Kamalika Chaudhuri, Associate Professor at University of California San Diego
Kamalika Chaudhuri received a Bachelor of Technology degree in Computer Science and Engineering in 2002 from Indian Institute of Technology, Kanpur, and a PhD in Computer Science from University of California at Berkeley in 2007. She held a postdoctoral researcher position at the Information Theory and Applications Center at UC San Diego from 2007-2009, and a postdoctoral researcher position in UC San Diego from 2007 to 2010. In July 2010, she joined the CSE department at UC San Diego as an assistant professor. She received an NSF CAREER Award in 2013 and a Hellman Faculty Fellowship in 2012. Kamalika’s research interests are in the statistical foundations of machine learning. She is interested in many aspects of learning theory, such as interactive learning, non-parametric methods, privacy-preserving machine learning, and adversarial learning.
George Chen, Assistant Professor at Carnegie Mellon University
George Chen is an assistant professor at Carnegie Mellon University’s Heinz College of Public Policy and Information Systems, and an affiliated faculty member of the Machine Learning Department. He primarily works on machine learning for healthcare and infrastructure development. A recurring theme across his work is the use of nonparametric prediction methods in solving temporal or spatial forecasting problems. Since these methods inform interventions that can be costly and affect people’s well-being, ensuring that predictions are interpretable is essential. George obtained his PhD in 2015 from the Electrical Engineering and Computer Science department at MIT, where he received the George M. Sprowls award for best PhD thesis in Computer Science. He previously completed his BS at UC Berkeley in 2010, dual majoring in Electrical Engineering and Computer Sciences, and Engineering Mathematics and Statistics.
Vishal Doshi, Data Scientist at Celect
Vishal Doshi is a data scientist with years of experience building systems that apply data science principles to complex problems. He is currently with Celect. He previously worked at the Pentagon, FCC, and the White House as part of the Obama Administration.
Alexei Efros, Associate Professor at University of California Berkeley
Alexei (Alyosha) Efros joined UC Berkeley in 2013. Prior to that, he was nine years on the faculty of Carnegie Mellon University, and has also been affiliated with École Normale Supérieure/INRIA and University of Oxford. His research is in the area of computer vision and computer graphics, especially at the intersection of the two. He is particularly interested in using data-driven techniques to tackle problems where large quantities of unlabeled visual data are readily available. Efros received his PhD in 2003 from UC Berkeley. He is a recipient of CVPR Best Paper Award (2006), NSF CAREER award (2006), Sloan Fellowship (2008), Guggenheim Fellowship (2008), Okawa Grant (2008), Finmeccanica Career Development Chair (2010), SIGGRAPH Significant New Researcher Award (2010), ECCV Best Paper Honorable Mention (2010), Helmholtz Test-of-Time Prize (2013), and the ACM Prize in Computing (2016).
Piotr Indyk, Professor at Massachusetts Institute of Technology
Piotr Indyk is a Professor of Electrical Engineering and Computer Science at MIT. He joined MIT in 2000, after earning PhD from Stanford University. Earlier, he received Magister degree from Uniwersytet Warszawski in 1995. Piotr’s research interests lie in the design and analysis of efficient algorithms. Specific interests include high-dimensional computational geometry, streaming and sub-linear algorithms, sparse recovery and machine learning. He is an ACM Fellow. His work on Sparse Fourier Transform has been named to Technology Review “TR10” in 2012, while his work on locality-sensitive hashing has received the 2012 Kanellakis Theory and Practice Award.
Christina Lee, Postdoc at Microsoft Research New England
Christina Lee is a postdoc at Microsoft Research New England. She received her PhD and MS in Electrical Engineering and Computer Science from MIT in the Laboratory for Information and Decision Systems. She received her BS in Computer Science from California Institute of Technology. In fall of 2018, she will be starting as an assistant professor at Cornell University in the Operations Research and Information Engineering Department. She is a recipient of the MIT Jacobs Presidential Fellowship, the NSF Graduate Research Fellowship, and the Claude E. Shannon Research Assistantship. Her research focuses on designing scalable algorithms for processing social data based on principles from statistical inference.
Ke Li, PhD student at University of California Berkeley
Ke Li is a fourth-year Ph.D. student at UC Berkeley advised by Prof. Jitendra Malik. He is interested in machine learning, algorithms and computer vision. Currently, he works on two research directions: Dynamic Continuous Indexing, which is a new family of exact randomized algorithms for k-nearest neighbour search that overcomes the curse of dimensionality, and Learning to Optimize, which is a new meta-learning framework for automatically discovering good optimization algorithms. He is grateful for fellowship support from the Natural Sciences and Engineering Research Council of Canada (NSERC). He received his Hon. B. Sc. in Computer Science from the University of Toronto in 2014.
Sewoong Oh, Assistant Professor at University of Illinois Urbana-Champaign
Sewoong Oh is an Assistant Professor of Industrial and Enterprise Systems Engineering at UIUC. He received his PhD from the department of Electrical Engineering at Stanford University. Following his PhD, he worked as a postdoctoral researcher at Laboratory for Information and Decision Systems (LIDS) at MIT. He was co-awarded the Kenneth C. Sevcik outstanding student paper award at the Sigmetrics 2010, the best paper award at the SIGMETRICS 2015, and NSF CAREER award in 2016.
Jonathan Yedidia, Director of AI Research at Analog Devices
Jonathan Yedidia is Director of AI Research for Analog Devices, and the Director of the Algorithmic Systems Group at Analog Garage. This growing group of researchers creates advanced algorithms in signal processing, machine learning, and artificial intelligence, and implements those algorithms in practical and efficient hardware. Previously he worked at Disney Research (2011-2015) and MERL (1998-2011), the internet start-up Viaweb (1997-1998), and as an independent chess player (1993-1997). His academic training at Harvard (A.B., 1985), Princeton (Ph.D. 1990) and the Ecole Normale Superieure (two years visit in 1989 and 1991) was in theoretical statistical physics, and he was also a junior fellow at Harvard’s Society of Fellows (1990-1993).