Keynote Speakers

Bin Yu

University of California, Berkeley

Veridical Data Science
Tuesday, Feb 4th

Bin Yu is Chancellor’s Professor in the Departments of Statistics and of Electrical Engineering & Computer Sciences at the University of California at Berkeley and a former chair of Statistics at UC Berkeley. Her research focuses on practice, algorithm, and theory of statistical machine learning and causal inference. Her group is engaged in interdisciplinary research with scientists from genomics, neuroscience, and precision medicine. In order to augment empirical evidence for decision-making, they are investigating methods/algorithms (and associated statistical inference problems) such as dictionary learning, non-negative matrix factorization (NMF), EM and deep learning (CNNs and LSTMs), and heterogeneous effect estimation in randomized experiments (X-learner). Their recent algorithms include staNMF for unsupervised learning, iterative Random Forests (iRF) and signed iRF (s-iRF) for discovering predictive and stable high-order interactions in supervised learning, contextual decomposition (CD) and aggregated contextual decomposition (ACD) for phrase or patch importance extraction from an LSTM or a CNN. Yu was a founding co-director of the Microsoft Research Asia (MSR) Lab at Peking Univeristy and is a member of the scientific advisory board at the Alan Turning Institute in the UK. She is a member of the U.S. National Academy of Sciences and Fellow of the American Academy of Arts and Sciences. She was a Guggenheim Fellow in 2006, and the Tukey Memorial Lecturer of the Bernoulli Society in 2012. She was President of IMS (Institute of Mathematical Statistics) in 2013-2014 and the Rietz Lecturer of IMS in 2016. She received the E. L. Scott Award from COPSS (Committee of Presidents of Statistical Societies) in 2018.

Ed H. Chi


From Missing Data to Boltzmann Distributions and Time Dynamics: The Statistical Physics of Recommendation
Tuesday, Feb 4th

Ed H. Chi is a Principal Scientist at Google, leading several machine learning research teams focusing on neural modeling, inclusive ML, reinforcement learning, and recommendation systems in Google Brain . He has delivered significant improvements for YouTube, News, Ads, Google Play Store, and other systems at Google with more than 150 product launches in the last 3 years. With 39 patents and over 120 research articles, he is also known for research on user behavior on the web. Prior to Google, he was Area Manager and Principal Scientist at Palo Alto Research Center's Augmented Social Cognition Group, where he led the team in understanding how social systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota. Recognized as an ACM Distinguished Scientist and elected into the CHI Academy, he recently received a 20-year Test of Time award for research in information visualization. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press. An avid swimmer, photographer and snowboarder in his spare time, he also has a blackbelt in Taekwondo.

Kristen Grauman

University of Texas at Austin & Facebook AI Research

Computer Vision for Fashion: From Individual Recommendations to World-Wide Trends
Wednesday, Feb 5th

Kristen Grauman is a Professor in the Department of Computer Science at the University of Texas at Austin and a Research Scientist at Facebook AI Research. Her research in computer vision and machine learning focuses on visual recognition and search. Before joining UT Austin in 2007, she received her Ph.D. at MIT. She is a AAAI Fellow, a Sloan Fellow, and a recipient of the NSF CAREER, ONR YIP, PECASE, PAMI Young Researcher award, and the 2013 IJCAI Computers and Thought Award. She and her collaborators were recognized with best paper awards at CVPR 2008, ICCV 2011, ACCV 2016, and a 2017 Helmholtz Prize “test of time” award. She served as a Program Chair of the Conference on Computer Vision and Pattern Recognition (CVPR) in 2015 and Neural Information Processing Systems (NeurIPS) in 2018, and she currently serves as Associate Editor-in-Chief for the Transactions on Pattern Analysis and Machine Intelligence (PAMI).