Speakers

• 

  Aravind Srinivasan

  University of Maryland, College Park

  Professor

  See Bio
• 

  Ben Moseley

  Carnegie Mellon University

  Associate Professor

  See Bio
• 

  Christian Kroer

  Columbia University

  Assistant Professor

  See Bio
• 

  Itai Ashlagi

  Stanford University

  Associate Professor

  See Bio
• 

  Julian Mestre

  Facebook

  Research Scientist

  See Bio
• 

  Mohammad Taghi Hajiaghayi

  University of Maryland, College Park

  Professor

  See Bio
• 

  Mohsen Bayati

  Stanford University

  Associate Professor

  See Bio
• 

  Nicolas Stier

  Facebook

  Director, Core Data Science

  See Bio
• 

  Rad Niazadeh

  University of Chicago

  Assistant Professor

  See Bio
• 

  Ramesh Johari

  Stanford University

  Professor

  See Bio
• 

  René Caldentey

  University of Chicago

  Professor

  See Bio
• 

  Stefan Wager

  Stanford University

  Assistant Professor

  See Bio
• 

  Vashist Avadhanula

  Facebook

  Research Scientist

  See Bio

×

Aravind Srinivasan

University of Maryland, College Park

Aravind Srinivasan is a Distinguished University Professor and a Professor with the Department of Computer Science and the Institute for Advanced Computer Studies at the University of Maryland, College Park. He received his undergraduate degree from the Indian Institute of Technology, Madras, and his Ph.D. from Cornell University. His main research-interests are in algorithms, probabilistic methods, data science, network science, and machine learning: theory, and applications in areas including health, E-commerce, cloud computing, Internet advertising, and fairness.Srinivasan was appointed a Distinguished University Professor of the University of Maryland in 2020. He is an elected Fellow of six professional societies: ACM, AAAS, IEEE, AMS, EATCS, and SIAM. He received a Distinguished Alumnus Award from his alma mater IIT Madras. He is also a recipient of the Dijkstra Prize and the Danny Lewin Award.

×

Ben Moseley

Carnegie Mellon University

Ben Moseley is the Carnegie Bosch Associate Professor of Operations Research in the Tepper School of Business at Carnegie Mellon University (CMU) and is a consulting professor at the start-up Relational AI. He is an Associate Professor of Machine Learning in the School of Computer Science (by courtesy) and member of the Ph.D. program Algorithms, Combinatorics and Optimization (ACO). He received a Ph.D. from the University of Illinois in Computer Science and was advised by Chandra Chekuri.Professor Moseley's research interests are broadly in operations research, theoretical computer science and machine learning. He works on the design, analysis and evaluation of algorithms. He is currently working on the algorithmic foundations of machine learning, big data analysis (e.g. relational in-database algorithms, distributed algorithm design, and streaming), and algorithms for scheduling and logistics.Ben Moseley has won several best paper awards including a 2015 IPDPS Best Paper Award, a SPAA 2013 Best Paper Award, and a SODA 2010 Best Student Paper Award. His work has been recognized with an oral presentation at NIPS 2017 (top 1.3% of submissions) and a spotlight presentation at NIPS 2018 (top 3.5% of submissions).Moseley's work has been supported by generous grants from the National Science Foundation, Yahoo, Infor, Google and Bosch. Financial support for Moseley's work includes a NSF CAREER Award, grants from NSF divisions on Computing and Communication Foundations (CCF) and Operations Engineering (OE), two Google Faculty Research Awards, a Yahoo! Academic Career Enabling (ACE) Award, an Infor faculty award and a Carnegie-Bosch faculty chair.In the past, he was an Assistant Professor in the Department of Computer Science and Engineering at Washington University in St. Louis from 2014-2017. He was a 2016 Simons-Berkeley Fellow. From 2012-2014 he was a Research Assistant Professor at the Toyota Technological Institute at Chicago. He was a visiting professor at Sandia National Laboratories in 2013 and has frequently been affiliated with Yahoo Labs.

×

Christian Kroer

Columbia University

Christian Kroer is an Assistant Professor of Industrial Engineering and Operations Research at Columbia University, as well as a member of the Data Science Institute at Columbia. His research interests are at the intersection of operations research, economics, and computation, with a focus on how optimization and AI methods enable large-scale economic solution concepts. He obtained his Ph.D. in computer science from Carnegie Mellon University, and spent a year as a postdoc with the Economics and Computation team at Facebook Research.

×

Itai Ashlagi

Stanford University

Ashlagi is an associate professor at the Management Science & Engineering department at Stanford.He is interested in market design, matching and game theory. His work contributed to school choice systems, organ allocation and kidney exchange platforms for which he also a Franz Edelman Laureate.

×

Julian Mestre

Facebook

Julian is a Research Scientist at Facebook. He is broadly interested in the design and analysis of efficient algorithms for combinatorial optimization problems such as covering, scheduling, routing and matching problems.

×

Mohammad Taghi Hajiaghayi

University of Maryland, College Park

I am a Guggenheim Fellow (2019), an ACM Fellow (2018) (the youngest in the class of 2018 fellows), an IEEE Fellow (2020), an EATCS Fellow (2020), a Blavatnik National Awards for Young Scientists Honoree (2020) and the Jack and Rita G. Minker Professor of Computer Science Department at the University of Maryland at College Park. I am also affiliated Professor with Robert H. Smith School of Business, University of Maryland. In addition, I hold a Research Affiliate position in MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). I am also a Permanent Member of Center for Discrete Mathematics and Theoretical Computer Science (DIMACS) at Rutgers. I was also a Visiting Scientist in the Simons Institute for the Theory of Computing at University of California, Berkeley, in Fall 2016, Spring 2018, and Fall 2018. My research is supported in part by NSF CAREER Award, ONR Young Investigator Award, Google Faculty Research Award (twice), University of Maryland Research and Scholarship Award (RASA), AT&T Research Labs, and a few other DARPA and AFOSR grants. I also thank European Association for Theoretical Computer Science for EATCS Nerode Prize, 2015 (awarded for our outstanding JACM paper on our Bidimensionality Theory, which was the topic of my Ph.D. thesis; see below for more info). Regarding my industry experience, since January 2019, I am also an Amazon Scholar with Amazon Sponsored & Display Advertisement Division. Before I was a Visiting Research Scientist at Google AI- Research, New York City, in 2017-2018. I was also a Visiting Researcher in Microsoft Research at New England and New York City Labs in 2016. Before joining UMD in 2010, I was a Senior Researcher in the Algorithms and Theoretical Computer Science group at AT&T Labs - Research from 2007. During my doctoral studies, I also worked as an intern at Microsoft Research Theory group in 2004 and Department of Mathematical Sciences at the IBM T.J. Watson Research Center in 2002. Regarding my education, I have been one-year Postdoctoral Fellow in the School of Computer Science at Carnegie Mellon University (with ALADDIN project) and a one-year Postdoctoral Associate in MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) from which I also earned my Ph.D. under supervision of Professor Erik D. Demaine and Professor Tom Leighton (Sep. 2001- May 2005). I got my M.Sc. from the Computer Science Department of the University of Waterloo (Sep. 2000- Sep. 2001) and my B.Sc. from the Computer Engineering Department of Sharif University of Technology (Sep 1997-Sep 2000).

×

Mohsen Bayati

Stanford University

Mohsen received a BS in Mathematics from Sharif University of Technology and a PhD in Electrical Engineering from Stanford University in 2007. His dissertation was on algorithms and models for large-scale networks. During the summers of 2005 and 2006 he interned at IBM Research and Microsoft Research respectively. He was a Postdoctoral Researcher with Microsoft Research from 2007 to 2009 working mainly on applications of machine learning and optimization methods in healthcare and online advertising. In particular, he helped develop a system for predicting hospital patient readmissions and obtained a decision support mechanism for allocating scarce hospital resources to post-discharge care. Their system is currently used in several hospitals across US and Europe. He was a Postdoctoral Scholar at Stanford University from 2009 to 2011 with a research focus in high-dimensional statistical learning. In 2011 he joined Stanford University as a faculty, and since 2015 he is an associate professor of Operations, Information, and Technology at Stanford University Graduate School of Business. He was awarded the INFORMS Healthcare Applications Society best paper (Pierskalla) award in 2014 and in 2016, INFORMS Applied Probability Society best paper award in 2015, and National Science Foundation CAREER award.

×

Nicolas Stier

Facebook

I’m a Director at Facebook Core Data Science. The work of the team leverages innovative research to drive impact to the products, infrastructure and processes at Facebook, the company. We draw inspiration from a rich and diverse set of disciplines including Operations, Statistics, Economics, Mechanism Design, Machine Learning, Experimentation, Algorithms, and Computational Social Science (in no particular order). Between 2014 and 2017, I supported the Economics, Algorithms and Optimization team, which is one the areas of focus of Core Data Science. Prior to joining Facebook, I was an Associate Professor at the Decision, Risk and Operations Division of Columbia Business School and at the Business School of Universidad Torcuato Di Tella. I received a Ph.D. degree from the Operations Research Center of the Massachusetts Institute of Technology.

×

Rad Niazadeh

University of Chicago

Rad Niazadeh is an Assistant Professor of Operations Management at The University of Chicago Booth School of Business. Prior to joining Chicago Booth, he was a visiting researcher at Google Research NYC and a Motwani postdoctoral fellow at Stanford University, Computer Science Department. He obtained his PhD in Computer Science (minored in Applied Mathematics) from Cornell University. He primarily studies the interplay between algorithms, incentives, and learning, with the goal of advancing the theoretical methodologies and foundations of market design and operations in dynamic and complex environments. On the practical side, his research aims to build generic frameworks to design faster and economically efficient online marketplaces, in order to address various operational challenges in modern online platforms, and to address humanitarian needs (such as equity, fairness, and non-discrimination) in operations of govermnetal agencies and e-commerce platforms. Rad has received several awards for his research, including the INFORMS Auctions and Market Design 2021 Rothkopf Junior Researcher Award, the INFORMS Revenue Management and Pricing Dissertation Award (honorable mention), and the Google PhD Fellowship in Market Algorithms.

×

Ramesh Johari

Stanford University

Ramesh Johari is a Professor at Stanford University, with a full-time appointment in the Department of Management Science and Engineering (MS&E), and courtesy appointments in the Departments of Computer Science (CS) and Electrical Engineering (EE). He is an associate director of Stanford Data Science. He is a member of the Operations Research group and the Social Algorithms Lab (SOAL) in MS&E, the Information Systems Laboratory in EE, and the Institute for Computational and Mathematical Engineering. He received an A.B. in Mathematics from Harvard, a Certificate of Advanced Study in Mathematics from Cambridge, and a Ph.D. in Electrical Engineering and Computer Science from MIT.He is the recipient of a British Marshall Scholarship, First Place in the INFORMS George E. Nicholson Student Paper Competition, the George M. Sprowls Award for the best doctoral thesis in computer science at MIT, Honorable Mention for the ACM Doctoral Dissertation Award, the Okawa Foundation Research Grant, the MS&E Graduate Teaching Award, the INFORMS Telecommunications Section Doctoral Dissertation Award, the NSF CAREER Award, and the Cisco Faculty Scholarship. He has served on the program committees of ACM Economics and Computation (including co-chairing the conference in 2019), ACM SIGCOMM, IEEE Infocom, and ACM SIGMETRICS, as the track chair for the Internet Economics and Monetization Track at WWW, and as a founding co-organizer of the Marketplace Innovation Workshop. He served as founding Area Co-Editor of the Revenue Management and Market Analytics Area for Operations Research, and currently serves as associate editor for Management Science (in the Stochastic Models and Simulation area) and Stochastic Systems.

×

René Caldentey

University of Chicago

René Caldentey is the Eli B. and Harriet B. Williams Professor of Operations Management at The University of Chicago Booth School of Business.His primary research interests include stochastic modeling with applications to revenue and retail management, queueing theory, and finance. He has been published in numerous journals including Advances in Applied Probability, Econometrica, Management Science, Mathematics of Operations Research, M&SOM, Operations Research, and Queueing Systems. He serves on the editorial board of Management Science, M&SOM, Naval Research Logistics, Operations Research, and Production and Operations Management. Prior to joining Booth, Caldentey was a professor in the department of Information, Operations, and Management Science at New York University’s Stern School of Business. Before joining NYU Stern in 2001, he worked for the Chilean Central Bank and taught at the University of Chile. Professor Caldentey received his Master of Arts in Civil Industrial Engineering from the University of Chile and his Doctor in Philosophy in Operations Management from MIT.

×

Stefan Wager

Stanford University

I am an assistant professor of Operations, Information, and Technology at the Stanford Graduate School of Business, and an assistant professor of Statistics (by courtesy). My research lies at the intersection of causal inference, optimization, and statistical learning. I am particularly interested in developing new solutions to problems in statistics, economics and decision making that leverage recent advances in machine learning.

×

Vashist Avadhanula

Facebook

I am a research scientist on the Core Data Science team. I have recently graduated from Columbia University with a PhD in decision, risk and operations. I worked on online algorithms for learning choice models. As part of my thesis work, I have also collaborated with Flipkart, India’s largest e-commerce firm in improving their product recommendations. I am broadly interested in developing tractable approaches for decision making under uncertainty. I am particularly excited about applying these ideas to improve business products at Facebook.InterestsMachine learning, optimization, multi-armed bandits and fairness in machine learning

Facilitators

• 

  Amine Allouah

  Facebook

  Research Scientist

  See Bio
• 

  Ali Shameli

  Facebook

  Research Scientist

  See Bio
• 

  Deeksha Sinha

  Facebook

  Research Scientist

  See Bio
• 

  Greg Macnamara

  Facebook

  Research Scientist

  See Bio
• 

  Julian Mestre

  Facebook

  Research Scientist

  See Bio
• 

  Parikshit Shah

  Facebook

  Research Scientist

  See Bio
• 

  Sergey Pupyrev

  Facebook

  Research Scientist

  See Bio
• 

  Vahid Liaghat

  Facebook

  Research Scientist

  See Bio
• 

  Vashist Avadhanula

  Facebook

  Research Scientist

  See Bio
• 

  Xuan Zhang

  Facebook

  Visiting Researcher

  See Bio

×

Amine Allouah

Facebook

I am a Research Scientist on the Economics, Algorithms, and Optimization team, which is one of the areas of focus of Core Data Science. I am really excited about solving real world problems using modeling, optimization, and machine learning tools. Prior to joining Facebook, I graduated from Columbia University with a PhD in decision, risk, and operations. I worked on data-driven pricing and auction as well as on how to design marketplaces. I got my Master’s and undergraduate degrees from Ecole Polytechnique, France. Interests: Data-driven decision making, machine learning, optimization, modeling, auction theory, algorithmic game theory, data science, advertising marketplace. 

×

Ali Shameli

Facebook

Ali is a research scientist on Facebook's Core Data Science Team.

×

Deeksha Sinha

Facebook

Deeksha is a research scientist on the Core Data Science team at Facebook.

×

Greg Macnamara

Facebook

I am a Research Scientist at Facebook Core Data Science. I joined the team after completing my PhD in Operations, Information, & Technology at Stanford GSB. Interests: Statistical learning, stochastic modeling, game-theory, mechanism design, market design.

×

Julian Mestre

Facebook

Julian is a Research Scientist at Facebook. He is broadly interested in the design and analysis of efficient algorithms for combinatorial optimization problems such as covering, scheduling, routing and matching problems.

×

Parikshit Shah

Facebook

I am currently a research scientist at Facebook active in the areas of machine learning, optimization, algorithmic fairness, integrity and ad marketplaces. In the past I have been a research scientist at Yahoo, where I worked on optimization and machine learning with applications to ranking, personalization and computational advertising. I have held research positions at Philips Research, the University of Wisconsin and a visiting position at the Institute for Pure and Applied Mathematics. I received my PhD from MIT EE/CS (Laboratory for Information and Decision Systems) under the supervision of Pablo Parrilo. Earlier, I obtained my master’s degree at Stanford and a bachelor’s degree from the wonderful Indian Institute of Technology, Bombay. I am a recipient of the INFORMS Optimization Society Young Researcher Prize (2016). Interests: Optimization, machine learning, statistics and applications to industry problems.

×

Sergey Pupyrev

Facebook

I am a research scientist at Facebook. I am interested in combinatorial optimization, algorithmic graph theory, computational geometry, and code optimization. My current work focuses on developing algorithmic solutions for improving the efficiency of infrastructure. Prior to joining Facebook, I spent several years working on graph theory and computational geometry at the University of Arizona (USA), Microsoft Research (USA), and the Ural State University (Russia), where I received a PhD in Computer Science. Interests: Graph algorithms, computational geometry, distributed computation, code optimization.

×

Vahid Liaghat

Facebook

I am interested in the general area of algorithm design for combinatorial optimization problems. My focus is on designing solutions that can leverage learnings from data in a principled way that can provide measurable and concrete guarantees. Prior to joining Facebook, I spent several years working on approximation algorithms and online algorithms at the University of Maryland (where I got my PhD), and at the Stanford University (where I spent my postdoc). Interests: Combinatorial optimization, online algorithms, graph algorithms, algorithmic game theory.

×

Vashist Avadhanula

Facebook

I am a research scientist on the Core Data Science team. I have recently graduated from Columbia University with a PhD in decision, risk and operations. I worked on online algorithms for learning choice models. As part of my thesis work, I have also collaborated with Flipkart, India’s largest e-commerce firm in improving their product recommendations. I am broadly interested in developing tractable approaches for decision making under uncertainty. I am particularly excited about applying these ideas to improve business products at Facebook.InterestsMachine learning, optimization, multi-armed bandits and fairness in machine learning

×

Xuan Zhang

Facebook

Xuan is a visiting researcher on Facebook's Core Data Science Team.