Profile Evaluation for Stat Phd

[frequentist]

Undergrad: Big State School ranked around 150

Major: Mathematics, with Minor in Computer Science

GPA: 4.0 

Student Type: International Male 

 Courses:

Undergraduate:  Calculus Series, Into Linear Algebra, Differential Equations, Proofs and Logic, Discrete Mathematics, Undergraduate Advanced Calculus Series, Intro to Programming, Object Oriented Programming, Data Structures, Data Science in Python, Data Visualization in R, Database System, Elementary Number Theory, Complex Analysis.

Graduate: Group Theory, Mathematical Statistics I, Mathematical Statistics II, Analytic Number Theory, Probabilistic Number Theory. Currently taking Graduate Real Analysis I (Lebesgue theory of integration on real line), Probability Theory I, Proof based linear algebra. Will take Real Analysis II, Probability Theory II, Bayesian Inference (or Robust Statistics) next semester.

 

GRE General: 168 Q, 158 V

Math GRE: Not taking

Research:

1.  Proved a result in analytic number theory with my advisor (Will have a manuscript uploaded on arxiv before submission of application).

2. Proved a result based on classical theorem in probabilistic number theory (jointly supervised by two professors, both of whom will write me letters). Paper in preparation.

3. Participated in summer research program at The Fields Institute (the same institute that awards Fields Medal in mathematics) where I worked in probabilistic number theory. I worked primarily in simulation and generating data where I wrote parallel programs in C++ using OpenMP and MPI and used supercomputers to perform simulation. Got some meaningful results. Paper in preparation.

4. Proved another theoretical result on probabilistic number theory, with my research team as a part of summer research program. Paper in preparation. 

My research on probablistic number theory, particularly the project where I worked on simulation and data analysis was a big factor in choosing statistics.

Interest: High dimensional statistics, Bayesian Statistics

Letters of Recommendation:  I think I will get strong letters from all three people I have worked with. 

Programs Applying to: 

1. Wisconsin-Madison

2. Purdue

3. Chapel Hill

4. Texas A & M

5. Penn State

6. Carnegie Mellon

7. Berkley

8. Stanford  

9. Harvard

10. U Mich

11. U Chicago

12. U Washington

13. Yale

14. Duke

15. UT Austin

16. Michigan State

17. U Pittsburgh

(Everyone says that admission for international students is tough, so I have added a little too many schools. Might remove Stanford, Chicago, and Harvard since I dont think I have a chance at them with my non-reputed undergrad program.)

Am I being over ambitious with this school list? Since all the people whom I have worked with are pure mathematicians, they have not been able to give me much advice on choosing stat programs. So, can I get perhaps a bit harsher but an honest assesment? Some alternative suggestions fitting my interests and profile would be awesome.

 

[MrSergazinov]

I think you definitely have a shot at most of the schools on your list. I applied last year, and my list was a lot like yours. The competition is high, but that should not discourage you. I am also an international male student, coming from a big public university. Indeed, my profile looked a lot like yours. At the end, I was able to get into five schools out of fifteen, I applied to. Actually, one of the five was my target school, so I consider my application cycle to be quite successful. 

[StatsG0d]

I feel like the following schools are very high reaches for you: Carnegie Mellon, Berkeley, Stanford, Harvard, Chicago, and Washington.

Of the remaining schools, some (more attainable) reaches would be Duke, Yale, and Michigan. The remaining schools sound reasonable as targets.

[frequentist]

On 11/3/2020 at 2:22 PM, StatsG0d said:

I feel like the following schools are very high reaches for you: Carnegie Mellon, Berkeley, Stanford, Harvard, Chicago, and Washington.

Of the remaining schools, some (more attainable) reaches would be Duke, Yale, and Michigan. The remaining schools sound reasonable as targets.

Thank you for your honest assessment. I knew I was over ambitious with those schools, but I am just taking chances. As long as I will get into couple of schools from my list, I am fine with spending couple of hundreds extra in application.

[frequentist]

I ended up significantly revising my list. Do you all think Florida State and Pittsburgh can be considered safety for my profile? If not, can anyone suggest more suitable safety with decent cohort size?

[bayessays]

My only concern with FSU is that they often give unfunded offers and that Pitt is pretty small.  I'd feel Michigan State would probably be a little safer than those for those reasons. I think it's very hard to consider any individual school completely safe, but between the three schools I'd feel pretty safe. With your profile, I don't think you need to apply to lower ranked schools than that, but I would apply to more than 2 schools in the range.

[frequentist]

2 hours ago, bayessays said:

My only concern with FSU is that they often give unfunded offers and that Pitt is pretty small.  I'd feel Michigan State would probably be a little safer than those for those reasons. I think it's very hard to consider any individual school completely safe, but between the three schools I'd feel pretty safe. With your profile, I don't think you need to apply to lower ranked schools than that, but I would apply to more than 2 schools in the range.

I have been considering Michigan State as well. I have also been looking at UConn and they have pretty good strength in high dimensional statistics. But do you know how large is their incoming cohort size? They only list graduate assistants on their website and there are 57 of them but they do not mention if  those assistants are masters student. However, their department also states that only PhD students get funding so I assume they are mostly PhD students. If they accept around 10 students a year, I would be happy to apply there. 

[bayessays]

Not familiar with their cohort size or the program very much.