Studying at Mila
What are the projects at Mila?
Most projects involve deep learning, though recently the institute has broadened its interest to wider machine learning including optimization. Projects range from natural language understanding to computer vision, through work on general-purpose advances in algorithms.
Why should someone with good math / systems CS skills work on deep learning?
Deep learning is an exciting new research area. It is young enough that a student with good mathematical aptitude can quickly learn most of the central ideas and begin contributing to the field. Recently, deep learning has proven to be a resource-intensive field, with large neural networks powered by cutting edge GPU-based or distributed implementations making great leaps forward in fields like object recognition and speech recognition. Deep learning research presents an unparalleled opportunity for students with good mathematical skills and / or good CS implementation skills to really make a big impact.
Which professors are looking for students and what kind of projects do they want to work on?
Please refer to the Supervision Request page for more information on the supervision request process.
What are the advantages to being a student at Mila?
- Vibrant intellectual research environment: at Mila we are all excited to advance the state of artificial intelligence. At any given day you can easily find students discussing all kinds of ambitious research ideas. The professors are very involved and engaged with the students, making the research process very open and interactive.
- Quebecois work/life balance: at Mila we do good research, but we don’t work ourselves to death doing it. The culture here is much more laid back than it is at other labs, especially in the US, that publish at the same conferences.
- Lots of computational resources: University of Montreal has the second largest research budget of any university in Canada, and Mila has a stellar track record of attracting grant money. This means Mila students have access to plenty of fast computers with modern GPUs. The institute’s resources are supplemented with access to shared computer clusters across Quebec and Canada in general.
- Funding for conferences: So long as your supervisor have the funding, which is usually the case, you will likely have the opportunity to join a large delegation of Mila students to the top machine learning and deep learning conferences like NeurIPS, ICML, and ICLR. It’s common for Master’s students, second authors, third authors, and even students who haven’t had a paper accepted to get to go to a conference. Professors here strongly encourage attending conferences as an integral part of the life of a researcher. This compares favorably to many other labs where students are discouraged from taking time away from research or where there is rarely funding to send many students.
- Research-based funding: Mila students are generally paid scholarships that are competitive with top US graduate programs. This funding is usually contingent on working on research projects related to one of the lab’s many industrial projects. These projects are often publishable. Most students find this arrangement preferable to some of the arrangements at other universities, where grad student funding is often tied to large amounts of non-research-related work, such as TAing.
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Internships: Mila students commonly do internships at top industrial research groups.