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Join IBM Research on March 28th, 2018 at 7:30pm ET for a conversation about our work using Data Science for Social Good. Members of IBM Research will give an overview of the program, and walk through recent projects that leveraged machine learning to produce social good. Projects include using natural language processing-based methodology to accelerate the work-flow of policy experts at UNDP, Accelerate Science Discovery. Following the talk, there will be a panel discussion to answer any questions. We look forward to meeting you!
Kush R. Varshney
Kush R. Varshney was born in Syracuse, NY in 1982. He received the B.S. degree (magna cum laude) in electrical and computer engineering with honors from Cornell University, Ithaca, NY, in 2004. He received the S.M. degree in 2006 and the Ph.D. degree in 2010, both in electrical engineering and computer science from the Massachusetts Institute of Technology (MIT), Cambridge. While at MIT, he was a National Science Foundation Graduate Research Fellow.
Dr. Varshney is a research staff member and manager with IBM Research AI at the Thomas J. Watson Research Center, Yorktown Heights, NY, where he leads the Learning and Decision Making group. He is the founding co-director of the IBM Science for Social Good initiative. He applies data science and predictive analytics to human capital management, healthcare, olfaction, computational creativity, public affairs, international development, and algorithmic fairness, which has led to recognitions such as the 2013 Gerstner Award for Client Excellence for contributions to the WellPoint team and the Extraordinary IBM Research Technical Accomplishment for contributions to workforce innovation and enterprise transformation. He conducts academic research on the theory and methods of statistical signal processing and machine learning. His work has been recognized through best paper awards at the Fusion 2009, SOLI 2013, KDD 2014, and SDM 2015 conferences.
Yaoli Mao is a Ben Wood Research Fellow affiliated with the Institute for Learning Technologies and Ph.D. student in the Cognitive Science in Education program at Teachers College Columbia University.
She conducts research using both quantitative and qualitative methods in the intersection of cognitive psychology, human-computer interaction and learning science. Yaoli is interested in social and cognitive-affective aspects of learning (engagement, boredom, and gaming etc.), learning strategies and behavior patterns. Her dissertation concerns collective intelligence, exploring knowledge sharing and learning among diverse expertise and human crowds’ intelligence can be properly evaluated, supported and elevated by machine learning and system design.
Jonathan will graduate in May with an MS in Data Science from Columbia University. He graduated summa cum laude in with a bachelor in Computer Science and Mathematics. Before coming to Columbia, he served as adjunct lecturer as well as developer for a health-tech company. Jonathan main areas of interests are machine learning and natural language processing, especially their utilization for social good. DSI students might recognize Jonathan from the 2017 Columbia Data Science Hackathon, in which his team came in first place. As a Science for Social Good Fellow at IBM, Jonathan’s work was focused on sentence/paragraph embedding and semantic searching techniques & applications.