Lina Mezghani

I am a final-year PhD student at FAIR Paris and Inria (Thoth team). I am advised by Piotr Bojanowski and Karteek Alahari. My research focuses on making reinforcement learning algorithm rely less on supervision, by exploiting self-supervised and unsupervised methods.

I received an MSc/BSc in Applied Mathematics & Computer Science from École polytechnique (diplôme d'Ingénieur) and an MSc in Machine Learning from École Normale Supérieure Paris-Saclay (Master MVA). During my studies I was a software engineer intern at Pilot Vision, Stockholm, and I visited Cornell Tech University working on computer vision with Prof. Mor Naaman.

Email / CV / Google Scholar / LinkedIn / GitHub

Research

	Learning Goal-Conditioned Policies Offline with Self-Supervised Reward Shaping Lina Mezghani, Sainbayar Sukhbaatar, Piotr Bojanowski, Alessandro Lazaric, Karteek Alahari CoRL, 2022 paper / code / project page / bibtex
	Walk the Random Walk: Learning to Discover and Reach Goals Without Supervision Lina Mezghani, Sainbayar Sukhbaatar, Piotr Bojanowski, Karteek Alahari Agent Learning in Open-Endedness (ALOE) Workshop, ICLR, 2022 (oral) paper / bibtex
	Memory-Augmented Reinforcement Learning for Image-Goal Navigation Lina Mezghani, Sainbayar Sukhbaatar, Thibaut Lavril, Oleksandr Maksymets, Dhruv Batra, Piotr Bojanowski, Karteek Alahari IROS, 2022 paper / data / bibtex
	Learning to Visually Navigate in Photorealistic Environments Without any Supervision Lina Mezghani, Sainbayar Sukhbaatar, Arthur Szlam, Armand Joulin, Piotr Bojanowski arXiv, 2020 paper / bibtex

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Credits to Jon Barron for the template.