Romina Abachi

I have recently completed my MSc. from Computer Science (Artificial Intelligence) 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.

Previously, 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.

Email / CV / Google Scholar / Github

Research

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 PDF
	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 arXiv

Miscellaneous

	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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