Due to heightened concerns about the spread of coronavirus (COVID-19), we will be moving to an all-virtual format for the Data Science Research Symposium. There will be no in-person program this year; attendees will receive instructions for participating online. Please continue to register as this will help us plan, and will ensure that you receive timely updates on the new program, schedule, and delivery method.
The 2020 Data Science Research Symposium will be held vritually on Thursday, April 16, 2020. This annual event explores the practical applications of data science and the potential of its research.
Questions? Email us at CDSemail@example.com.
* Amazon * Colaberry * C&S Wholesale Grocers * Lexalytics * MassMutual * Motorola * MuyVentive * Raytheon * Voya Financial *
The agenda will include two keynote addresses, as well as lightning talks from UMass faculty.
Industry Keynote Address
Susan Dumais, Director, Microsoft Research Labs New England, New York City, Montréal.
Title: The Potential for Personalization in Search
Abstract: Traditionally, web search engines returned the same results to everyone who asks the same question. However, using a single ranking for everyone in every context at every point in time limits how well a search engine can do in providing relevant information. In this talk I present a framework to quantify the "potential for personalization” which is used to characterize the extent to which different people have different intents for the same query. I describe several examples of how different types of contextual features are represented and used to improve search quality for individuals and groups. Finally, I conclude by highlighting important challenges in developing personalized systems at web scale including privacy, transparency, serendipity, and evaluation.
Bio: Susan Dumais is a technical fellow and director of the Microsoft Research Labs in New England, New York City and Montréal, and an adjunct professor at the University of Washington. Prior to joining Microsoft, she was a member of technical staff at Bell Labs and Bellcore. Her research is at the intersection of human-computer interaction, information retrieval, and web and data science. A common theme that runs through her work is the importance of understanding and improving information systems from an interdisciplinary and user-centered perspective. She is a co-inventor of Latent Semantic Analysis, a well-known word embedding technique, which was designed to mitigate the disagreement between the words that authors use in writing and those that searchers use to find information. Her research spans a wide range of topics in information systems, including email spam filtering, user modeling and personalization, context-aware information systems, temporal dynamics of information, and large-scale behavioral interactions. She has worked closely with several Microsoft product teams (Bing, Windows Search, SharePoint, and Office Help) on search-related innovations. Susan is an ACM Fellow, was elected to the CHI Academy, the National Academy of Engineering (NAE) and the American Academy of Arts and Sciences (AAAS), and received the SIGIR Gerard Salton Award for lifetime achievement in information retrieval, the ACM Athena Lecturer Award, the Tony Kent Strix Award for outstanding contributions to information systems, and the Lifetime Achievement Award from Indiana University Department of Psychological and Brain Science.
Faculty Keynote Address
Erik Learned-Miller, Professor, UMass College of Information and Computer Sciences (CICS).
Title: How to manage the complex implications of face recognition technology: A possible way forward
Abstract: There has been a great deal of hand-wringing about face recognition technology. Important work has shown that face recognition algorithms can be unfair, can amplify pre-existing biases, and can create substantial harm to individuals. Others have testified to the improvements in efficiency of doing important work like tracking down child sex traffickers. Still others argue that many applications of face recognition technology are benign and that large scale bans are unreasonable. How can we integrate and balance these concerns? Many arguments center around machine learning ideas like unbiased learning, better training sets, and algorithms that are robust to “domain transfer.” In this talk, I will argue that the problem is much larger than these (important) technical issues. To do so, I will examine some of the regulatory structures, processes, definitions, rules, and conventions that have been developed by the US Food and Drug Administration (FDA). I will draw heavily from two separate scenarios: the FDA’s regulation of pharmaceuticals and their regulation of medical devices. The elaborate processes set up to regulate the drug and medical device industries have been remarkably successful (in many ways), and I will argue that many of the structures in place there deserve analogous systems for the regulation of face recognition.
Bio: Ph.D., Electrical Engineering and Computer Science, Massachusetts Institute of Technology (2002), M.S., Electrical Engineering and Computer Science, Massachusetts Institute of Technology (1997), B.A., Psychology, Yale University (1988). Professor Learned-Miller joined the faculty at the University of Massachusetts Amherst College of Information and Computer Sciences in 2004 as an Assistant Professor. His most recent position was a post-doctoral research engineer in the Electronics Research Laboratory at the University of California, Berkeley. Previously, Professor Learned-Miller was the Chief Executive Officer and co-founder of CORITechs, Inc., a company that designed surgical planning software for neurosurgeons.
Privacy & Security
Brian Levine, Professor and Director of the Cybersecurity Institute, UMass CICS
Gerome Miklau, Professor, UMass CICS
Phillipa Gill, Associate Professor, UMass CICS
Smart and Connected Society
Prashant Shenoy, Professor and Associate Dean of Computing and Facilities, UMass CICS
Brittany Johnson, CDS Postdoctoral Fellow, UMass CICS
Philip Thomas, Assistant Professor, UMass CICS, "Safe and Fair Machine Learning"
Secure Machine Learning
Adam O’Neill, Assistant Professor, UMass CICS
Madalina Fiterau, Assistant Professor, UMass CICS
Tauhidur Rahman, Assistant Professor, UMass CICS
Natural Language Processing
Mohit Iyyer, Assistant Professor, UMass CICS
Andrew McCallum, Distinguished Professor and Director of the Center for Data Science, UMass CICS