People

Eric Auerbach

Eric Auerbach

(Northwestern, Economics)

Eric Auerbach focuses on developing statistical tools for analyzing models with network data (i.e, models where groups of agents are connected in an economically meaningful way). In economics, it is natural to interpret networks as random since connections are often the result of human decision making, which can be heterogeneous and indeterminate. However, standard statistical tools are often difficult to implement because the number of possible connections between agents can be intractably large. His research emphasizes methods for reducing this complexity without imposing too many restrictions on how agents might be connected. He is particularly interested in nonparametric and semiparametric methods and in understanding how they might be adapted to a network setting.

Randall Berry

Randall Berry

(Northwestern, ECE)

Prof. Berry’s research covers resource allocation problems that arise in networked systems ranging from communication networks to social networks. This work uses mathematical models to gain insights into such systems and draws on tools from stochastic modeling, optimization, economics and algorithms. Specific topics of current interest include developing distributed resource allocation technicques for wireless networks, dynamic spectrum sharing and wireless spectrum policy, understanding the role of incentives in network security and modeling learning and adoption in social networks.

Ivan Canay

Ivan Canay

(Northwestern, Economics)

Ivan Canay’s research is in econometric theory, with specific interests in developing tests for assessing models that are partially identified. His most recent work includes methods for inference on individual components of parameters defined by moment inequalities, inference in models with few clusters via randomization tests, and inference under covariate adaptive randomization. He is currently an associate editor of the Journal of Econometrics, the Journal of Business and Economic Statistics, and the Econometrics Journal.

Ozan Candogan

Ozan Candogan

(University of Chicago, Operations Management, Booth)

Ozan Candogan is an Associate Professor of Operations Management at Chicago Booth. Prior to joining Booth, he was an Assistant Professor at the Fuqua School of Business where he was a member of the Decision Sciences area. He received his Ph.D. and M.S. degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology.

Professor Candogan’s research is primarily motivated by the role that social and economic networks play in different operational settings. It has three complementary branches:
(i) Impact of networks on operational decisions.
(ii) Complex social and economic systems and new methodologies.
(iii) Pricing and revenue management problems in service systems.

Ozan Candogan was a finalist in the 2013 George Nicholson Student Paper Competition. He is also a recipient of the 2012 Microsoft Research Ph.D. Fellowship, and 2009 Siebel Scholarship. He teaches Operations Management, as well as a PhD course on Networks at Booth.

Chao Gao

Chao Gao

(University of Chicago, Statistics)

Chao Gao’s research interests are spread across nonparametric and high-dimensional statistics, network analysis, Bayes theory and robust statistics.

Dongning Guo

Dongning Guo

(Northwestern, ECE)

Dongning Guo’s research interests lie in communications, networking, information theory, and signal processing. His current projects concern next generation cellular networks, the Internet of Things, information theory of many-user systems, millimeter wave and full-duplex communication, as well as experimentation using software-defined radios. Dongning received his Master’s and Ph.D. in Electrical Engineering from Princeton University.

Varun Gupta

Varun Gupta

(University of Chicago, Operations Management, Booth)

Varun Gupta is an Associate Professor of Operations Management at the Booth School of Business at the University of Chicago. He received his PhD in Computer Science from Carnegie Mellon University in Pittsburgh, PA and holds a Bachelor of Technology in Computer Science and Engineering from the Indian Institute of Technology in New Delhi, India. His research interests include stochastic processes and applied probability, algorithm design and analysis, learning and control in non-stationary environments, queueing and scheduling theory, and mechanism design. He is particularly interested in applications of these tools to cloud computing infrastructure, healthcare operations, two-sided markets for freight and other 3PL, and influence propagation in social networks.
Jason Hartline

Jason Hartline

(Northwestern, CS)

Prof. Hartline’s research introduces design and analysis methodologies from computer science to understand and improve outcomes of economic systems. Optimal behavior and outcomes in complex environments are complex and, therefore, should not be expected; instead, the theory of approximation can show that simple and natural behaviors are approximately optimal in complex environments. This approach is applied to auction theory and mechanism design in his graduate textbook Mechanism Design and Approximation (http://www.jasonhartline.com/MDnA/) which is under preparation.

Joel Horowitz

Joel Horowitz

(Northwestern, Economics)

Joel Horowitz’s research is concerned with estimation and inference under weak assumptions about the process that generates the available data. It is also concerned with improving the accuracy of inference with samples of practical size and with using shape restrictions provided by economic theory to improve estimation and inference. He is a former co-editor of Econometrica and Econometric Theory, a Fellow of the Econometric Society and of the American Statistical Association, and an elected member of the International Statistical Institute.

Samir Khuller

Samir Khuller

(Northwestern, CS)

Samir Khuller is chair and Peter and Adrienne Barris Professor in the Department of Computer-Science at Northwestern University. Prior to joining Northwestern’s newly-established department of computer science in March 2019, Khuller spent twenty-nine years at the University of Maryland College Park where he held the Elizabeth Stevinson Iribe Chair, and spearheaded the development of the Brendan Iribe Center for Computer Science and Engineering.

Khuller’s research interests are centered on graph algorithms, discrete optimization and computational geometry. He has published over 200 journal and conference papers as well as book chapters on these topics. He is a recipient of the National Science Foundation’s Career Development Award, a Google Research Award as well as the University of Maryland’s Distinguished Scholar Teacher Award. In 2016, his work on Connected Dominating Sets (with Sudipto Guha) received the European Symposium on Algorithms inaugural Test of Time Award.

Khuller has served as editor for several journals including Algorithmica, the International Journal on Foundations of Computer Science and Networks. He has also served on numerous program committees including SODA 1997, APPROX 1999, APPROX 2000 (chair), STOC 2003, PODS 2006, SODA 2007, APPROX 2010, ESA 2010, STOC 2013, SPAA 2017. A member of the ESA Steering Committee (2012-2016), he chaired the 2019 MAPSP Scheduling Workshop and will serve as the Chair of SIGACT from 2018-2021.

Khuller received his M.S and Ph.D from Cornell University in 1989 and 1990, respectively, under the supervision of Vijay Vazirani. His completed his undergraduate degree in computer science at the Indian Institute of Technology (Kanpur) in 1986.

Konstantin Makarychev

Konstantin Makarychev

(Northwestern, CS)

Konstantin Makarychev is an Associate Professor of Computer Science at Northwestern University. He is interested in designing efficient algorithms for computationally hard problems. The aim of his research is to introduce new core techniques and design general principles for developing and analyzing algorithms that work in theory and practice. His research interests include approximation algorithms, beyond worst-case analysis, theory of machine learning, and applications of high-dimension geometry in computer science.
 
Before joining Northwestern, Konstantin Makarychev was a researcher at Microsoft and IBM Research Labs. He graduated from Princeton University in 2007.
Yury Makarychev

Yury Makarychev

(TTI Chicago, CS)

Yury Makarychev received his MS degree in Mathematics from Moscow State University in 2001 and his PhD degree in Computer Science from Princeton University in 2008. He spent the following two years as a Postdoctoral Researcher at Microsoft Research in Redmond, WA and Cambridge, MA.

Yury’s research interests include combinatorial optimization, approximation algorithms, semi-definite programming, and low-distortion metric embedding. He has recently worked on developing approximation algorithms for unique games, constraint satisfaction, graph partitioning, and vertex ordering problems, investigating tradeoffs between local and global properties of metric spaces, and studying lift-and-project hierarchies of mathematical relaxations.

Dr. Makarychev also has a personally maintained website which can be found at http://www.ttic.edu/makarychev.

Nathan Srebro

Nathan Srebro

(TTI Chicago, CS)

Dr. Srebro is interested in statistical and computational aspects of machine learning, and the interaction between them. He has done theoretical work in statistical learning theory and in algorithms, devised novel learning models and optimization techniques, and has worked on applications in computational biology, text analysis and collaborative filtering. Before coming to TTIC, Dr. Srebro was a postdoctoral fellow at the University of Toronto and a visiting scientist at IBM Research.

Dr. Srebro also has a personally maintained website which can be found at http://www.ttic.edu/srebro.

Aravindan Vijayaraghavan

Aravindan Vijayaraghavan

(Northwestern, CS)

Aravindan Vijayaraghavan’s research interests are broadly in the field of Theoretical Computer Science, particularly, in designing efficient algorithms for problems in Combinatorial Optimization and Machine Learning. He is also interested in using paradigms that go Beyond Worst-Case Analysis to obtain good algorithmic guarantees.

Zhaoran Wang

Zhaoran Wang

(Northwestern, IEMS)