Along with Prof. Samuel Kaski, I have been involved in helping PhD candidates get one of the highest university fellowships for graduate students - the Dean’s Doctoral Scholarship. Hence maybe now I can say something about what is required to get this :)

IMHO, doing a Ph.D. is an extremely important decision in life and it can have significant ramifications on everything about oneself. Probably naturally, the Ph.D. admission is a very long and complex process and it’s very important to start preparing the documents as early as possible. At our department, to have a chance at any of the important fellowships it is vitally important that the candidate has significant familiarity with their intended research field - just excellence in studies thus far is unlikely to cut it - I wrote a Ph.D. thesis on deep-learning while not knowing anything about neural nets till the middle of my first year of Ph.D. - unlikely this situation will fly here :D

So, to start a Ph.D. with me in the September of any year, ideally start having research discussions with me during the summer the year before ~ 1.5 years before the application deadline. I am assuming that at that point you already have demonstrable excellence in advanced maths/statistics/theoretical physics or E.C.E. courses - could be via top grades in these mathematically intensive courses. To maximize your chances, submit your applications (with the required TOEFL/IELTS scores) in December the year before - or definitely by March of the same year - though by then you would have already missed out on the chance to qualify for the President’s Doctoral Scholar Award. This page lists all the funding possibilities that you can explore for doing your Ph.D. at our department.

(In particular, note if you qualify for any of the ``External Scholarships” listed therein.)

Almost Necessary Prerequisite Reading for Starting a PhD in Our Group

The students are expected to have evidence of satisfying at least 2 or more of the following criteria,

(a) Know basics of stochastic optimization, like Chapter-6 here

(b) Know basic machine learning theory, like these lectures

(c) Have top grades in any one of the topics, like PDE theory/advanced analysis/mathematical statistics/differential geometry/probability theory/Fourier analysis

and have some familiarity with the lectures here. The lectures above are from a group in ETH Zurich with whom we have a close relationship.