Hi all,
I'm currently a senior planning on applying to PhD programs in statistics this winter. I'm especially interested in machine learning theory.
Undergraduate Institution: Cornell University
Majors: Mathematics, Computer Science (Double major)
GPA: 3.83/4.0 (cumulative)
Type of Student: Domestic Asian Male
Relavant Courses:
Math:
Linear Algebra (lower level) (A-)
Multivariable Calculus (B+)
Applicable Algebra (A-)
Numerical Analysis (A)
Linear Algebra (upper level) (B+)
Proofs (A+)
Honors Analysis (Currently taking)
Computer Science:
Discrete Math (A-)
Objected Oriented Programming and Data Structures (A)
Functional Programming (A-)
Systems Programming (A-)
Operating Systems (A-)
Algorithms (A)
Machine Learning (A)
Artificial Intelligence (A)
Natural Language Processing (Currently taking)
Other:
Statistics and Probability (Economics) (A)
Data and Systems Analysis (Operations Research) (A-)
Learning with Big, Messy Data (Operations Research) (Currently taking)
GRE General: Have yet to take
GRE Math Subject: Have yet to take, not sure if it's worth it
Research Experience:
Started to do research this semester in machine learning fairness. There's not much time, but I'll put a lot of time into it and try my best to accomplish something.
Not sure if this counts, but the proofs class had a term paper assignment where we came up with an interesting math problem to solve, and wrote a pretty detailed paper exploring it throughout the semester (~8 pages)
Work Experience:
Two internships at large companies. One for software development, one for actuarial science.
Letters of Recommendation:
Have not asked yet, but planning to get one from the professor (from the operations research department) supervising the research I started this semester.
One from the professor from the proofs class.
One from the professor from a sociology class I took. The class was focused on discussing data science papers that tackled issues in social science. Because it was discussion based, and it was a small class, the professor knows a decent amount about me.
Alternatively, I may be able to ask for a letter from the numerical analysis professor, as I went to his office hours a lot. However, I took his course 2 years ago.
Schools I'm Considering, Ranking from US News
University of Massachusetts–Amherst (#74)
University of Virginia (#69)
University of California–Santa Barbara (#67)
Northwestern University (#55)
University of Connecticut (#44)
Colorado State University (#44)
Ohio State University (#37)
University of Illinois–Urbana-Champaign (#37, Math GRE recommended ?)
University of California--Davis (#31)
Cornell University (#20)
University of Wisconsin–Madison (#16)
University of Michigan–Ann Arbor (#12)
As you can see, I don't have that many math classes, and I didn't do exceptional in them. I consider my work in computer science to be stronger, but in order to do research in machine learning, I figured it would still be easier to get into a PhD program for statistics than for computer science, because there is less of an expectation to have undergraduate research. Would switching Natural Language Processing for grad measure theory help? Or perhaps would taking the Math GRE help more? Both of these would require a significant time commitment, which might impact the quality of my research (along with the recommendation letter).
Please let me know if my list is too ambitious, or if I could do better. I'm worried about not getting in anywhere though, as even the #69 University of Virginia typically accepts 3/80 applicants (according to their website). Also, some of these schools accept applications from January onward, which could be helpful to have more time to do research and improve my application. Should I be only looking at those? Thanks for your input!