riashat dot islam dot 93 at gmail dot com
I am a final year PhD student in Machine Learning and Reinforcement Learning where I am supervised by Doina Precup in the Reasoning and Learning Lab at McGill University and a part of Mila - Quebec AI Institute. I am currently a Student Researcher at Microsoft Research NYC and Microsoft Research Montreal, where I work with John Langford, Alex Lamb, Dipendra Misra, Remi Tachet Des Combes, Romain Laroche and Harm Van Seijen. Previously, I was also a research intern at Microsoft Research Montreal, working with Phil Bachman. I also collaborate closely with Anirudh Goyal and Yoshua Bengio. For past and present collaborators, please see the list here.
I completed my Masters at University of Cambridge in the MPhil Machine Learning, Speech and Language Technology Program, under the supervision of Zoubin Ghahramani and Yarin Gal in the Cambridge Machine Learning Group. My Masters was funded by the Cambridge Commonwealth and International Trust, and I was a member of St John's College, Cambridge.
Prior to that I studied Electronic and Electrical Engineering at University College London working under supervision of John Shawe-Taylor and Guy Lever from the Centre of Computational Statistics and Machine Learning and Gatsby Computational and Neuroscience Unit at UCL. I also had a great fortune working with David Silver from Google DeepMind during my undergraduate thesis.
I was also a summer research student at California Institute of Technology (Caltech) in the Summer Undergraduate Research Fellowship program (SURF) where I worked under supervision of Richard Murray in the Control and Dynamical Systems Lab, under a project in collaboration with the NASA Jet Propulsion Laboratory (JPL), and Keck Institute for Space Studies. Before that, I was a summer research student in the Machine Learning Group at Johns Hopkins University working under supervision of Suchi Saria.
Google Scholar /
- (2017 - 2022) PhD, McGill University (Supervisor : Doina Precup)
- (2016 - 2017) MPhil, University of Cambridge (Supervisor : Zoubin Ghahramani)
- (2011 - 2015) Undergraduate, University College London, UCL (Supervisor : John Shawe-Taylor)
Research Work Experience
- (Jan'22 - Present) Student Researcher, Microsoft Research
(Advisor : J. Langford, H. Seijen, R.T.D Combes)
- (Jun'21 - Nov'21) Research Intern, Microsoft Research NYC (Advisor : John Langford)
- (Jan'18 - Feb'19) Student Researcher, Microsoft Research Montreal (Advisor : Phil Bachman)
- (Oct'17 - Dec'17) Research Intern, Microsoft Research Montreal, Maluuba (Advisor : Phil Bachman)
- (Jun'15 - Oct'15) Research Intern, Caltech Surf Program (Advisor : Richard Murray)
- (Jun'14 - Oct'14) Research Intern, Johns Hopkins SRE Program (Advisor : Suchi Saria)
- (Jun'13 - May'14) Industrial Placement Intern, J.P. Morgan
- (Jun'12 - Oct'12) Intern, Deutsche Bank
PcLast : Discovering Plannable Continuous Latent States
Anurag Koul, Shivakanth Sujit, Shaoru Chen, Ben Evans, Lili Wu, Riashat Islam
, Raihan Seraj, Yonathan Efroni, Miroslav Dudik, John Langford, Alex Lamb
In Submission, ICLR 2024
Agent-Controller Representations : Principled Offline RL with Rich Exogenous Information
, Manan Tomar, Alex Lamb, Yonathan Efroni, Hongyu Zang, Aniket Didolkar, Dipendra Misra, Xin Li, Harm Van Seijen, Remi Tachet Des Combes, John Langford.
Representation Learning in Deep RL with Discrete Information Bottleneck
, Hongyu Zang, Manan Tomar, Aniket Didolkar, Md Mofijul Islam, Anirudh Goyal, Samin Yeasar Arnob, Xin Li, Tariq Iqbal, Nicolas Hess, Alex Lamb.
Offline Policy Optimization with Variance Regularization
, Samarth Sinha*, Homanga Bharadhwaj, Samin Yeasar Arnob, Zhuoran Yang, Zhaoran Wang, Animesh Garg, Lihong Li, Doina Precup
Openreview 2021 (ICLR Submission)
, The Optimization Foundations for Reinforcement Learning workshop, NeurIPS 2021
- Deep Reinforcement Learning
- Latent State Discovery (State Abstraction and Representation Learning) and Exploration
- Offline Reinforcement Learning
- Optimization Methods for RL
- Probabilistic Models and Bayesian Deep Learning
- Deep Generative Models, Approximate and Bayesian Inference
- Active Learning