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, where I have worked since 2023. Before joining Cornell, I was a postdoctoral researcher at the Tepper School of Business at Carnegie Mellon University (2021-2023) and the Automatic Control Laboratory at ETH Zurich (2020-2021). I received my PhD from École Polytechnique Fédérale de Lausanne in 2020 and my MSc and BSc in Electrical and Computer Engineering from the University of Tehran in 2014 and 2011.

Research Interests

My research focuses on optimization under uncertainty, low-complexity decision-making and optimal transport. I study the following topics.

• Models and algorithms for robust and distributionally robust optimization

• Statistical and computational complexity of data-driven optimization

• Structured nonconvex optimization with applications in machine learning and finance

Honors and Awards

• Cornell College of Engineering Ralph S. Watts ’72 Excellence in Teaching Award, 2026

• US National Science Foundation CAREER Award, 2026

• Swiss National Science Foundation Early PostDoc Mobility Fellowship, 2020

• EPFL PhD Thesis Distinction Award, 2020

PhD Students

• Khanh-Hung (Bruce) Giang-Tran, 2024-present

• Mohammed Lagmah, 2025-present

Publications and Projects

Journal Publications

• Nash Equilibria, Regularization and Computation in Optimal Transport-Based Distributionally Robust Optimization
  with L. Aolaritei, F. Dörfler, D. Kuhn
  Operations Research, 74(3), 1689-1709, 2026

• Wasserstein Distributionally Robust Estimation in High Dimensions: Performance Analysis and Optimal Hyperparameter Tuning
  with L. Aolaritei and F. Dörfler
  Mathematical Programming, 2026

• A Saddle Point Algorithm for Robust Data-Driven Factor Model Problems
  with S. Khodakaramzadeh, G. de Albuquerque Gleizer, and P. Mohajerin Esfahani
  Automatica, 190, 113095, 2026

• Distributionally Robust Optimization
  with D. Kuhn, W. Wiesemann
  Acta Numerica, 2025

• Conic Mixed-Binary Sets: Convex Hull Characterizations and Applications
  with F. Kılınç-Karzan, S. Küçükyavuz, D. Lee
  Operations Research, 2025

• A Robust Optimization Approach to Network Control Using Local Information Exchange
  with G. Darivianakis, A. Georghiou, J. Lygeros
  Operations Research, 73(5), 2849-2866, 2025

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

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

• Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solution
  with B. Taşkesen and D. Kuhn
  Mathematical Programming, 2023
  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, and P. Mohajerin Esfahani
  Mathematics of Operations Research, 2023

• Regularization via Mass Transportation
  with P. Mohajerin Esfahani and D. Kuhn
  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 Ahmadabadi
  Robotics and Autonomous Systems, 2013

Conference Publications

• Projection-Free Algorithms for Minimax Problems
  with K.-H. Giang-Tran and N. Ho-Nguyen
  International Conference on Machine Learning (ICML), 2026

• Conditional Gradient Methods with Standard LMO for Stochastic Simple Bilevel Optimization
  with K.-H. Giang-Tran, N. Ho-Nguyen
  Advances in Neural Information Processing Systems (NeurIPS), 2025

• Scalable First-order Method for Certifying Optimal k-Sparse GLMs
  with J. Liu, A. Lodi
  International Conference on Machine Learning (ICML), 2025

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

• Outlier-Robust Wasserstein DRO
  with S. Nietert, Z. Goldfeld
  Advances in Neural Information Processing Systems (NeurIPS), 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), 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), 2019

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

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

Book Chapter

• 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

Preprints

• Mean-Covariance Robust Risk Measurement
  with V. A. Nguyen, D. Filipović, and D. Kuhn

• Estimate then Predict: Convex Formulation for Travel Demand Forecasting
  with Y. Kim, G. Zardini, and S. Samaranayake

• Oracle-Based Distributionally Robust Optimization under Optimal Transport Ambiguity Sets
  with G. Chen and S. Fattahi

• GPU-friendly and Linearly Convergent First-order Methods for Certifying Optimal k-sparse GLMs
  with J. Liu and A. Lodi

• Risk-Averse Wasserstein Distributionally Robust Online Learning
  with G. Chen and S. Fattahi

• Projection-Free Algorithms for Nonsmooth Stochastic Convex-Concave Saddle-Point Problems
  with K.-H. Giang-Tran

• Auto-Conditioned Frank-Wolfe Algorithms
  with K.-H. Giang-Tran and N. Ho-Nguyen

Teaching

• ORIE 6300, Mathematical Programming I, 2025-2026

• ORIE 3741/5741, Learning with Big Messy Data, 2025-2026

• ORIE 4570/5570, Reinforcement Learning with Operations Research Applications, 2023-2024

• ORIE 6360, Optimization under Uncertainty, 2024

Profiles and Contact