Portrait of Sarath Chandar

Sarath Chandar

Core Academic Member
Canada CIFAR AI Chair
Assistant 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
Deep Learning
Medical Machine Learning
Natural Language Processing
Online Learning
Optimization
Recurrent Neural Networks
Reinforcement Learning
Representation Learning

Biography

Sarath Chandar is an assistant 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
Principal supervisor :
Independent visiting researcher - no
PhD - Université de Montréal
PhD - Polytechnique Montréal
PhD - Université de Montréal
Principal supervisor :
Collaborating Alumni - Université de Montréal
Principal supervisor :
Research Intern - Université de Montréal
Principal supervisor :
PhD - Polytechnique Montréal
Master's Research - Université de Montréal
Independent visiting researcher - NA
Master's Research - Polytechnique Montréal
PhD - Polytechnique Montréal
Co-supervisor :
PhD - Polytechnique Montréal
Postdoctorate - Polytechnique Montréal
PhD - Université de Montréal
PhD - Université de Montréal
PhD - Université de Montréal
Master's Research - Université de Montréal
Postdoctorate - Université de Montréal
Co-supervisor :
PhD - Université de Montréal
Principal supervisor :
Independent visiting researcher
Master's Research - Université de Montréal
PhD - Polytechnique Montréal
Co-supervisor :
Master's Research - Université de Montréal
PhD - Polytechnique Montréal
PhD - Polytechnique Montréal
PhD - Polytechnique Montréal
PhD - Polytechnique Montréal

Publications

Chaotic Continual Learning
Touraj Laleh
Mojtaba Faramarzi
Training a deep neural network requires the model to go over training data for several epochs and update network parameters. In continual le… (see more)arning, this process results in catastrophic forgetting which is one of the core issues of this domain. Most proposed approaches for this issue try to compensate for the effects of parameter updates in the batch incremental setup in which the training model visits a lot of samples for several epochs. However, it is not realistic to expect training data will always be fed to model in a batch incremental setup. This paper proposes a chaotic stream learner that mimics the chaotic behavior of biological neurons and does not updates network parameters. In addition, it can work with fewer samples compared to deep learning models on stream learning setup. Our experiments on MNIST, CIFAR10, and Omniglot show that the chaotic stream learner has less catastrophic forgetting by its nature in comparison to a CNN model in continual learning.
Environments for Lifelong Reinforcement Learning
To achieve general artificial intelligence, reinforcement learning (RL) agents should learn not only to optimize returns for one specific ta… (see more)sk but also to constantly build more complex skills and scaffold their knowledge about the world, without forgetting what has already been learned. In this paper, we discuss the desired characteristics of environments that can support the training and evaluation of lifelong reinforcement learning agents, review existing environments from this perspective, and propose recommendations for devising suitable environments in the future.