Dr. Oren Etzioni has served as the Chief Executive Officer of the Allen Institute for AI (AI2) since its inception in 2014. He has been a Professor at the University of Washington’s Computer Science department since 1991, and a Venture Partner at the Madrona Venture Group since 2000. He has garnered several awards including Seattle’s Geek of the Year (2013), the Robert Engelmore Memorial Award (2007), the IJCAI Distinguished Paper Award (2005), AAAI Fellow (2003), and a National Young Investigator Award (1993). He has been the founder or co-founder of several companies, including Farecast (sold to Microsoft in 2008) and Decide (sold to eBay in 2013). He has written commentary on AI for The New York Times, Nature, Wired, and the MIT Technology Review. He helped to pioneer meta-search (1994), online comparison shopping (1996), machine reading (2006), and Open Information Extraction (2007). He has authored over 100 technical papers that have garnered over 2,000 highly influential citations on Semantic Scholar. He received his Ph.D. from Carnegie Mellon in 1991 and his B.A. from Harvard in 1986.
Semantic Scholar, NLP, and the Fight Against COVID-19(Track Keynote)
Andrew Gelman is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University. He has received the Outstanding Statistical Application award from the American Statistical Association, the award for best article published in the American Political Science Review, and the Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of 40. His books include Bayesian Data Analysis (with John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Don Rubin), Teaching Statistics: A Bag of Tricks (with Deb Nolan), Data Analysis Using Regression and Multilevel/Hierarchical Models (with Jennifer Hill), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, and Jeronimo Cortina), and A Quantitative Tour of the Social Sciences (co-edited with Jeronimo Cortina). Andrew has done research on a wide range of topics, including: why it is rational to vote; why campaign polls are so variable when elections are so predictable; why redistricting is good for democracy; reversals of death sentences; police stops in New York City, the statistical challenges of estimating small effects; the probability that your vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in your basement; toxicology; medical imaging; and methods in surveys, experimental design, statistical inference, computation, and graphics.
Dr. Shanghang Zhang is a postdoctoral research fellow in the Berkeley AI Research (BAIR) Lab, the Department of Electrical Engineering and Computer Sciences, UC Berkeley, USA. She received her Ph.D. from Carnegie Mellon University in 2018. Her research interests cover deep learning, computer vision, and reinforcement learning, as reflected in her numerous publications in top-tier journals and conference proceedings, including NeurIPS, CVPR, ICCV, and AAAI. Her research mainly focuses on machine learning with limited training data, including low-shot learning, domain adaptation, and meta-learning, which enables the learning system to automatically adapt to real-world variations and new environments. She was one of the “2018 Rising Stars in EECS” (a highly selective program launched at MIT in 2012, which has since been hosted at UC Berkeley, Carnegie Mellon, and Stanford annually). She has also been selected for the Qualcomm Innovation Fellowship (QInF) Finalist Award and Chiang Chen Overseas Graduate Fellowship.
Gill Bejerano is an Associate Professor of Developmental Biology and Computer Science at Stanford University, and a member of the Stanford Artificial Intelligence (AI) Laboratory and Bio-X program. Mr. Bejerano is a pioneer of Human Genome research. He is the discoverer of “Ultraconserved Elements”, human genomic regions that defy understanding of molecular evolution. He has also done influential work in applying Markovian models to biosequence analysis, and in showing that co-option of junk DNA into functional roles is an under-appreciated force shaping the evolution of the Human Genome. He serves Member of the Technical Advisory Board of Numenta, Inc. He holds a triple BSc in Mathematics, Physics and Computer Science (summa cum laude) and a PhD in Computer Science (Machine Learning applications in Bioinformatics) from the Hebrew University of Jerusalem.
Sriram Sankararaman is an assistant professor in the Departments of Computer Science, Human Genetics, and Computational Medicine at UCLA where he leads the machine learning and genomic lab. His research interests lie at the interface of computer science, statistics and biology and is interested in developing statistical machine learning algorithms to make sense of large-scale biomedical data and in using these tools to understand the interplay between evolution, our genomes and traits. He received a B.Tech. in Computer Science from the Indian Institute of Technology, Madras, a Ph.D. in Computer Science from UC Berkeley and was a post-doctoral fellow in Harvard Medical School before joining UCLA. He is a recipient of the Alfred P. Sloan Foundation fellowship (2017), Okawa Foundation grant (2017), the UCLA Hellman fellowship (2017), the NIH Pathway to Independence Award (2014), a Simons Research fellowship (2014), and a Harvard Science of the Human Past fellowship (2012) as well as the Northrop-Grumman Excellence in Teaching Award at UCLA (2019).
Brian Lucena is a Principal at Lucena Consulting and a consulting Data Scientist at Agentero. An applied mathematician in every sense, he is passionate about applying modern machine learning techniques to understand the world and act upon it. In previous roles, he has served as SVP of Analytics at PCCI, Principal Data Scientist at Clover Health, and Chief Mathematician at Guardian Analytics. He has taught at numerous institutions including UC-Berkeley, Brown, USF, and the Metis Data Science Bootcamp.
Ariel Rokem is a Research Assistant Professor at the University of Washington Department of Psychology. He received a PhD in neuroscience from UC Berkeley (2010) and additional postdoctoral training in computational neuroimaging at Stanford (2011 – 2015). He was also previously a Senior Data Scientist at the University of Washington eScience Institute (2015-2020)
He leads a research program in neuroinformatics, the development of data science tools, techniques and methods and their application to the analysis of neural data. One thrust of this research focuses specifically on the application of methods from statistical learning to analysis of diffusion MRI data acquired in human brain. This type of data sheds light on the role that human brain connections play in cognitive abilities, in diverse behaviors, and in neurological and psychiatric disorders.
Another thrust of the research focuses specifically on the development of systems for analysis (e.g., Mehta et al. 2017, Richford and Rokem, 2018) and sharing (e.g. Yeatman et al. 2018) of large-scale open datasets, to enable research with datasets that are increasingly becoming available through data-sharing initiatives, and to facilitate its reproducibility.
He is a member of the Software and Data Carpentry communities, where he has been an instructor since 2013 and an instructor trainer 2015. He also directs the annual Summer Institute for Neuroimaging and Data Science (NeuroHackademy). A contributor to multiple open-source software projects in the scientific Python ecosystem, he is a member of the editorial board of the Journal for Open Source Software (JOSS).
Victor S.Y. Lo is a seasoned Big Data, Marketing, Risk, and Finance leader with over 25 years of extensive consulting and corporate experience employing data-driven solutions in a wide variety of business areas, including Customer Relationship Management, Market Research, Advertising Strategy, Risk Management, Financial Econometrics, Insurance Analytics, Product Development, Healthcare Analytics, Operations Management, Transportation, and Human Resources. He is actively engaged with causal inference and is a pioneer of Uplift/True-lift modeling, a key subfield of data science.
Victor has managed teams of quantitative analysts in multiple organizations. He currently leads the AI and Data Science Center of Excellence, Workplace Investing at Fidelity Investments. Previously he managed advanced analytics/data science teams in Personal Investing, Corporate Treasury, Managerial Finance, and Healthcare and Total Well-being at Fidelity Investments. Prior to Fidelity, he was VP and Manager of Modeling and Analysis at FleetBoston Financial (now Bank of America), and Senior Associate at Mercer Management Consulting (now Oliver Wyman).
For academic services, Victor has been a visiting research fellow and corporate executive-in-residence at Bentley University. He has also been serving on the steering committee of the Boston Chapter of the Institute for Operations Research and the Management Sciences (INFORMS) and on the editorial board for two academic journals. He is also an elected board member of the National Institute of Statistical Sciences (NISS). Victor earned a master’s degree in Operational Research and a PhD in Statistics and was a Postdoctoral Fellow in Management Science. He has co-authored a graduate-level econometrics book and published numerous articles in Data Mining, Marketing, Statistics, and Management Science literature, and is completing a graduate-level book on causal inference in business.
Talk: The Era of Brain Observatories: Open-Source Tools for Data-Driven Neuroscience
Talk: Semantic Scholar and the Fight Against COVID-19
Talk: Bayesian Workflow as Demonstrated with a Coronavirus Example
Talk: StructureBoost: Gradient Boosting with Categorical Structure
Talk: Uplift Modeling Tutorial: From Predictive to Prescriptive Analytics
Talk: AI Research at Bloomberg
Workshop: Opening the Pod Bay Doors: Building Intelligent Agents That Can Interpret, Generate and Learn from Natural Language
Talk: Learning with Limited Labels
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