Portrait of Sarath Chandar

Sarath Chandar

Core Academic Member
Canada CIFAR AI Chair
Associate Professor, Polytechnique Montréal, Department of Computer Engineering and Software Engineering
Adjunct Professor, Université de Montréal, Department of Computer Science and Operations Research
Indian Institute of Technology Madras
Research Topics
AI Alignment
Deep Learning
Explainable AI (XAI)
Foundation Models
Interpretability
Large Language Models (LLM)
Lifelong Learning
Medical Machine Learning
Multi-Agent Systems
Natural Language Processing
Online Learning
Optimization
Recurrent Neural Networks
Reinforcement Learning
Representation Learning
Transfer Learning
Trustworthy AI

Biography

Sarath Chandar is an associate professor at Polytechnique Montreal's Department of Computer and Software Engineering, where he leads the Chandar Research Lab. He is also a Core Academic Member at Mila – Quebec Artificial Intelligence Institute and holds a Canada CIFAR AI Chair and the Canada Research Chair in Lifelong Machine Learning.

Chandar’s research interests include lifelong learning, deep learning, optimization, reinforcement learning and natural language processing. To promote research in lifelong learning, Chandar created the Conference on Lifelong Learning Agents (CoLLAs) in 2022, for which he served as program chair in 2022 and 2023.

He has a PhD from Université de Montréal and an MSc (By Research) from the Indian Institute of Technology Madras.

Current Students

Master's Research - Université de Montréal
Master's Research - Polytechnique Montréal
PhD - Polytechnique Montréal
Co-supervisor :
Collaborating researcher
Master's Research - McGill University
Master's Research - Polytechnique Montréal
PhD - Polytechnique Montréal
Principal supervisor :
PhD - Polytechnique Montréal
PhD - Université de Montréal
Principal supervisor :
PhD - Université de Montréal
PhD - Polytechnique Montréal
PhD - Université de Montréal
Postdoctorate - Polytechnique Montréal
PhD - Polytechnique Montréal
Master's Research - Université de Montréal
Co-supervisor :
Postdoctorate - Université de Montréal
PhD - Polytechnique Montréal
Postdoctorate - Polytechnique Montréal
Principal supervisor :
Research Intern - Polytechnique Montréal
Research Intern - Polytechnique Montréal
PhD - Université de Montréal
PhD - Polytechnique Montréal
PhD - Université de Montréal
Collaborating researcher - Polytechnique Montréal Montreal
PhD - Université de Montréal
PhD - Polytechnique Montréal
Master's Research - Polytechnique Montréal
PhD - Polytechnique Montréal
Master's Research - Université de Montréal
PhD - Polytechnique Montréal
Collaborating researcher
Research Intern - Polytechnique Montréal
Postdoctorate - Université de Montréal
PhD - Polytechnique Montréal
PhD - Polytechnique Montréal
PhD - Polytechnique Montréal

Publications

GuessWhat?! Visual Object Discovery through Multi-modal Dialogue
We introduce GuessWhat?!, a two-player guessing game as a testbed for research on the interplay of computer vision and dialogue systems. The… (see more) goal of the game is to locate an unknown object in a rich image scene by asking a sequence of questions. Higher-level image understanding, like spatial reasoning and language grounding, is required to solve the proposed task. Our key contribution is the collection of a large-scale dataset consisting of 150K human-played games with a total of 800K visual question-answer pairs on 66K images. We explain our design decisions in collecting the dataset and introduce the oracle and questioner tasks that are associated with the two players of the game. We prototyped deep learning models to establish initial baselines of the introduced tasks.
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
Iulian V. Serban
Alberto García-Durán
Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances. Howeve… (see more)r, to this date, there are no large-scale question-answer corpora available. In this paper we present the 30M Factoid Question-Answer Corpus, an enormous question answer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions. The produced question answer pairs are evaluated both by human evaluators and using automatic evaluation metrics, including well-established machine translation and sentence similarity metrics. Across all evaluation criteria the question-generation model outperforms the competing template-based baseline. Furthermore, when presented to human evaluators, the generated questions appear comparable in quality to real human-generated questions.