Arushi Jain

I am final year PhD in Computer Science (AI) with Prof Doina Precup at McGill University and Mila Lab, Montreal.

Recently, I interned at Microsoft Research (MSR) in Amsterdam with Elise Van Der Pol, where I worked on Molecular Drug Discovery. Prior to that, I was interning at Meta AI Lab (FAIR) in Paris with Alessandro Lazaric, where I worked on self-supervised framework for integrating exploration and representation in a reward-free zero-shot setting.

Research Summary: My research focuses on reinforcement learning (RL) with an emphasis on improving the reliability, sample efficiency , and safety of RL systems for real-world applications. I am also interested in the personalization potential of LLMs, aiming to develop more robust AI frameworks.

Email  /  CV  / Google Scholar  /  Twitter  /  Github

profile photo
Timeline
Fall 2024

Microsoft Research, Amsterdam

AI4Science Research Intern [Host: Elise Van Der Pol]

Fall 2022

Meta AI Lab, Paris

Research Scientist Intern [Host: Alessandro Lazaric]

Summer 2022

Amazon

AI Research Intern

2019 - Now

McGill University

Ph.D. in Computer Science

Fall 2019

SportLogiQ, Montreal

RL Research Intern [Host: Norm Ferns]

Summer 2018

Borealis AI, Edmonton

Research Scientist Intern [Host: Nidhi Hedge]

2017 - 2019

McGill University

Masters in Computer Science

2016 - 2017

Microsoft Research India

ML Research Fellow

Summer 2015

Amazon

SDE Intern

2012 - 2016

IIIT-Delhi

Bachelor’s Degree in Computer Science

Highlights and News
Research
3DSP GVFExplorer: Adaptive Exploration for Data-Efficient General Value Function Evaluations
Arushi Jain, Josiah P. Hanna, Doina Precup
NeurIPS, 2024
Paper
3DSP Towards Painless Policy Optimization for Constrained MDPs
Arushi Jain, Sharan Vaswani, Reza Babanezhad, Csaba Szepesvari, Doina Precup
UAI and RLDM, 2022
paper / short RLDM paper / code / RLDM poster
3DSP Variance Penalized On-Policy and Off-Policy Actor-Critic
Arushi Jain, Gandharv Patil, Ayush Jain, Khimya Khetarpal, Doina Precup
AAAI, 2021
paper / code / talk / slides / poster
3DSP Safe Option-Critic: Learning Safety in the Option-Critic Architecture
Arushi Jain*, Khimya Khetarpal*, Doina Precup
Knowledge Engineering Review (KER) Journal, 2021. (Cambridge University Press Journal)
Adaptive Learning Agents (ALA) Workshop, ICML, 2018.
paper / code / slides / poster
3DSP Safety using Constraint Variance in Policy-Gradient Methods
Arushi Jain
Master's thesis, McGill University, March 2020.
paper
3DSP Safe Actor-Critic
Arushi Jain*, Ayush Jain, Doina Precup
Safety, Risk and Uncertainty in RL Workshop, UAI, 2018.
Women in ML (WiML) Workshop, NeurIPS , 2018.
paper / / slides / poster
3DSP Safe Hierarchical Policy Optimization using Constrained Return Variance in Options
Arushi Jain*, Doina Precup
RLDM, 2019.
paper
3DSP Learning Options using Constrained Return Variance
Arushi Jain*, Doina Precup
Safety and Robustness in Decision Making Workshop , NeurIPS, 2019.
paper / poster
3DSP Safe Policy Learning with Constrained Return Variance
Arushi Jain*
Graduate Student AI Symposium, Canadian AI Conference, 2019.
Proceeding published in LNAI Series by Springer Verlag.
paper / talk
Reviewer
sym Reviewer, DARL, ICML Workshop ('22)

Reviewer, AISTATS ('22)

Reviewer, ICLR ('22), ML Evaluation Standards Workshop

Reviewer, NeurIPS ('18), WiML Workshop


Source code and style from Jon Barron's website.