Jennifer Wortman Vaughan
Jenn Wortman Vaughan is a Senior Principal Researcher at Microsoft Research, New York City. Her research background is in machine learning and algorithmic economics. She is especially interested in the interaction between people and AI, and has often studied this interaction in the context of prediction markets and other crowdsourcing systems. In recent years, she has turned her attention to fair and interpretable machine learning as part of MSR’s FATE group. 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 (PECASE), and a handful of best 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.
Jeff Kao is a computational journalist at ProPublica who uses data science to cover technology and artificial intelligence. He used natural language processing techniques to uncover 1.3 million fake comments submitted to the FCC in its proceeding repealing net neutrality. This work was cited in the Washington Post, Fortune Magazine and engadget, among other publications, and by members of the U.S. Senate. He has appeared as a data scientist in the New York Times and on the WNYC program Science Friday. He holds a law degree from Columbia Law School, where he was the editor-in-chief of the Columbia Science and Technology Law Review, and a bachelor’s degree in systems design engineering from the University of Waterloo.
David Jensen is Professor of Computer Science at the University of Massachusetts Amherst, and Director of the Knowledge Discovery Laboratory, which he founded in 2000. He also serves as the Associate Director of the Computational Social Science Institute, an interdisciplinary effort at UMass to study social phenomena using computational tools and concepts. His current research focuses on machine learning and data science for analyzing large social, technological, and computational systems. In particular, his work focuses on methods for constructing accurate causal models from observational and experimental data, with applications in explainable AI, social science, fraud detection, security, and systems management.
Steven Skiena is a Distinguished Teaching Professor of Computer Science at Stony Brook University, and director of the Stony Brook Institute for AI-Driven Discovery and Innovation. He was co-founder and the Chief Science Officer of General Sentiment, a social media and news analytics company. His research interests include algorithm design, data science and their applications to biology. Skiena is the author of several popular books in the fields of algorithms, programming, and data science. The Algorithm Design Manual is widely used as an undergraduate text in algorithms and within the tech industry for job interview preparation
Dr. David Doermann is a Professor of Empire Innovation at the University at Buffalo (UB) and the Director of the University at Buffalo Artificial Intelligence Institute. Prior to coming to UB he was a program manager at the Defense Advanced Research Projects Agency (DARPA) where he developed, selected and oversaw approximately $150 million in research and transition funding in the areas of computer vision, human language technologies and voice analytics. He coordinated performers on all of the projects, orchestrating consensus, evaluating cross team management and overseeing fluid program objectives. From 1993 to 2018, David was a member of the research faculty at the University of Maryland, College Park. David has over 250 publications in conferences and journals, is a fellow of the IEEE and IAPR, has numerous awards including an honorary doctorate from the University of Oulu, Finland and is a founding Editor-in-Chief of the International Journal on Document Analysis and Recognition.
Peter Koo is an Assistant Professor at the Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory. He is interested in uncovering the functional impact of genomic mutations through a computational lens using data-driven artificial intelligence solutions. His research develops methods to interpret high-performing deep learning models to distill knowledge that they learn from big, noisy genomic sequence data, with the broader aim of elucidating mechanisms of gene regulation. He received a Ph.D. in Physics at Yale University and transitioned into computational genomics during his postdoctoral research at Harvard University.