Vahid Balazadeh

I’m a Ph.D. student in Computer Science at the University of Toronto, supervised by Rahul G. Krishnan. I’m interested in understanding the concepts and mechanisms that help humans in optimal decision-making, especially in healthcare. To this end, I’m working on causality and its relationship with machine learning, as well as reinforcement learning from offline observations. In particular, my research involves causal estimation and partial identification of causal effects in high-dimensional data using machine learning. I also like to explore ideas from causality, such as causal representation learning, to design interpretable machine learning methods that are robust to out-of-distribution data.

Before starting my Ph.D., I was a research intern at MPI-SWS, where I had the chance to work with Manuel Gomez Rodriguez on human-machine collaboration in reinforcement learning.

I have earned a double major B.Sc. in Computer Engineering and Mathematics from the Sharif University of Technology.

Publications

Partial Identification of Treatment Effects with Implicit Generative Models

Vahid Balazadeh, Vasilis Syrgkanis, and Rahul G. Krishnan

Advances in Neural Information Processing Systems (NeurIPS) 2022

PDF Code
Learning to Switch Among Agents in a Team via 2-Layer Markov Decision Processes

Vahid Balazadeh, Abir De, Adish Singla, and 1 more author

Transactions on Machine Learning Research (TMLR) 2022

PDF Code

Preprints

Reinforcement Learning Under Algorithmic Triage

Eleni Straitouri, Adish Singla, Vahid Balazadeh, and 1 more author

arXiv preprint arXiv:2109.11328 2021

HTML
OCDaf: Ordered Causal Discovery with Autoregressive Flows

Hamidreza Kamkari, Vahid Zehtab, Vahid Balazadeh, and 1 more author

arXiv preprint arXiv:2308.07480 2023

HTML