I am planning to apply for (NYU, Columbia, Princeton…)
Which top grad programs would I be a strong candidate given a quick summary below?
"I recently graduated from Stony Brook Uni with Applied Math&Statistics (AMS) and Information Systems (ISE) majors. Overall GPA 3.64, AMS GPA 3.59, ISE GPA 3.79.
I acted as the president of Golf Club and had assistantships in two departments as well as being an active member on integrated research project about climate change.
I had some internship/work experiences, mainly investment analyst intern in wall st and ops analyst intern in San Diego. I have been teaching Python in CUNY since start of summer and got extended offer at least until December.
I am currently a Masters student in Stony Brook for AMS but I want to put it away soon and work on Ph.D. instead. If any other info could help, I can share."
For doctoral programs you pick the person, not the school. Only you know who is doing research in the area(s) that interest(s) you. Look at the literature. Reverse engineer where they work. Reach out to them. Good luck.
Admission to PhD programs is all about research fit. Selecting programs to apply to is not strictly about rankings. You need to first identify programs where there are active researchers currently conducting research in the area you want to pursue and where you have relevant research experience. Most importantly you also need to ensure that any researcher you would potentially wish to work with will actually be accepting new students for the upcoming cycle. There’s no point in applying to programs where you are not a fit for the research being conducted or where the researchers who are active in that area are not going to be taking on new students.
Once you have identified programs where you would potentially be a good research fit and where there are researchers actively conducting research in that field/topic, I would reach out to them via email expressing an interest in their work, giving a brief overview of your relevant experience, and enquire if they anticipate accepting new students in the upcoming cycle. That will then further narrow your selections by eliminating any programs where you would have 0 chance of an admit. Subsequent to that then you can further narrow down your list based on whatever other criteria are important to you like ranking, selectivity, and geographic location etc. That will then get you to your final list of target programs. In that list I would recommend including programs with a range of selectivity for which you can roughly use ranking as a proxy. I would not put all your eggs in one basket and apply only to T-20 programs unless they are the only programs you would be willing to attend and have an alternate backup plan in the event you don’t get an admit.
When formulating your applications, your “president of Golf Club” is going to be completely irrelevant. You research experience, your SOP, and your letters of recommendation will be critical for getting an admit.
I have been talking with professors in Montclair State (NJ), and Ohio State that matches my ML/Deep Learning area of focus as well as fintech applicability for quant purposes. Getting those recs from them would give me a better edge to attend Ph.D. programs there, would you agree?
Thanks a lot for your thorough feedback. I didn’t have a general idea of where I stand in terms of my level of qualification but I get it now. As I asked in my other reply:
I have been talking with professors in Montclair State (NJ), and Ohio State that matches my ML/Deep Learning area of focus as well as fintech applicability for quant purposes. Getting those recs/help from them would give me a better edge to attend Ph.D. programs there, would you agree? Or would that still be a reach?
It’s hard to say. The admission process is program dependent. There are some programs where PIs have input over who gets admitted, but more commonly admission decisions are made by a centralized departmental committee. For those types of programs it’s important for you to be able to demonstrate in your SOP how you would be a good fit for research being conducted by specific PIs in the department and that’s as close as bump you will get.