Keynote Speakers
Raia Hadsell, Google DeepMind
Superhuman Multitasking
Raia Hadsell, a research scientist at Google DeepMind, has worked on deep learning and robotics problems for the last 10 years. Her thesis on Vision for Mobile Robots won the Best Dissertation award from New York University, and was followed by a postdoc at Carnegie Mellon's Robotics Institute. Raia then worked as a senior scientist and tech manager at SRI International. Raia joined DeepMind in 2014, where she leads a research team studying robotic navigation and lifelong learning.
Lillian Lee, Cornell
Is it all in the Phrasing? Computational Explorations in how we say what we say, and why it matters
Lillian Lee is a professor of computer science and of information science at Cornell University, and the co-Editor-in-Chief, together with Michael Collins, of Transactions of the ACL. Her research interests include natural language processing and computational social science. She is the recipient of the inaugural Best Paper Award at HLT-NAACL 2004 (joint with Regina Barzilay), a citation in "Top Picks: Technology Research Advances of 2004" by Technology Research News (also joint with Regina Barzilay), and an Alfred P. Sloan Research Fellowship; and in 2013, she was named a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI). Her group's work has received several mentions in the popular press, including The New York Times, NPR's All Things Considered, and NBC's The Today Show, and one of her co-authored papers was publicly called "boring" by Youtubers Rhett and Link, in a video viewed over 2.2 million times.
Been Kim, AI2/University of Washington
Interactive and Interpretable Machine Learning Models for Human Machine Collaboration
Been Kim is a Research Scientist at AI2 and an affiliate faculty in the Department of Computer Science & Engineering at the University of Washington. Her research focuses on interactive and interpretable machine learning models for human-machine collaboration. She received her PhD. from MIT. Prior to her PhD, she worked at the MathWorks as a software engineer.
Corinna Cortes, Google Research
Structured Data
Corinna Cortes is the Head of Google Research, NY, where she is working on a broad range of theoretical and applied large-scale machine learning problems. Prior to Google, Corinna spent more than ten years at AT&T Labs - Research, formerly AT&T Bell Labs, where she held a distinguished research position. Corinna's research work is well-known in particular for her contributions to the theoretical foundations of support vector machines (SVMs), for which she jointly with Vladimir Vapnik received the 2008 Paris Kanellakis Theory and Practice Award, and her work on data-mining in very large data sets for which she was awarded the AT&T Science and Technology Medal in the year 2000. Corinna received her MS degree in Physics from University of Copenhagen and joined AT&T Bell Labs as a researcher in 1989. She received her Ph.D. in computer science from the University of Rochester in 1993.
Superhuman Multitasking
Raia Hadsell, a research scientist at Google DeepMind, has worked on deep learning and robotics problems for the last 10 years. Her thesis on Vision for Mobile Robots won the Best Dissertation award from New York University, and was followed by a postdoc at Carnegie Mellon's Robotics Institute. Raia then worked as a senior scientist and tech manager at SRI International. Raia joined DeepMind in 2014, where she leads a research team studying robotic navigation and lifelong learning.
Lillian Lee, Cornell
Is it all in the Phrasing? Computational Explorations in how we say what we say, and why it matters
Lillian Lee is a professor of computer science and of information science at Cornell University, and the co-Editor-in-Chief, together with Michael Collins, of Transactions of the ACL. Her research interests include natural language processing and computational social science. She is the recipient of the inaugural Best Paper Award at HLT-NAACL 2004 (joint with Regina Barzilay), a citation in "Top Picks: Technology Research Advances of 2004" by Technology Research News (also joint with Regina Barzilay), and an Alfred P. Sloan Research Fellowship; and in 2013, she was named a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI). Her group's work has received several mentions in the popular press, including The New York Times, NPR's All Things Considered, and NBC's The Today Show, and one of her co-authored papers was publicly called "boring" by Youtubers Rhett and Link, in a video viewed over 2.2 million times.
Been Kim, AI2/University of Washington
Interactive and Interpretable Machine Learning Models for Human Machine Collaboration
Been Kim is a Research Scientist at AI2 and an affiliate faculty in the Department of Computer Science & Engineering at the University of Washington. Her research focuses on interactive and interpretable machine learning models for human-machine collaboration. She received her PhD. from MIT. Prior to her PhD, she worked at the MathWorks as a software engineer.
Corinna Cortes, Google Research
Structured Data
Corinna Cortes is the Head of Google Research, NY, where she is working on a broad range of theoretical and applied large-scale machine learning problems. Prior to Google, Corinna spent more than ten years at AT&T Labs - Research, formerly AT&T Bell Labs, where she held a distinguished research position. Corinna's research work is well-known in particular for her contributions to the theoretical foundations of support vector machines (SVMs), for which she jointly with Vladimir Vapnik received the 2008 Paris Kanellakis Theory and Practice Award, and her work on data-mining in very large data sets for which she was awarded the AT&T Science and Technology Medal in the year 2000. Corinna received her MS degree in Physics from University of Copenhagen and joined AT&T Bell Labs as a researcher in 1989. She received her Ph.D. in computer science from the University of Rochester in 1993.
Opening Remarks
Amy Greenwald, Brown University
Dr. Amy Greenwald is Associate Professor of Computer Science at Brown University in Providence, Rhode Island. She studies game-theoretic and economic interactions among computational learning agents. In 2015, she was a visiting researcher at Microsoft Research in New York City. In 2011, she was named a Fulbright Scholar to the Netherlands (though she declined the award). She was awarded a Sloan Fellowship in 2006; she was nominated for the 2002 Presidential Early Career Award for Scientists and Engineers (PECASE); and she was named one of the Computing Research Association's Digital Government Fellows in 2001. Before joining the faculty at Brown, Dr. Greenwald was employed by IBM's T.J. Watson Research Center. Her paper entitled "Shopbots and Pricebots" (joint work with Jeff Kephart) was named Best Paper at IBM Research in 2000.
Hanna Wallach, Microsoft Research
Hanna Wallach is a senior researcher at Microsoft Research New York City and an assistant professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. She is also a core faculty member in UMass's Computational Social Science Institute. Hanna develops machine learning methods for analyzing the structure, content, and dynamics of social processes. Her work is inherently interdisciplinary---she collaborates with political scientists, sociologists, and journalists to learn how organizations work in practice by analyzing publicly available interaction data, such as public record email networks, document dumps, press releases, meeting transcripts, and news articles. Hanna's research has had broad impact in machine learning, natural language processing, and the nascent field of computational social science. Her work on infinite belief networks won the best paper award at AISTATS in 2010. She is the recipient of several National Science Foundation grants, an IARPA grant, and a grant from the Office of Juvenile Justice and Delinquency Prevention. Hanna is committed to increasing diversity and has co-founded several organizations to address the underrepresentation of women in computing, including the annual Women in Machine Learning Workshop. She holds a BA in computer science from the University of Cambridge, an MS in cognitive science and machine learning from the University of Edinburgh, and a PhD in machine learning from the University of Cambridge. Most importantly, she is (to her knowledge) the only person to have appeared in both Glamour magazine ("35 Women Under 35 Who Are Changing the Tech Industry") and Linux Format.
Jennifer Wortman Vaughan, Microsoft Research
Jenn Wortman Vaughan is a Senior Researcher at Microsoft Research, New York City, where she studies algorithmic economics, machine learning, and social computing, with a recent focus on prediction markets and crowdsourcing. Jenn came to MSR in 2012 from UCLA, where she was an assistant professor in the computer science department. She completed her Ph.D. at the University of Pennsylvania in 2009, and subsequently spent a year as a Computing Innovation Fellow at Harvard. She is the recipient of Penn's 2009 Rubinoff dissertation award for innovative applications of computer technology, a National Science Foundation CAREER award, a Presidential Early Career Award for Scientists and Engineers, and a handful of best paper or best student paper awards. In her "spare" time, Jenn is involved in a variety of efforts to provide support for women in computer science; most notably, she co-founded the Annual Workshop for Women in Machine Learning, which has been held each year since 2006.
Dr. Amy Greenwald is Associate Professor of Computer Science at Brown University in Providence, Rhode Island. She studies game-theoretic and economic interactions among computational learning agents. In 2015, she was a visiting researcher at Microsoft Research in New York City. In 2011, she was named a Fulbright Scholar to the Netherlands (though she declined the award). She was awarded a Sloan Fellowship in 2006; she was nominated for the 2002 Presidential Early Career Award for Scientists and Engineers (PECASE); and she was named one of the Computing Research Association's Digital Government Fellows in 2001. Before joining the faculty at Brown, Dr. Greenwald was employed by IBM's T.J. Watson Research Center. Her paper entitled "Shopbots and Pricebots" (joint work with Jeff Kephart) was named Best Paper at IBM Research in 2000.
Hanna Wallach, Microsoft Research
Hanna Wallach is a senior researcher at Microsoft Research New York City and an assistant professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. She is also a core faculty member in UMass's Computational Social Science Institute. Hanna develops machine learning methods for analyzing the structure, content, and dynamics of social processes. Her work is inherently interdisciplinary---she collaborates with political scientists, sociologists, and journalists to learn how organizations work in practice by analyzing publicly available interaction data, such as public record email networks, document dumps, press releases, meeting transcripts, and news articles. Hanna's research has had broad impact in machine learning, natural language processing, and the nascent field of computational social science. Her work on infinite belief networks won the best paper award at AISTATS in 2010. She is the recipient of several National Science Foundation grants, an IARPA grant, and a grant from the Office of Juvenile Justice and Delinquency Prevention. Hanna is committed to increasing diversity and has co-founded several organizations to address the underrepresentation of women in computing, including the annual Women in Machine Learning Workshop. She holds a BA in computer science from the University of Cambridge, an MS in cognitive science and machine learning from the University of Edinburgh, and a PhD in machine learning from the University of Cambridge. Most importantly, she is (to her knowledge) the only person to have appeared in both Glamour magazine ("35 Women Under 35 Who Are Changing the Tech Industry") and Linux Format.
Jennifer Wortman Vaughan, Microsoft Research
Jenn Wortman Vaughan is a Senior Researcher at Microsoft Research, New York City, where she studies algorithmic economics, machine learning, and social computing, with a recent focus on prediction markets and crowdsourcing. Jenn came to MSR in 2012 from UCLA, where she was an assistant professor in the computer science department. She completed her Ph.D. at the University of Pennsylvania in 2009, and subsequently spent a year as a Computing Innovation Fellow at Harvard. She is the recipient of Penn's 2009 Rubinoff dissertation award for innovative applications of computer technology, a National Science Foundation CAREER award, a Presidential Early Career Award for Scientists and Engineers, and a handful of best paper or best student paper awards. In her "spare" time, Jenn is involved in a variety of efforts to provide support for women in computer science; most notably, she co-founded the Annual Workshop for Women in Machine Learning, which has been held each year since 2006.