), participating in InfoVis PC 2017-19, the VIS OC 2017-19, the BioVis 2013 OC and PC, and other committees. I had the honor to attend three great and motivating Dagstuhl seminars on InfoVis, BioVis, and Progressive Data Science. Oh, and I like to give talks from time to time.