Internships:
https://mila.quebec/en/admission-process-for-interns/ (specify my name as a supervisor in the text box.)

PhD in Computer Science:

  1. Submit an application to MILA. I will look at this one.
  2. Also submit to McGill Computer Science. This is for official purposes like visa.

Specify my name as a supervisor in the applications. Deadline is December 1st for Mila and December 15th for McGill for Fall admission.

PhD in Linguistics:

  1. Submit an application to McGill Linguistics.

Specify my name as a supervisor in the application. Deadline is December 10th for Fall admission.

Postdocs:
Email me your CV and research interests.

Masters (by Thesis):

  1. If you are a CS student, you should submit an application to both Mila and McGill Computer Science.
  2. If you are linguistics student, submit an application at McGill Linguistics.
  3. No need to email me. I will look at your application material along with all other candidates.

If you don't have any experience in NLP/ML, get admission through Masters (by courses) and then do one of the NLP courses offered at McGill and then reach out to me.

Masters (by courses):
Please do not contact me. Apply either to McGill Computer Science or McGill Linguistics.

Potential Topics

These are some topics that interest me. But I am open to new ideas and topics. In fact, a major portion of your research time will be spent on finding good problems to work on (solutions will come easy if you find a good problem).

  1. Conversational Models, Dialogue Systems, Semantic Parsing, Question Answering
  2. Compositionality and Reasoning
  3. Language in Grounded Environments (text worlds, vision, robotics)
  4. Representation Learning for NLP (Attention Models, Graph Neural Networks, Transfer Learning, Meta-learning)
  5. Symbolic Representations for NLP (induction, Reinforcement Learning)
  6. Probing Deep Learning Models (Bias, Interpretability)
  7. NLP for Software Engineering (code search, generating comments)
  8. Linguistic Representations (universal dependencies, logical Forms, syntax to semantics, CCG, parsing)
  9. Incremental and Pyscholinguistic Models of Language
  10. NLP for low resource languages (Indian languages)

Some of these topics can be co-supervised with other faculty at MILA and McGill.

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