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2020 Fall Statistics PhD Profile Evaluation

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Hi all, 

I wish that I had done this sooner! I never thought to ask the GradCafe, and everyone is so helpful here. I don't have much idea of which programs I should be targeting, so I'd like some advice based on my background. 

I graduated in 2018, so I spent a gap year after graduating without applying. I mention some of the things I did in here. 

Undergraduate Institution: UC San Diego (mid-tier university of california)

Degree granted 2018: BS in Mathematics and Physics (double major), with department honors in Mathematics 

GPA: 3.59/4.0 (cumulative) 

Type of Student: Domestic Asian Male

Relevant Upper Div Courses (quarter system):

Math and Stats, Undergrad level

  • Real analysis (lower level) (A, A-, A)
  • Abstract Algebra (A-, A, Pass)
  • Complex Analysis (A)
  • Number Theory (A+)
  • Mathematical Statistics (A-, A)
  • Nonparametric Statistics (A-)
  • Probability and Stochastic Processes (A, A-, A)

Math and Stats, Graduate level

  • Real analysis (A)
  • Algebraic Topology (A-, A-) 
  • Probability (A-) 
  • Mathematical Statistics (A-, A)
  • High Dimensional Statistics (A-)

Physics, undergrad level:

  • Classical Mechanics (B, A)
  • Electromagnetism (B+, A-, A)
  • Statistical Physics (A+)
  • Quantum Physics (A-, B ) 
  • Mathematical Methods (A, A)
  • Electronics (A-)
  • Laboratory Projects (B)

Physics, Graduate level:

  • Special topics---Quantitative Physics (A-)

Relevant Lower Div Courses (quarter system):

  • Lower Division Physics (C, B+, B+, B+, A)
  • Lower Division Physics Lab (B-, A-)
  • Lower Division Honors Calculus/Linear Algebra (B+, B-, B+)

GRE General: Verbal 167 (98%) / Quant 166 (89%) / Writing 5.0 (92%)

GRE Math Subject: 760 (71%)

Research Experience:

  • Two summer REUs. I spent one summer working on improving facial recognition with neural nets. I spent the next summer on a theoretical math paper, basically using combinatorics. The revision is almost done, but we're not ready to submit it yet!
  • My honors thesis was mostly expository, but I proved and explored one new thing, buried under all the surrounding context. Just that small result wasn't worth a publication though, I think. 
  • I spent the last year working part-time in a marine biology laboratory. I was mostly doing data engineering, organizing, and cleaning using bash, Python, R. I did train a neural net to recognize fish for the lab, and worked with a government biologist to fit some survival analysis models. That paper is also being written up, and is almost done, but we're not ready to submit yet! 

Work Experience:

  • I was a TA for the Math department for nearly three years. 

Letters of Recommendation:

  • Undergraduate honors thesis advisor (who also taught my grad mathematical statistics)
  • High dimensional statistics prof
  • Prof facial recognition REU


  • I read the Elements of Statistical Learning cover to cover this year very closely, and I have some detailed notes and problem solutions that I want to expand and post online but haven't done yet. 

Schools I'm Considering, Ranking from US News

  • Stanford (1)
  • Berkeley (2)
  • UChicago (6)
  • CMU (8)
  • Duke (12)
  • Columbia (16)
  • Penn State (20)
  • UCLA (27)
  • UC Davis (31) 
  • UCI (50)
  • UCSB (67)

I've also applied for the NSF GRFP, if that matters. 

My main questions are: Am I targeting the right schools? Which ones of these are reach, match, safety? Should I try to cram in a retake of the general GRE? And should I submit my math subject GRE? 

I also have some other concerns... if anyone has time to lend an ear and donate a few cents. 

About my rec letters: I might have time to switch some of these around. The post-doc who led the combinatorics REU could also write a letter instead, or he could get the supervising professor to ultimately write it. Is that a better idea? And what about the government biologist? Honestly, we met somewhat infrequently, and it was mostly him asking me for advice on how to do a technique and generating plots for him. His training and credibility isn't in statistics. 

About my in-progress papers: Is there a good way to talk about these when they haven't been submitted yet and I'm not getting letters from anyone regarding them? It might be very important to get one of those letters, but I do think my high dimensional stats prof thinks very highly of me already, and I've known her longer...

About my lower div grades: Those were all done in my freshman year, which has the lowest GPA of all my years. Is it worth explaining something about this? I think it was carryover from high school, where I also did not do very well, with plenty of C's and a few D's even, but I also had some family issues. 

About my Pass in Algebra: I accidentally signed up for pass no pass and didn't realize until the middle. The professor who taught that course acknowledges he would have assigned me an A, and he can let one of my letter writers know that. Is that appropriate? Should I mention it in my personal statement as well? 

Thanks so much for your input! You guys are such an awesome resource. 


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With your GPA being so low, I don't think it's worth it for you to apply to Stanford, Berkeley, Chicago, CMU, Duke and Columbia. Those are the top programs and have tons of applicants with near perfect grades and GRE scores to pick from.

I'm not exactly sure what you should expect since very few people get B's in calculus and linear algebra, then an A in graduate real analysis. I would apply to a range of schools ranked between 20-50 and see if someone is willing to take a chance on you, since you clearly are capable of advanced math. It's not a good idea to apply to only UC schools, since their location makes them more desirable and harder to get into.

As for your GRE, it's probably not worth retaking.

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I think the range of schools you're applying to is fine, although I don't think you'll get into the top 6 on your list.  Agreed on adding more 20-50, although you could go higher.  Your low GPA won't be a huge issue because you obviously turned it around after freshman year.  I wouldn't bother explaining.

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Awesome! If I could ask one more question about my potential research interests. 

If I'm interested in the following: 

* Statistical learning e.g. CMU

* Software development, e.g. the Stan team at Columbia

* Robustness and censored data problems

* High-dimensional/large scale inference

* Functional data analysis, wavelet methods

* Ecological/environmental/spatial statistics

I'm getting a lot out of reading other replies on this board regarding specific programs. Is there anything directed you folks can say about these, regarding programs in 20-50? 

Thank you so much! 

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You'll find censored data/high dimensional people at a ton of places - Minnesota has a big focus on HD machine learning (think Lasso). For software, UC Davis has a major R guy, Duncan something. For spatial, look at Ohio State (and lower down, Mizzou).  For functional data, FSU has a huge group of people working on shape analysis and a wavelet guy - one of their grads is also a prof at OSU now.  

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