Fall 2024 Statistics PhD Domestic Profile Evaluation

[almostsurely]

Hi all, I'd really appreciate some honest feedback on whether the set of schools I'm considering is realistic. (Information may or may not be fudged by some small epsilon for privacy reasons)
 

Undergrad Institution: University of Illinois at Urbana-Champaign
Major: Computer Science
GPA:  (Combined BS/MS degree) 3.88 undergrad, 3.55 grad (senioritis)

Masters Institution: University of Illinois at Urbana-Champaign
Major: Statistics
GPA: 4.0

Research Experience: None
Work Experience: Internships at Microsoft, Google, then 5 years working full-time split between two proprietary trading firms. Very limited modeling work, mostly software development.

Letters of Recommendation: 2 from most recent trading firm, 1 from statistics professor, 1 from CS theory professor for whom I graded papers for 2 semesters

Relevant Classes: lin alg, diffeq, abstract alg, real analysis, undergrad stat theory 1&2, grad mathematical stat, discrete math, algorithms I, algorithms II, stochastic processes, machine learning, financial mathematics, time series analysis, statistical learning, deep learning

Schools I'm considering for PhD programs (non-biostat):
UPenn, Columbia, Duke, UMich, University of Washington, Cornell, UW Madison
UCLA, Texas A&M, UT Austin, NYU, Minnesota Twin Cities, Northwestern

Which schools in the list above seem most realistic? Are there ones that I shouldn't even bother with?
If all the above are out of reach, what schools would you suggest? Thanks!
 

Edited September 17, 2023 by almostsurely
fixed typo

[bayessays]

Have you talked to any of your stats professors at UIUC?  I think that would be a good target given your profile (though of course I'd understand if you want to go somewhere new).  

I'm going to assume you basically have straight A/A-s in your math/stats courses

I think Penn and Columbia are big reaches, but if application fees aren't an issue I don't think your list is crazy - I think the first row of schools (up to UW Madison) are probably "reaches" and the bottom rows are "matches" but you don't have any really safe options, if there even exists such a thing in PhD admissions.  I don't think you need to settle though, so if I were you I'd probably just apply to more schools in the same league as the bottom row.  

For the top schools, I think the biggest issue is that you don't really have any statistics experience that stands out and people might wonder why you didn't get involved in research during your master's.  Combined with going to a good but not elite school, there's not a lot that makes you stand out.

 

[almostsurely]

Hey bayessays, thanks for the input! I do have all A's in the classes listed above, but I got 2 B's in math classes during my combined masters, which is why my first grad GPA is so horrendous.

I did my MS remotely right in the middle of the pandemic, and did it quite quickly (1.5 years) which is a lousy excuse, but it's part of why I didn't get involved in anything.

I'll try to target more schools similar to the second list. Thanks!

[bayessays]

If the Bs are in core math classes (calc, linear algebra, or real analysis), I would definitely lower my expectations.  I was thinking the low GPA was solely from some irrelevant grad CS class (something like operating systems or compilers).

Given the new info, I'd consider the list to be too reach-heavy for comfort, and I think the top row is very unlikely.

[almostsurely]

Hi Bayessays, thanks for your honest input again. The B's were in an ECE/signal-processing oriented random processes class and in a grad level combinatorics class. So, they're weren't exactly "core", but they were definitely math classes...