2019 Stats Profile Eval/School Recommendations

[alshap1010]

Undergrad Institution: UCLA

Major: Applied Math

Minor: Statistics

GPA: 3.793

Type of Student: domestic white male

 

Relevant Courses: Linear Algebra (A), Calc. II (A), Multivariable Calc. 1/2 (A-, A, 2 courses at my school), Numerical Analysis I/II(A, A), Analysis (A), Probability (A-), Nonlinear Diff. Eqn (A-), ODEs (A-), C++ programming I/II/III (A, A, A-), Math Modelling (B-, first Upper Div Math, got rocked, only B in a math class, though), Python (A)

Currently taking: Mathematical Statistics, Statistical Programming in R

Taking Fall Quarter: Complex Analysis, Monte Carlo Methods, Algorithms, Statistical Linear Models

 

GRE: Only have taken a single practice test thus far without studying, but this is what I got. Expecting/hoping to see 3-5 points of improvement in Q/V.

Q: 165

V: 156

W: 4  

Will be taking the Math GRE in September

 

Programs Applying:  Statistics PhD unless otherwise specified

 

Research Experience:

- One summer in a Physics/Biology lab, mainly student run and not very impressive (fun, though)

- Math research course in fluid dynamics

- Applied Math REU, same fluid dynamics lab/advisors, but a different project

 

Teaching Experience: Curriculum director for a club that teaches science to low income middle schoolers.

 

Recommendation Letters: 2 from  math professors / research advisers from fluid dynamics lab (one was my programming professor, the other was the adviser for the REU project, not the adviser of the whole REU program), other from a Stats professor I have taken classes with. I think they will be decent, not sure if they will be stellar.

 

Coding Experience: C++, R, Python, and Matlab

 

Research Interests: I am interested in researching Machine Learning, Data Science, etc. Not 100 percent sure, but definitely computational and data intensive. Not sure if I want to go into industry research, but I’d like to keep that option open.

 

• Stanford

• Berkeley

• UWashington

• CMU - Stats/ML joint program

• UChicago

• Cornell

• Harvard

• UPenn

• Duke

• UNC

• U Michigan

• UT Austin

• Texas A&M

• UCLA

• Yale

• Johns Hopkins

• Maybe some Canadian schools

• CalTech - Computational and Mathematical Sciences PhD

• G Tech - ML PhD

• UCSD - ECE with emphasis on ML and Data Science

• NYU - Data Science PhD

Concerns:

• Finding good Stats programs with research in ML, DS, DL. Most web searches bring up CS departments, which isn’t particularly helpful.

• Not sure what are reasonable schools to apply for. Dreams/Reaches are pretty obvious, but choices are less obvious for more mid/low tier.

• Should I apply to programs listed as specifically Machine Learning PhDs? Do other programs/industry respect these programs? What about Data Science programs?

What schools should I add, which should I take away? Any other recommendations?

 

Edited August 26, 2018 by alshap1010
minor specification

[Stat Assistant Professor]

I think your list is rather top-heavy, and some of those on your list are *very* difficult to get into, unless you have a superstar profile with tons of advanced math classes (e.g. UPenn Wharton only admits 5-6 new students every year... and a lot of the time, 4 of those will be international students). Admissions can also be a bit unpredictable, and ranking does not always correlate with admissions chances -- there are other posters on this board who had similar profiles to you who reported getting admitted into TAMU but rejected from UCLA. Yale Statistics is also very difficult to get into even though it's USNWR ranking isn't as high as say, Stanford.

I would take a closer look at those schools and trim down the list of schools from the top tier to ones that really seem like the best fit. You can apply to a few of those, but I would add some bigger programs like NCSU, ISU, Purdue, and UIUC. It may not hurt to apply to some schools like Rice, UConn, and FSU too to be safe. 

Edited August 26, 2018 by Applied Math to Stat

[StatsG0d]

17 hours ago, Applied Math to Stat said:

I think your list is rather top-heavy, and some of those on your list are *very* difficult to get into, unless you have a superstar profile with tons of advanced math classes (e.g. UPenn Wharton only admits 5-6 new students every year... and a lot of the time, 4 of those will be international students). Admissions can also be a bit unpredictable, and ranking does not always correlate with admissions chances -- there are other posters on this board who had similar profiles to you who reported getting admitted into TAMU but rejected from UCLA. Yale Statistics is also very difficult to get into even though it's USNWR ranking isn't as high as say, Stanford.

I would take a closer look at those schools and trim down the list of schools from the top tier to ones that really seem like the best fit. You can apply to a few of those, but I would add some bigger programs like NCSU, ISU, Purdue, and UIUC. It may not hurt to apply to some schools like Rice, UConn, and FSU too to be safe. 

Don't really have anything to add, just want to second this.