Hi, I’m a math major at the University of Alberta, with a 3.8 gpa.

I’m not anything super special, and I think that my ability to math is pretty subpar and I’m probably not capable of doing a PhD in math. ML is something that always interested me (read a good chunk of Pattern Recognition and Machine Learning and all of Reinforcement Learning over the past year, worked as a MLE for a small stint) but I have 0 research experience.

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My big pluses would be:

• 3.8 GPA in mostly pure math isn’t too shabby (got a B- in Real Analysis II though, which looks very very bad for PhD applications in pure math)
• A+ achieved in the introduction to machine learning course offered at my university
• Currently on the final stretch of an internship at LinkedIn as an Infra SWE, built a cute little compiler which generates linear algebra kernels for sparse tensors
• Going to intern at Jane Street Capital next summer (prestige wise it’s pretty much the best an undergrad could do in terms of SWE)

My big minuses would be:

• 0 research experience
• B- in Real Analysis II (got an A in Real Analysis I though)
• Have not written the GRE (pretty much limits me to masters programs in Canada I think)

I’m mainly concerned with getting a PhD anywhere, I’m not too concerned with getting a PhD at somewhere prestigious. I have 3 semesters left in my degree, all of which are light (I have 4 very difficult math courses left, 1 CS course (compilers), 2 english courses and 4 arts courses which I plan to break into semesters of 4, 4, 3 courses). I have some background in compilers (read a good portion of Engineering A Compiler while I was at LinkedIn in order to do my project) which might be an interesting intersection. I’m taking a Reinforcement Learning class next semester, and I’m preparing to knock it out of the park.

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What should I do within my last 3 semesters in order to maximize my quality of PhD acceptances come the end of my undergrad?