Romina Abachi

I am a Computer Science (Artificial Intelligence) PhD student at the University of Toronto and Vector Institute, supervised by Profs. Amir-massoud Farahmand and Sheila McIlraith. Broadly, my research interests span reinforcement learning and practical and safe RL algorithms.

Prior to my doctoral studies, I did my Master's in Electrical and Computer Engineering at the University of Toronto, where I was advised by Amir-massoud Farahmand and Brendan Frey. I did my Bachelor's in Electrical Engineering at the University of Toronto, as well. There, I worked in the Energy Systems lab, where I was advised by Prof. Olivier Trescases.

Previously, I have done internships at Borealis AI, where I studied Adversarial attacks and certified robustness methods, Qualcomm , and Toronto Hydro.

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My research focuses on Model-Based Reinforcement Learning (MBRL) and safe RL algorithms, including risk-sensitive and robust planning. I am interested in algorithms grounded in theory and directed by practical and real-world problems.

Optimistic Risk-Aware Model-based Reinforcement Learning
Romina Abachi, Amir-massoud Farahmand
European Workship in Reinforcement Learning, 2022
VIPer: Iterative Value-Aware Model Learning on the Value Improvement Path
Romina Abachi*, Claas Voelcker*, Animesh Garg, Amir-massoud Farahmand (*equal contribution)
Decision Awareness in Reinforcement Learning Workshop, ICML, 2022
Policy-Aware Model Learning for Policy Gradient Methods
Romina Abachi, Mohammad Ghavamzadeh, Amir-massoud Farahmand
arXiv, 2020
arXiv / code / short version
Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation
Evgenii Nikishin, Romina Abachi, Rishabh Agarwal, Pierre-Luc Bacon
AAAI, 2022
I served as reviewer for:
  • NeurIPS 2022, 2021
  • ICML 2022, 2021
  • AISTATS 2021
TA, CSC412/2506 (Probabilistic Learning and Reasoning) Winter 2022
TA, CSC2547 (Introduction to Reinforcement Learning) Winter 2021
TA, CSC311 (Introduction to Machine Learning) Fall 2020, 2021, Winter 2022

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