About Me

I am a PhD candidate in Operations Research at Imperial College Business School, advised by Wolfram Wiesemann and Martin Haugh. Before joining Imperial, I completed my bachelor’s and master’s degrees in Electrical Engineering at Sharif University of Technology.

My research focuses on optimization methods for machine learning, particularly in settings involving distribution shift, limited information, and uncertainty. My interests include domain adaptation, distributionally robust optimization, optimal transport, and interpretable machine learning.

My work aims to develop learning methods that are statistically principled, computationally tractable, and reliable when the available data or model information is imperfect.

News

• “It’s All in the Mix: Wasserstein Classification and Regression with Mixed Features” published in Manufacturing & Service Operations Management (M&SOM).
• Presented our work at the London Operations Research Day (LORD) in Oxford.
• Presented our work at the International Conference on Stochastic Programming (ICSP) in Paris.
• Presented our work at the International Symposium on Mathematical Programming (ISMP) in Montreal.
• “Wasserstein Logistic Regression with Mixed Features” published in Advances in Neural Information Processing Systems (NeurIPS).