Soroosh Shafiee (Shafieezadeh Abadeh) joined the faculty at Cornell in July 2023 as an Assistant Professor in the School of Operations Research and Information Engineering. Before that, he held positions as a postdoctoral researcher at the Tepper School of Business at Carnegie Mellon University and the Automatic Control Laboratory at ETH Zurich. He holds 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.
His research interests revolve around optimization under uncertainty, low-complexity decision-making and optimal transport. Most of his 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
Shafieezadeh-Abadeh, Mohajerin Esfahani, and Daniel Kuhn. "Distributionally robust logistic regression." Advances in Neural Information Processing Systems 28 (2015).
Shafieezadeh-Abadeh, Kuhn, and Mohajerin Esfahani. "Regularization via mass transportation." Journal of Machine Learning Research 20, no. 103 (2019): 1-68.
Kuhn, Mohajerin Esfahani, Nguyen, and Shafieezadeh-Abadeh. "Wasserstein distributionally robust optimization: Theory and applications in machine learning." In Operations research & management science in the age of analytics, pp. 130-166. Informs, 2019.
Kılınç-Karzan, Küçükyavuz, Lee, and Shafieezadeh-Abadeh. "Conic mixed-binary sets: Convex hull characterizations and applications." Operations Research, in press, (2023).
Selected Awards and Honors
- Swiss National Science Foundation Early PostDoc Mobility Fellowship, 2020
- PhD Thesis Distinction Award, EPFL, 2020
B.Sc. in Electrical and Computer Engineering, University of Tehran, 2011
M.Sc. in Electrical and Computer Engineering, University of Tehran, 2014
Ph.D. in Operations Research, EPFL, 2020