Markov Lecture

In 2005, the Applied Probability Society initiated the annual Markov Lecture, to be delivered in the fall of each year at the INFORMS Annual Meeting. The intent is to both honor the associated speaker and to bring to the APS membership topical work of the highest calibre in our discipline. The Markov Lecturer is selected by the APS prize committee.

2024 Markov Lecturer

Devavrat Shah

Electrical Engineering and Computer Science
Massachusetts Institute of Technology

2024 Markov Lecture Discussant
Christina Yu

Operations Research and Information Engineering (ORIE)

Cornell University

Title: Various Incarnations of Matrix Completion

Abstract: The objective of matrix completion is to estimate or complete an unknown matrix from its partial, noisy observations. Since its introduction as a model for recommendation systems in the 1990s, it has been central to advances in machine learning, statistics, and applied probability. In this talk, I will discuss a few incarnations of it that arise in the context of time-series analysis, causal inference, reinforcement learning and empirical risk minimization.

* If you gave the lecture or led the discussion in the past and want to share your slides, please contact the APS Webmaster.

Year	Lecturer	Affiliation	Topic	Discussant(s)
2023	Assaf Zeevi	Columbia University	Robustness and Adaptivity Considerations in Bandit Algorithms	N. Bora Keskin
2022	Elchanan Mossel	MIT	Stochastic Models of Information on Networks and Their Inference	Miklos Racz
2021	Darrell Duffie	Stanford University	Fragmenting Financial Markets	Mathieu Rosenbaum
2020	Sem Borst	Eindhoven University of Technology	Load Balancing in Networked Systems: Scalable Algorithms and Resource Pooling Issues	Neil Walton
2019	Laurent Massoulié	INRIA	Finding Structures Planted in Random Graphs	Bruce Hajek Jiaming Xu
2018	Sara van de Geer	ETH Zurich	Adaptive Estimation using Regularized Empirical Risk	Alexandre Belloni
2017	Paul Dupuis	Brown University	Methods for Model Approximation and Optimization in the Presence of Model Uncertainty Using Information Divergences	Markos Katsoulakis
2016	Gareth Roberts	University of Warwick	Piecewise deterministic Markov processes for Monte Carlo	Natesh Pillai Jeff Rosenthal
2015	David Yao	Columbia University	Risk Analytics	Jose Blanchet Paul Glasserman
2014	Peter Glynn	Stanford University	Perspectives on Traffic Modeling	Assaf Zeevi Pierre L'Ecuyer
2013	Martin Reiman	Bell Labs	A Stochastic Programming Based Approach to Assemble-to-Order Inventory Systems	Alan Scheller-Wolf Amy Ward
2012	Jim Dai	Cornell University and Georgia Tech	Stochastic Network Models for Hospital Inpatient Flow Management Part I Part II	Baris Ata Avi Mandelbaum
2011	Francois Baccelli	ENS and INRIA	Routing on Point Processes	David Gramarnik Devavrat Shah
2010	Ward Whitt	Columbia University	Multiserver Queues with Time-Varying Arrival Rates	Galit Yom-Tov Bill Massey
2009	John N. Tsitsiklis	MIT	Information Aggregation and Consensus in Networks Part I Part II Part III	Ben Van Roy Vivek Borkar
2008	Steve Shreve	CMU	Mixing Models to Capture Stock Price Volatility	Paul Glasserman Ronnie Sircar
2007	Ruth Williams	UCSD	Stochastic Networks with Resource Sharing	Kavita Ramanan Mark Squillante
2006	Bruce Hajek	UIUC	On Connections between Network Coding and Stochastic Network Theory: An Introduction	Michael Mitzenmacher R.W.R. Darling
2005	Avi Mandelbaum	Technion -- Israel Institute of Technology	QED Q’s: Quality & Efficiency Driven Telephone Call/Contact Centers	Ger Koole Marty Reiman