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Le Song, Assistant Professor, College of Computing, Georgia Institute of Technology

Le Song is an assistant professor in the College of Computing, Georgia Institute of Technology. He received his Ph.D. in Machine Learning from University of Sydney and NICTA in 2008, and then conducted his post-doctoral research in the Department of Machine Learning, Carnegie Mellon University, between 2008 and 2011. Before he joined Georgia Institute of Technology, he was a research scientist at Google. His principal research direction is machine learning, especially nonlinear methods and probabilistic graphical models for large scale and complex problems, arising from artificial intelligence, social network analysis, healthcare analytics, and other interdisciplinary domains. He is the recipient of the NSF CAREER Award’14, AISTATS’16 Best Student Paper Award, IPDPS’15 Best Paper Award, NIPS’13 Outstanding Paper Award, and ICML’10 Best Paper Award. He has also served as the area chair for leading machine learning conferences such as ICML, NIPS and AISTATS, and action editor for JMLR.

Abstract summary

Understanding Deep Learning for Big Data: The complexity and scale of big data impose tremendous challenges for their analysis. Yet, big data also offer us great opportunities. Some nonlinear phenomena, features or relations, which are not clear or cannot be inferred reliably from small and medium data, now become clear and can be learned robustly from big data. Typically, the form of the nonlinearity is unknown to us, and needs to be learned from data as well. Being able to harness the nonlinear structures from big data could allow us to tackle problems which are impossible before or obtain results which are far better than previous state-of-the-arts.

Nowadays, deep neural networks are the methods of choice when it comes to large scale nonlinear learning problems. What makes deep neural networks work? Is there any general principle for tackling high dimensional nonlinear problems which we can learn from deep neural works? Can we design competitive or better alternatives based on such knowledge? To make progress in these questions, my machine learning group performed both theoretical and experimental analysis on existing and new deep learning architectures, and investigate three crucial aspects on the usefulness of the fully connected layers, the advantage of the feature learning process, and the importance of the compositional structures. Our results point to some promising directions for future research, and provide guideline for building new deep learning models.

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Jacob Eisenstein, Assistant Professor, School of Interactive Computing, Georgia Institute of Technology

Jacob Eisenstein is an Assistant Professor in the School of Interactive Computing at Georgia Tech. He works on statistical natural language processing, focusing on computational sociolinguistics, social media analysis, discourse, and machine learning. He is a recipient of the NSF CAREER Award, a member of the Air Force Office of Scientific Research (AFOSR) Young Investigator Program, and was a SICSA Distinguished Visiting Fellow at the University of Edinburgh. His work has also been supported by the National Institutes for Health, the National Endowment for the Humanities, and Google. Jacob was a Postdoctoral researcher at Carnegie Mellon and the University of Illinois. He completed his Ph.D. at MIT in 2008, winning the George M. Sprowls dissertation award. Jacob’s research has been featured in the New York Times, National Public Radio, and the BBC. Thanks to his brief appearance in If These Knishes Could Talk, Jacob has a Bacon number of 2.

Abstract summary

Jennifer Marsman, Principal Software Development Engineer, Microsoft

Jennifer Marsman is a Principal Software Development Engineer in Microsoft’s Developer and Platform Evangelism group, where she educates developers on Microsoft’s new technologies. In this role, Jennifer is a frequent speaker at software development conferences around the world. In 2016, Jennifer was recognized as one of the “top 100 most influential individuals in artificial intelligence and machine learning” by Onalytica. She has been featured in Bloomberg for her work using EEG and machine learning to perform lie detection. In 2009, Jennifer was chosen as “Techie whose innovation will have the biggest impact” by X-OLOGY for her work with GiveCamps, a weekend-long event where developers code for charity. She has also received many honors from Microsoft, including the “Best in Role” award for Technical Evangelism, Central Region Top Contributor Award, Heartland District Top Contributor Award, DPE Community Evangelist Award, CPE Champion Award, MSUS Diversity & Inclusion Award, Gold Club, and Platinum Club. Prior to becoming a Developer Evangelist, Jennifer was a software developer in Microsoft’s Natural Interactive Services division. In this role, she earned two patents for her work in search and data mining algorithms. Jennifer has also held positions with Ford Motor Company, National Instruments, and Soar Technology. Jennifer holds a Bachelor’s Degree in Computer Engineering and Master’s Degree in Computer Science and Engineering from the University of Michigan in Ann Arbor. Her graduate work specialized in artificial intelligence and computational theory. Jennifer blogs at http://blogs.msdn.com/jennifer and tweets at http://twitter.com/jennifermarsman.

Abstract summary

Qiaoling Liu, Lead Data Scientist, CareerBuilder

Qiaoling Liu is a lead data scientist in CareerBuilder’s Information Extraction and Retrieval team under Data Science R&D group. Her team owns the projects of Company Name Normalization, School Name Normalization, Skill Identification and Normalization, and Recruitment Edge Signals at CareerBuilder. Her research interests include information retrieval, text mining, and semantic web. She received a Ph.D. in Computer Science and Informatics from Emory University, and a B.S. in Computer Science and Technology from Shanghai Jiao Tong University in China. During her PhD studies, she was a student recipient of the 2011, 2012, 2013 Yahoo! Faculty Research and Engagement Program (FREP) Award.

Abstract summary

Tim Chartier, Chief Academic Officer, Tresata

Chief Researcher for Tresata and Professor of Mathematics and Computer Science at Davidson College Dr. Tim Chartier specializes in sports analytics. He frequently consults on data analytics questions, including projects with ESPN Magazine, ESPN’s Sport Science program, NASCAR teams, the NBA, and fantasy sports sites. In 2014, Tim was named the inaugural Math Ambassador for the Mathematical Association of America, which also recognized Dr. Chartier’s ability to communicate math with a national teaching award. His research and scholarship were recognized with the prestigious Alfred P. Sloan Research Fellowship. Published by Princeton University Press, Tim authored Math Bytes: Google Bombs, Chocolate-Covered Pi, and Other Cool Bits in Computing. Through the Teaching Company, he taught a 24-lecture series entitled Big Data: How Data Analytics Is Transforming the World. In K-12 education, Tim has also worked with Google and Pixar on their educational initiatives. Dr. Chartier has served as a resource for a variety of media inquiries, including appearances with Bloomberg TV, NPR, the CBS Evening News, USA Today, and The New York Times.

Abstract summary

Robert Morris, CTO and Co-Founder, Predikto, Inc.

Robert Morris, Ph.D. is Co-founder and CTO of Predikto, Inc. He is also an award winning academic (formerly Associate Professor of Criminology (with tenure) at the University of Texas at Dallas). At UTD, he taught a variety of courses covering advanced data analytics and machine learning for the social sciences and for operations research. He has published over 50 peer-reviewed journal articles across many disciplines in outlets such as PLOS One, Journal of Quantitative Criminology, Justice Quarterly, Intelligence, etc.
Robert’s expertise lies in machine learning approaches for longitudinal processes to predict and explain human (criminal) behavior. However, he now applies this philosophy into Predikto’s patent pending automated machine learning platform, which has been successful predicting unplanned events across a range of different equipment classes within the IoT space, including: freight locomotives (electric and diesel), high-speed commuter trains, quay cranes, rail cars, commercial aircraft, datacenter HVAC, and more.

Abstract summary