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SDSCon
MIT Statistics and Data Science Conference
Elisa CelisYale University
Elisa Celis’ research is on understanding and addressing problems of discrimination and bias in artificial intelligence and machine learning. Her focus is on problems that arise in the context of the Internet and its societal and economic implications, and she uses both experimental and theoretical methodologies. Her work spans multiple areas including data and network science, mechanism design, and algorithms.
Celis is an Assistant Professor at Yale University, where she co-founded the Computation & Society Initiative. She was named one of the 100 Brilliant Women in AI Ethics in 2020, and has a TEDx talk titled "Should you trust what AI says?". She 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 is on understanding and addressing problems of discrimination and bias in artificial intelligence and machine learning. Her focus is on problems that arise in the context of the Internet and its societal and economic implications, and she uses both experimental and theoretical methodologies. Her work spans multiple areas including data and network science, mechanism design, and algorithms.
Celis is an Assistant Professor at Yale University, where she co-founded the Computation & Society Initiative. She was named one of the 100 Brilliant Women in AI Ethics in 2020, and has a TEDx talk titled "Should you trust what AI says?". She 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. He received his Masters in Mathematics from Arizona State University, and earned his PhD in Cognitive Science in 1985 from the University of California, San Diego. He was a professor at MIT from 1988 to 1998. His research interests bridge the computational, statistical, cognitive,
biological and social sciences. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering,
a member of the American Academy of Arts and Sciences, and a Foreign Member of the Royal Society. He is a Fellow of the American Association
for the Advancement of Science. He was a Plenary Lecturer at the International Congress of Mathematicians in 2018. He received the
Ulf Grenander Prize from the American Mathematical Society in 2021, the IEEE John von Neumann Medal in 2020, the IJCAI Research Excellence
Award in 2016, the David E. Rumelhart Prize in 2015, and the ACM/AAAI Allen Newell Award in 2009. He gave the Inaugural IMS Grace Wahba Lecture
in 2022, the IMS Neyman Lecture in 2011, and an IMS Medallion Lecture in 2004. 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. He received his Masters in Mathematics from Arizona State University, and earned his PhD in Cognitive Science in 1985 from the University of California, San Diego. He was a professor at MIT from 1988 to 1998. His research interests bridge the computational, statistical, cognitive,
biological and social sciences. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering,
a member of the American Academy of Arts and Sciences, and a Foreign Member of the Royal Society. He is a Fellow of the American Association
for the Advancement of Science. He was a Plenary Lecturer at the International Congress of Mathematicians in 2018. He received the
Ulf Grenander Prize from the American Mathematical Society in 2021, the IEEE John von Neumann Medal in 2020, the IJCAI Research Excellence
Award in 2016, the David E. Rumelhart Prize in 2015, and the ACM/AAAI Allen Newell Award in 2009. He gave the Inaugural IMS Grace Wahba Lecture
in 2022, the IMS Neyman Lecture in 2011, and an IMS Medallion Lecture in 2004. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM.
Xiao-Li MengHarvard University
Xiao-Li Meng, the Founding Editor-in-Chief of HDSR and 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. In 2020, he was elected to the American Academy of Arts and Sciences. He has delivered more than 400 research presentations and public speeches on these topics, and he is the author of “The XL-Files," a thought-provoking and entertaining column in the IMS (Institute of Mathematical Statistics) Bulletin.
Xiao-Li MengHarvard University
Xiao-Li Meng, the Founding Editor-in-Chief of HDSR and 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. In 2020, he was elected to the American Academy of Arts and Sciences. He has delivered more than 400 research presentations and public speeches on these topics, and he is the author of “The XL-Files," a thought-provoking and entertaining column in the IMS (Institute of Mathematical Statistics) Bulletin.
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. Her particular areas of focus include research into scenarios where the dark matter experiences new forces of nature, precision theoretical predictions for photon signals from heavy colliding dark matter particles, modeling of the possible effects of dark matter interactions on the history of the early cosmos, and hands-on data analysis of high-energy gamma-ray data in search of dark matter signals. She was a co-discoverer of the giant gamma-ray structures known as the “Fermi Bubbles” erupting from the center of the Milky Way.
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. Her particular areas of focus include research into scenarios where the dark matter experiences new forces of nature, precision theoretical predictions for photon signals from heavy colliding dark matter particles, modeling of the possible effects of dark matter interactions on the history of the early cosmos, and hands-on data analysis of high-energy gamma-ray data in search of dark matter signals. She was a co-discoverer of the giant gamma-ray structures known as the “Fermi Bubbles” erupting from the center of the Milky Way.
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, and robust inference/prediction for multi-study or multi-source data. A major part of her work focuses on the theoretical underpinnings of statistical inference procedures applicable for high-dimensional data. She simultaneously works on the statistical and computational properties of machine learning algorithms. She strives to develop robust algorithms with replicable prediction performance across multiple studies that might exhibit covariate or label shifts. On the applied side, she finds interest in the applications of large-scale statistical methods to computational neuroscience and statistical genetics.
Her current research is supported by an NSF DMS Award and a William F. Milton Fund Award. Previously, 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, and robust inference/prediction for multi-study or multi-source data. A major part of her work focuses on the theoretical underpinnings of statistical inference procedures applicable for high-dimensional data. She simultaneously works on the statistical and computational properties of machine learning algorithms. She strives to develop robust algorithms with replicable prediction performance across multiple studies that might exhibit covariate or label shifts. On the applied side, she finds interest in the applications of large-scale statistical methods to computational neuroscience and statistical genetics.
Her current research is supported by an NSF DMS Award and a William F. Milton Fund Award. Previously, 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.