Sarath’s note to prospective students:

My group works in several areas of Machine Learning (ML). Our lab publications in the publications page and the research interests of lab members in the people page provide a better summary of the research problems the lab is currently interested in.

For Fall 2022, I am looking for strong Masters and PhD students to work in the following areas (sorted by order of priority): memory-augmented neural networks, learning to learn through natural language interactions, optimization for DL/RL/RNNs, lifelong learning, reinforcement learning.

I will maintain a list of my current research interests here. When you email me, mention the research topics and sub-topics you are interested in.

  • Lifelong Learning - catastrophic forgetting, capacity expansion, dynamic neural architectures, modular neural networks, memory for lifelong learning, class incremental learning, meta-learning, few-shot learning, applications.
  • Reinforcement Learning (RL) - lifelong RL, model-based RL, multi-agent RL, generalization in RL, hierarchical reinforcement learning, meta RL, sample efficient RL, RL for drug discovery, language-based RL agents.
  • Memory Augmented Neural Networks (MANNs) - long-term dependency modeling, vanishing gradients, exploding gradients, memory-augmented architectures, deep RNNs, optimization for RNNs, Transformers.
  • Natural Language Processing (NLP) - natural language generation, dialogue systems, knowledge graphs and reasoning over graphs, natural language grounding, learning to learn through natural language interactions.

General Expectations: I expect the students to have a thorough understanding of the basics of ML and DL before applying. You are also expected to have a thorough understanding of RL if you want to do research in RL. In the interviews, I will test your math and ML knowledge. Ideally, you should be familiar with the topics covered in:

You should also have strong Python programming skills. My team uses PyTorch for research and hence you should be proficient in using PyTorch. Some of these skill requirements can be waived if you are an exceptional candidate with a different academic background.

Women and underrepresented minorities are especially encouraged to apply.


  • I am currently not looking for Postdocs.

Ph.D./Masters Students

  • The next batch of Ph.D. and Masters admissions will be for Fall 2022.
  • If you are interested in working with me, you should submit your application to Mila before December 01, 2021. I will only take a look at the applications which mention me as a potential adviser.
  • If you get shortlisted for an interview, you are required to submit an application to Polytechnique Montreal (with my name). Please note that this application submission does not guarantee admission. Your admission is only based on your performance in the interview. Applying to Poly before the interview is to avoid the possibility of future admission/visa delays.

Current Students at Poly and UdeM

  • If you are already a student (undergrad, Masters, Ph.D.) at Poly or UdeM and want me to be your adviser, email me with your detailed CV, all the transcript copies, and a summary of your research interests.
  • If you want me to read your email, your email should have “[RESEARCH_APPLICATION_INTERNAL]” in the subject.

Interns and Visiting Students

  • I am currently not taking any interns.