Spyros Zoumpoulis

Spyros Zoumpoulis is an assistant professor of Decision Sciences at INSEAD. His research is on prescriptive analytics for optimal personalized decision making, with applications in marketing, healthcare, public health policy, and revenue management. In his work, he develops algorithms for optimal personalized decision making, and studies how to learn and evaluate personalized policies using data from randomized experiments.

More broadly, he is interested in problems at the interface of information, learning, and decisions. His research has appeared in leading management science academic journals such as Management Science and Operations Research.

Spyros has worked with companies including Microsoft, LinkedIn, IBM, Oracle, and Accenture and has served on the advisory board of start-ups in the areas of his expertise. At INSEAD, he teaches the MBA core course on uncertainty, data and judgment, the MBA electives on data science for business and decision models, the MBA business foundations course on quantitative methods, the PhD courses on probability and statistics, and the INSEAD-Sorbonne business foundations course on uncertainty, data and judgment. He has won the Dean's Commendation for Excellence in MBA Teaching award numerous times and has been nominated for the best MBA elective professor award.

Spyros received the B.S., M.Eng., and Ph.D. degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology.

Research

Working papers

Quantifying and realizing the benefits of targeting for pandemic response, S. Camelo, D. F. Ciocan, D. A. Iancu, X. S. Warnes, S. I. Zoumpoulis. Under 2nd-round review after Major revision at Manufacturing & Service Operations Management [Previous version]
Learning personalized treatment strategies with predictive and prognostic covariates in adaptive clinical trials, A. Alban, S. E. Chick, S. I. Zoumpoulis. Major Revision at Management Science
A sample size calculation for training and certifying targeting policies, D. Simester, A. Timoshenko, S. I. Zoumpoulis. Major Revision at Management Science
Using the timing of past responses in targeting models to address dilution, T. Evgeniou, D. Simester, A. Timoshenko, S. I. Zoumpoulis.
Using past responders to target non-responders, T. Evgeniou, D. Simester, A. Timoshenko, S. I. Zoumpoulis.

Journal publications

Optimal pricing and introduction timing of technology upgrades in subscription-based services, I. A. Kash, P. B. Key, S. I. Zoumpoulis. Accepted in Operations Research [Previous version]
The effect of drone strikes on civilian communication: Evidence from Yemen, F. Christia, S. I. Zoumpoulis, M. Freedman, L. Yao, A. Jadbabaie. Political Science Research and Methods, 2021 [Online appendix]
Efficiently evaluating targeting policies: Improving on champion vs. challenger experiments, D. Simester, A. Timoshenko, S. I. Zoumpoulis. Management Science, Vol. 66(8), pp. 3412-3424, 2020 [Previous version and Appendix with theoretical results]
Targeting prospective customers: Robustness of machine learning methods to typical data challenges, D. Simester, A. Timoshenko, S. I. Zoumpoulis. Management Science, Vol. 66(6), pp. 2495-2522, 2020 [Appendix]
Coordination with local information, M. A. Dahleh, A. Tahbaz-Salehi, J. N. Tsitsiklis, S. I. Zoumpoulis. Operations Research, Vol. 64(3), pp. 622-637, 2016
The value of field experiments, J. Li, P. Rusmevichientong, D. Simester, J. N. Tsitsiklis, S. I. Zoumpoulis. Management Science, Vol. 61(7), pp. 1722-1740, 2015 [e-companion]
Right-protected data publishing with provable distance-based mining, S. I. Zoumpoulis, M. Vlachos, N. M. Freris, C. Luchhese. IEEE Transactions on Knowledge and Data Engineering, Vol. 26 (8), pp. 2014-2028, 2013
On decentralized detection with partial information sharing among sensors, O. Patrick Kreidl, J. N. Tsitsiklis, S. I. Zoumpoulis. IEEE Transactions on Signal Processing, Vol. 59(4), pp. 1759-1765, 2011

Media (selected)

Why targeted lockdowns coule be better for everyone, F. Ciocan, D. Iancu, S. I. Zoumpoulis. The Business Times Singapore; The Jakarta Post; INSEAD Knowledge, 2021
Run field experiments to make sense of your big data, S. I. Zoumpoulis, D. Simester, T. Evgeniou. Harvard Business Review Blog, 2015

Conference Proceedings

Expected value of information methods for contextual ranking and selection: Clinical trials and simulation optimization, A. Alban, S. E. Chick, S. I. Zoumpoulis. Proceedings of the 2021 Winter Simulation Conference
Optimal pricing and introduction timing of new virtual machines, I. Kash, P. Key, S. I. Zoumpoulis. Proceedings of the 2018 ACM Conference on Economics and Computation, 26% acceptance [Full paper]
The value of temporal data for learning of influence networks: A characterization via Kullback-Leibler divergence, M. A. Dahleh, J. N. Tsitsiklis, S. I. Zoumpoulis. Proceedings of the 54th IEEE Conference on Decision and Control, Osaka, Japan, December 2015
The value of temporally richer data for learning of influence networks, M. A. Dahleh, J. N. Tsitsiklis, S. I. Zoumpoulis. Book chapter in Web and Internet Economics, Lecture Notes in Computer Science Volume 8877, pp. 322-323, Proceedings of the 10th Conference on Web and Internet Economics, 30% acceptance, 2014 [Full paper]
Coordination with local information, M. A. Dahleh, A. Tahbaz-Salehi, J. N. Tsitsiklis, S. I. Zoumpoulis. ACM Performance Evaluation Review, Proceedings of the joint Workshop on Pricing and Incentives in Networks and Systems (W-PIN + NetEcon within ACM Sigmetrics), 30% acceptance, 2013
On global games of regime change in networks, M. A. Dahleh, A. Tahbaz-Salehi, J. N. Tsitsiklis, S. I. Zoumpoulis. Proceedings of the 6th Workshop on the Economics of Networks, Systems, and Computation (NetEcon), within 12th ACM Conference on Electronic Commerce, 35% acceptance, 2011 [Full paper]
Decentralized detection in network sensor architectures with feedback, O. Patrick Kreidl, J. N. Tsitsiklis, S. I. Zoumpoulis. Proceedings of the 48th Annual Allerton Conference on Communications, Control and Computing, 2010