phD in statistics Profile Evaluation

[AndrewYao]

Hello, I am apply for mostly phD programs in statistics for fall 2018, and I would love to hear your thoughts on my profile.

Undergraduate Institution： Top 40 USnews, public school; no statistics department, but math department is top 20

major: statistics

GPA：3.732 in general, major GPA is 3.76

Ethnicity: International Asian Male （what I have heard is that my actual competition will be the applicants from my country, as opposed to applicants that went to undergraduate schools in the US ）

GRE: 161 verbal, 166 quant, 4.5 writing

Math GRE: did not take it

TOEFL: I know this is not required since my undergraduate school is in the US, and I took my TOEFL in 2013, but still, just in case. 112

Programs Applying: phD in statistics, phD in biostatistics, ms in statistics

Noticeable Courses Taken:  all A's in freshman calculus series (4 courses, including multivariate),  A's in linear algebra ( 2 courses, one elementary, the other applied upper division), 2  A-'s in real analysis (took two courses in a supposedly rigorous 3-course series), A in complex analysis, A- in a project-based big-data statistics course, A+ in computational statistic,  A and B in a two-course mathematical statistic series, ABA in a 3-course probability/stochastic series, B+ in time series, A in numerical analysis, A in a project-based math programming course, A in financial mathematics. P(pass) in a graduate-level applied statistics course. 

Recommendation letters: one from the professor I took computational statistic with, also doing a reading course with him this quarter; one from a professor I took multiple statistics courses with; and one from the professor I took numerical analysis and math programming with. There first two professors are quite well-known in the statistics industry, although I doubt that their recommendations would be strong because of the usual high standards they set. The third professor is less well-known and his letter should be better than a generic letter.

Research experience:  had a big group project in which I build regression models on a Kaggle data-set, on which I developed most of the theory and did all of the coding;

a walk-through research on a R-package on isotonic regression, had a 5-page long research report 

Professional experience: no experience in the industry so far, but I am looking into some data analysis internships after I graduate.

Misc: I TA'ed a elementary statistics course for two quarters, and I also have TA'ed some calculus courses; the TA reviews were all quite good.

I am graduating in the start of my 4th year as an undergraduate. It is unusual, but I am not too sure how this will things.

If I do get into a masters program, I really hope to get enough funding and TA-ship to at least reduce my tuition: I just really want to be financially independent and not have my parents pay tuition anymore.

 

Schools and programs I am applying to: 

OSU,

UIUC,

Florida State,

UC Davis,

Iowa State,

North Carolina State,

Purdue

Pittsburg University,

Rutgers

UConn

UC Irvine

Texas A&M

Texas Austin

Northwestern

Rice

UCLA

North Carolina Chapel Hill

 

I am applying to phD in statistics to all of those schools above, phD in biostatistics if available, masters in statistics/biostatistics if enough funding is provided. The way I list these schools is the by how much I fit, Northwestern, Rice,UCLA, NC Chapel Hill are reaches;    

UIUC, OSU, Texas A&M, Texas Austin, Florida State, NC State, Iowa State are hopefully matches;

and the rest are naively considered safeties for now.

 

Concerns: I have had some B's in statistic courses;

                  no GRE math score;

                  research is not stellar;

                  no professional experience;

                  no strong recommendation letters.

 

Please let me know your thoughts:

what other schools should I apply to?

what phD programs do I have a decent chance at ?

what masters programs should I apply to, given that I really want some funding ?

 

Thank you so much for your time and thoughts.