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SDSCon
MIT Statistics and Data Science Conference
Elisa CelisYale University
Elisa Celis’ research focuses on problems that arise in the context of the Internet and its societal and economic implications. She approaches these problems by using both experimental and theoretical techniques. Her work spans multiple areas including social and computing crowdsourcing, data and network science, and mechanism design and algorithm with a current emphasis on fairness and diversity in artificial intelligence and machine learning. She has published articles in journals such as IEEE Transactions on Network Science and Engineering, Journal of Applied Network Science, Human Computation Journal, Management Science, SIAM Journal on Computing, among others. Before coming to Yale, she worked at the École Polytechnique Fédérale de Lausanne as a senior research scientist since June of 2014. Celis holds a Ph.D. in Computer Science and Engineering and an M.Sc. in Mathematics, both from the University of Washington.
Elisa CelisYale University
Elisa Celis’ research focuses on problems that arise in the context of the Internet and its societal and economic implications. She approaches these problems by using both experimental and theoretical techniques. Her work spans multiple areas including social and computing crowdsourcing, data and network science, and mechanism design and algorithm with a current emphasis on fairness and diversity in artificial intelligence and machine learning. She has published articles in journals such as IEEE Transactions on Network Science and Engineering, Journal of Applied Network Science, Human Computation Journal, Management Science, SIAM Journal on Computing, among others. Before coming to Yale, she worked at the École Polytechnique Fédérale de Lausanne as a senior research scientist since June of 2014. Celis holds a Ph.D. in Computer Science and Engineering and an M.Sc. in Mathematics, both from the University of Washington.
Michael JordanUC Berkeley
Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley.
His research interests bridge the computational, statistical, cognitive and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in distributed computing systems, natural language processing, signal processing and statistical genetics. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM.
Michael JordanUC Berkeley
Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley.
His research interests bridge the computational, statistical, cognitive and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in distributed computing systems, natural language processing, signal processing and statistical genetics. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM.
*Tentative* Xiao-Li MengHarvard University
Xiao-Li Meng, the Whipple V. N. Jones Professor of Statistics, and the Founding Editor-in-Chief of Harvard Data Science Review, is well known for his depth and breadth in research, his innovation and passion in pedagogy, his vision and effectiveness in administration, as well as for his engaging and entertaining style as a speaker and writer. Meng was named the best statistician under the age of 40 by COPSS (Committee of Presidents of Statistical Societies) in 2001, and he is the recipient of numerous awards and honors for his more than 150 publications in at least a dozen theoretical and methodological areas, as well as in areas of pedagogy and professional development.
*Tentative* Xiao-Li MengHarvard University
Xiao-Li Meng, the Whipple V. N. Jones Professor of Statistics, and the Founding Editor-in-Chief of Harvard Data Science Review, is well known for his depth and breadth in research, his innovation and passion in pedagogy, his vision and effectiveness in administration, as well as for his engaging and entertaining style as a speaker and writer. Meng was named the best statistician under the age of 40 by COPSS (Committee of Presidents of Statistical Societies) in 2001, and he is the recipient of numerous awards and honors for his more than 150 publications in at least a dozen theoretical and methodological areas, as well as in areas of pedagogy and professional development.
Philip NelsonGoogle
Philip Nelson leads a translational research team at Google, applying machine learning and advanced computational techniques to science and healthcare challenges. He joined Google in 2008 with the acquisition of his startup, and was previously responsible for a range of Google apps and geo services. He graduated from MIT in 1985 where he did award winning research on hip prosthetics at Harvard Medical School. Philip helped found and lead several Silicon Valley startups in search, optimization, and genome sequencing, and was also the CTO of JDate and an Entrepreneur in Residence at Accel Partners. Philip has lectured at Harvard, MIT, Caltech, Stanford, and other universities and has delivered keynote addresses at multiple conferences and corporate events.
Philip NelsonGoogle
Philip Nelson leads a translational research team at Google, applying machine learning and advanced computational techniques to science and healthcare challenges. He joined Google in 2008 with the acquisition of his startup, and was previously responsible for a range of Google apps and geo services. He graduated from MIT in 1985 where he did award winning research on hip prosthetics at Harvard Medical School. Philip helped found and lead several Silicon Valley startups in search, optimization, and genome sequencing, and was also the CTO of JDate and an Entrepreneur in Residence at Accel Partners. Philip has lectured at Harvard, MIT, Caltech, Stanford, and other universities and has delivered keynote addresses at multiple conferences and corporate events.
Tracy SlatyerMIT
Prof. Slatyer is a theoretical physicist who works on particle physics, cosmology and astrophysics. Her research is motivated by questions of fundamental particle physics — in particular, the nature and interactions of dark matter — but she seeks answers to these questions by studying possible signatures of new physics in astrophysical and cosmological data. She was awarded the 2017 Henry Primakoff Award for Early-Career Particle Physics by the Division of Particles and Fields of the American Physical Society.
Tracy SlatyerMIT
Prof. Slatyer is a theoretical physicist who works on particle physics, cosmology and astrophysics. Her research is motivated by questions of fundamental particle physics — in particular, the nature and interactions of dark matter — but she seeks answers to these questions by studying possible signatures of new physics in astrophysical and cosmological data. She was awarded the 2017 Henry Primakoff Award for Early-Career Particle Physics by the Division of Particles and Fields of the American Physical Society.
Pragya SurHarvard University
Pragya Sur is an Assistant Professor in the Statistics Department at Harvard University. Her research broadly spans high-dimensional statistics, statistical machine learning, robust inference and prediction under multi-study settings, and algorithmic fairness. A major part of her work focuses on the theoretical underpinnings of statistical inference procedures suitable for high-dimensional data. She simultaneously works on the statistical and computational properties of machine learning algorithms, and strives to develop robust algorithms with replicable prediction performance across multiple studies. Alongside, and to complement these ventures, she has explored the different definitional aspects of algorithmic fairness and their connections. She also finds interest in the applications of large scale statistical methods to computational neuroscience and genetics.
Prior to Harvard, she spent a year as a postdoctoral fellow at the Center for Research on Computation and Society, Harvard John A. Paulson School of Engineering and Applied Sciences. She completed a Ph.D. in Statistics in 2019 from Stanford University, where she received the Ric Weiland Graduate Fellowship (2017-2019) and the 2019 Theodore W. Anderson Theory of Statistics Dissertation Award.
Pragya SurHarvard University
Pragya Sur is an Assistant Professor in the Statistics Department at Harvard University. Her research broadly spans high-dimensional statistics, statistical machine learning, robust inference and prediction under multi-study settings, and algorithmic fairness. A major part of her work focuses on the theoretical underpinnings of statistical inference procedures suitable for high-dimensional data. She simultaneously works on the statistical and computational properties of machine learning algorithms, and strives to develop robust algorithms with replicable prediction performance across multiple studies. Alongside, and to complement these ventures, she has explored the different definitional aspects of algorithmic fairness and their connections. She also finds interest in the applications of large scale statistical methods to computational neuroscience and genetics.
Prior to Harvard, she spent a year as a postdoctoral fellow at the Center for Research on Computation and Society, Harvard John A. Paulson School of Engineering and Applied Sciences. She completed a Ph.D. in Statistics in 2019 from Stanford University, where she received the Ric Weiland Graduate Fellowship (2017-2019) and the 2019 Theodore W. Anderson Theory of Statistics Dissertation Award.