Soroosh Shafiee

Assistant Professor Operations Research and Information Engineering Cornell University Contact Address: Operations Research and Information Engineering 224 Rhodes Hall Ithaca, NY 14853 United States E-mail: shafiee@cornell.edu

Biography

I am an assistant professor in the School of Operations Research and Information Engineering at Cornell University. Before that, I held positions as a postdoctoral researcher at both the Tepper School of Business at Carnegie Mellon University and the Automatic Control Laboratory at ETH Zurich. I hold a B.Sc. and M.Sc. degree in Electrical Engineering from the University of Tehran and a Ph.D. degree in Management of Technology from École Polytechnique Fédérale de Lausanne.

Research Interest

My primary research interests revolve around optimization under uncertainty, low-complexity decision-making and optimal transport. Most of my works fall into one of the following categories:

• Designing new models and algorithms based on (distributionally) robust optimization

• Statistical and computational complexity analyses of data-driven optimization problems

• Structured nonconvex optimization with application in machine learning and finance

Awards and Scholarships

• EPFL Thesis Distinction Award, 2020

• Swiss National Science Foundation Early PostDoc Mobility Fellowship, 2020

Publications and Projects

Journal Publications

• Distributionally Robust Optimization
  with D. Kuhn, W.Wiesemann
  Acta Numerica (in press)

• A Robust Optimization Approach to Network Control Using Local Information Exchange
  with G. Darivianakis, A. Georghiou, J. Lygeros
  Operations Research (in press)

• Constrained Optimization of Rank-One Functions with Indicator Variables
  with F. Kılınç-Karzan
  Mathematical Programming, 2024

• Conic Mixed-Binary Sets: Convex Hull Characterizations and Applications
  with F. Kılınç-Karzan, S. Küçükyavuz, D. Lee
  Operations Research, 2023

• Discrete Optimal Transport with Independent Marginals is #P-Hard
  with B. Taşkesen and D. Kuhn and K. Natarajan
  SIAM Journal on Optimization, 2023

• Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solution
  with B. Taşkesen and D. Kuhn
  Mathematical Programming, 2022
  Winner of the 2022 INFORMS Optimization Society Student Paper Prize
  Runner-up for the 2022 best student paper prize of the joint European Conference on Stochastic Optimization and the Computational Management Science Conference (ESCO-CMS)

• Bridging Bayesian and Minimax Mean Square Error Estimation via Wasserstein Distributionally Robust Optimization
  with V. A. Nguyen, D. Kuhn, P. Mohajerin Esfahani
  Mathematics of Operations Research, 2021

• Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning
  with D. Kuhn, P. Mohajerin Esfahani, and V. A. Nguyen
  INFORMS TutORials in Operations Research, 2019

• Regularization via Mass Transportation
  with D. Kuhn, and P. Mohajerin Esfahani
  Journal of Machine Learning Research, 2019

• Data-driven Inverse Optimization with Imperfect Information
  with P. Mohajerin Esfahani, G. A. Hanasusanto, D. Kuhn
  Mathematical Programming, 2018

• Evolving Takagi-Sugeno Model Based on Online Gustafson-Kessel Algorithm and Kernel Recursive Least Square Method
  with A. Kalhor
  Evolving Systems, 2016

• Piecewise Linear Spine for Speed-Energy Efficiency Trade-off in Quadruped Robots
  with M. Khoramshahi, H. Jalaly Bidgoly, A. Asaei, A. J. Ijspeert, M. Nili Ahmadabad
  Robotics and Autonomous Systems, 2013

Conference Publications

• Robust Distribution Learning with Local and Global Adversarial Corruptions
  with S. Nietert, Z. Goldfeld
  Conference on Learning Theory (COLT), Edmonton, 2024

• Outlier-Robust Wasserstein DRO
  with S. Nietert, Z. Goldfeld
  Advances in Neural Information Processing Systems (NeurIPS), New Orleans, 2023

• Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization
  with V. A. Nguyen, M.-C. Yue, D. Kuhn, W. Wiesemann
  Advances in Neural Information Processing Systems (NeurIPS), Vancouver, 2019

• Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation
  with V. A. Nguyen, M.-C. Yue, D. Kuhn, W. Wiesemann
  Advances in Neural Information Processing Systems (NeurIPS), Vancouver, 2019

• Wasserstein Distributionally Robust Kalman Filtering
  with V. A. Nguyen, D. Kuhn, P. Mohajerin Esfahani
  (spotlight) Advances in Neural Information Processing Systems (NeurIPS), Montreal, 2018

• Distributionally Robust Logistic Regression
  with P. Mohajerin Esfahani, D. Kuhn
  (spotlight) Advances in Neural Information Processing Systems (NeurIPS), Montreal, 2015

Ongoing Projects

• Mean-Covariance Robust Risk Measurement
  with V. A. Nguyen, D. Kuhn, D. Filipović
  Major Revision in Management Science

• New Perspectives on Regularization and Computation in Optimal Transport-Based Distributionally Robust Optimization
  with L. Aolaritei, F. Dörfler, D. Kuhn
  Major Revision in Operations Research

• Wasserstein Distributionally Robust Estimation in High Dimensions: Performance Analysis and Optimal Hyperparameter Tuning
  with L. Aolaritei and F. Dörfler
  Major Revision in Mathematical Programming

Professional Services

Reviewer for Journals: Mathematical Programming, Operations Research, Management Science, Mathematics of Operations Research, SIAM Journal on Optimization, Journal of Machine Learning Research, SIAM Journal on Mathematics of Data Science, Machine Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Automatic Control, Operations Research Letters, INFORMS Journal on Optimization, European Journal of Operational Research.

Reviewer for Conferences: ICML (2019, 2021), NeurIPS (2019, 2020, 2021), AAAI (2020).

ICCOPT 2022 Summer School on Distributionally Robust Optimization: Slides are available here.

Social Contacts