Synthetic Control Methods: Workshop Speakers

Alberto Abadie

Alberto Abadie
  • Massachusetts Institute of Technology

David Blei

David Blei
  • Columbia University

Jelena Bradic

Jelena Bradic
  • UC San Diego

Yingjie Feng

Yingjie Feng
  • Princeton University

Kathleen Li

Kathleen Li
  • UT Austin

Serena Ng

Serena Ng
  • Columbia University

Uros Seljak

Uros Seljak
  • UC Berkeley

Devavrat Shah

Devavrat Shah
  • Massachusetts Institute of Technology

Eric Tchetgen Tchetgen

Eric Tchetgen Tchetgen
  • University of Pennsylvania

Alex Torgovitsky

Alexander Torgovitsky
  • University of Chicago

Stefan Wager

Stefan Wager
  • Stanford University

Plenary Speakers

Coralia Cartis

Coralia Cartis

Mathematical Institute
University of Oxford

 

Bio

Coralia Cartis is Associate Professor in Numerical Optimisation in the Mathematical Institute, University of Oxford since 2013, and a Turing fellow of the Alan Turing Institute for Data Science since 2016; previously, she held academic and research positions at University of Edinburgh and Rutherford Appleton Laboratory, respectively. She holds a PhD degree from Cambridge University (under the supervision of Prof MJD Powell) and a BSc in Mathematics from Babesh-Bolyai University, Cluj, Romania. Her research interests are in nonlinear optimisation algorithm, especially their complexity analysis, as well as their implementation, and in diverse applications of optimisation from climate modelling to signal processing and machine learning.

 


 

Frank E. Curtis

Frank E. Curtis

Industrial and Systems Engineering
Lehigh University

 

Bio

Frank E. Curtis is an Associate Professor in the Department of Industrial and Systems Engineering at Lehigh University, where he has been employed since 2009. He received his Bachelor degree from the College of William and Mary in 2003 with a double major in Mathematics and Computer Science, received his Master degree in 2004 and Ph.D. in 2007 from the Department of Industrial Engineering and Management Science at Northwestern University, and spent two years as a Postdoctoral Researcher in the Courant Institute of Mathematical Sciences at New York University from 2007 until 2009. His research focuses on the design, analysis, and implementation of numerical methods for solving large-scale nonlinear optimization problems. He received an Early Career Award from the Advanced Scientific Computing Research program of the U.S. Department of Energy, and has received funding from various programs of the U.S. National Science Foundation, including through a TRIPODS Institute grant awarded to him and his collaborators at Lehigh, Northwestern, and Boston University. He was awarded, with James V. Burke (U. of Washington), Adrian Lewis (Cornell), and Michael Overton (NYU), the 2018 INFORMS Computing Society Prize. He and team members Daniel Molzahn (Georgia Tech), Andreas Waechter (Northwestern), Ermin Wei (Northwestern), and Elizabeth Wong (UC San Diego) were awarded second place in the ARPA-E Grid Optimization Competition in 2020. He currently serves as an Associate Editor for Mathematical Programming, SIAM Journal on Optimization, Mathematics of Operations Research, and Mathematical Programming Computation. He served as the Vice Chair for Nonlinear Programming for the INFORMS Optimization Society from 2010 until 2012, and is currently very active in professional societies and groups related to mathematical optimization, including INFORMS, the Mathematics Optimization Society, and the SIAM Activity Group on Optimization.

 

 

 

David Goldfarb

Donald Goldfarb

Industrial Engineering and Operations Research

Columbia University 

 

Bio

Donald Goldfarb is the Avanessians Professor in the IEOR Department at Columbia University. He is internationally recognized for the development and analysis of ecient and practical algorithms for solving various classes of optimization problems, including the BFGS quasi-Newton method (QN) for unconstrained optimization, steepest-edge simplex algorithms for linear programming, and the Goldfarb-Idnani algorithm for convex quadratic programming. According to SIAM News (2016), Newton and quasi-Newton methods and simplex methods rank first and ninth, respectively among all ”algorithms with the greatest influence on the development and practice of science and engineering in the 20th century”. The BFGS and steepest-edge algorithms developed by Goldfarb, are the basis for the most successful variants of these classes of methods. Professor Goldfarb has also developed simplex and combinatorial algorithms for network flow problems, interior-point methods for linear, quadratic and second-order cone programming, including algorithms for robust optimization, and first-order algorithms for image de-noising, compressed sensing and machine learning. After obtaining a PhD degree from Princeton, Goldfarb spent two years as a post-doc at the Courant Institute. In 1968, he co-founded the CS Department at the City College of New York, serving 14 years on its faculty. During the 1979-80 academic year, he was a Visiting Professor in the CS and ORIE Departments at Cornell University. In 1982, Goldfarb joined the IEOR Department at Columbia, serving as Chair from 1984-2002. He also served as Interim Dean of Columbia’s School of Engineering and Applied Science during the 1994-95 and 2012-13 academic years and its Executive Vice Dean during the Spring 2012 semester. Goldfarb is a SIAM Fellow. He was awarded the INFORMS John Von Neumann Theory Prize in 2017, the Khachiyan Prize in 2013 the INFORMS Prize for Research Excellence in the Interface between OR and CS in 1995, and was listed in The Worlds Most Influential Scientific Minds, 2014, as being among the 99 most cited mathematicians between 2002 and 2012. Goldfarb has served as an editor-in-chief of Mathematical Programming, an editor of the SIAM Journal on Numerical Analysis and the SIAM Journal on Optimization, and as an associate editor of Mathematics of Computation, Operations Research and Mathematical Programming Computation.

 

Elad Hazan

Elad Hazan

Department of Computer Science

Princeton University

 

Bio:
Elad Hazan is a professor of computer science at Princeton University. His research focuses on the design and analysis of algorithms for basic problems in machine learning and optimization. Amongst his contributions are the co-development of the AdaGrad optimization algorithm, and the first sublinear-time algorithms for convex optimization. He is the recipient of the Bell Labs prize, (twice) the IBM Goldberg best paper award in 2012 and 2008, a European Research Council grant, a Marie Curie fellowship and Google Research Award (twice). He served on the steering committee of the Association for Computational Learning and has been program chair for COLT 2015. In 2017 he co-founded In8 inc. focusing on efficient optimization and control, acquired by Google in 2018. He is the co-founder and director of Google AI Princeton.

Adrian Lewis

Adrian Lewis

Operations Research and Information Engineering

Cornell University

 

Bio

 

 

Jorge Nocedal

Jorge Nocedal

Industrial Engineering and Management Sciences

Northwestern University

 

Bio

Jorge Nocedal is the Walter P. Murphy Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University. He obtained his B.S. degree in physics from UNAM, Mexico, and a PhD in mathematical sciences from Rice University. His research is in optimization, both deterministic and stochastic, and with emphasis on large-scale problems.  His current work is driven by applications in machine learning. He served as editor-in-chief of the SIAM Journal on Optimization,  is a SIAM Fellow, was awarded the 2012 George B. Dantzig Prize as well as the 2017 Von Neumann Theory Prize, for contributions to theory and algorithms of nonlinear optimization. He is a member of the US National Academy of Engineering.
 

Michael Overton

Michael Overton

Courant Institute of Mathematical Sciences

New York University 

 

Bio:

Michael L. Overton is Silver Professor of Computer Science and Mathematics at the Courant Institute of Mathematical Sciences, New York University.  He received his Ph.D. in Computer Science from Stanford University in 1979.  He is a fellow of SIAM (Society for Industrial and Applied Mathematics) and of the IMA (Institute of Mathematics and its Applications, UK). He served on the Council and Board of Trustees of SIAM from 1991 to 2005,

including a term as Chair of the Board from 2004 to 2005.

He was Editor-in-Chief of SIAM Journal on Optimization

from 1995 to 1999 and of the IMA Journal of Numerical Analysis from 2007 to 2008, and was the Editor-in-Chief of the MPS(Mathematical Programming Society)-SIAM joint book series

from 2003 to 2007.  He is currently an editor of SIAM Journal on Matrix Analysis and Applications, IMA Journal of Numerical Analysis, Foundations of Computational Mathematics, and Numerische Mathematik. His research interests are at the interface of optimization and linear algebra, especially nonsmooth optimization problems involving eigenvalues, pseudospectra, stability and robust control.  He is the author of “Numerical Computing with IEEE Floating Point Arithmetic” (SIAM, 2001).

 

 

Katya Scheinberg

Katya Scheinberg

Operations Research and Information Engineering

Cornell University

 

Bio

Katya Scheinberg is professor at the School of Operations Research and Information Engineering at Cornell University. Before then she held the Harvey E. Wagner Endowe Chair Professor position at the Industrial and Systems Engineering Department at Lehigh University. She was also a co-director of Lehigh Institute on Data, Intelligent Systems and Computation. Her main research areas are related to developing practical algorithms (and their theoretical analysis) for various problems in continuous optimization, such as convex optimization, derivative free optimization, machine learning, quadratic programming, etc. She published a book titled, Introduction to Derivative Free Optimization, which is co-authored with Andrew R. Conn and Luis N. Vicente for which they were awarded the Lagrange Prize in Continuous Optimization. Her resent research focuses on the analysis of probabilistic methods and stochastic optimization with a variety of  applications in machine learning and reinforcement learning. In 2019 she has been awarded the Farkas Prize by the Informs Optimization Society.
 

 

Invited Speakers