Ph.D. in Statistics Profile Evaluation for Fall 2022

[stat1709]

Undergrad Institution: Top 100 US university, private university in Texas
Major(s): Mathematics (Actuarial Science concentration)

Minor: Computer Science, Economics
GPA: 3.8

Type of Student: International Asian female, first generation

GRE: Not taken yet, planning in July

 

GRE Subject: Not planning to take to due busy schedule

 

Coursework (completed)

Math/ Statistics: Cal I, II, III: A/B/B | Interest Theory I,II: A-/A | Linear Algebra: A | Statistics: A | Probability: A | Discrete Math: A | Honors Math Seminar (for Research): A | Intro to Math Proof: A | Time Series & Regression (grad level): A

Economics: Intro to Micro,Macro: A/A | Intermediate Micro/Macro: A-/A 

Computer Science: Intro to Programming: A- | Data Modeling and Querying: A | Web Development: A | Linux/Unix Admin System: B+ | Techniques In Programming: A | Data Structures: A-
 

Will take: 

Math/Statistics: Real Analysis (this class is considered the hardest math class in my school, the average score is C+), Penalization & Shrinkage Methods (will prob get an A), Honors Research Project (will get an A for sure), Predictive Modeling (grad level)

Economics: Econometrics

Computer Science: Database Systems, Data Mining and Visualization

 

Research/ working experiences: A two-year research (including the summer in between) in high dimensional data analysis and parameter shrinkage methods in estimators. In this research, I did all of the coding and came up with automated scripts that reduce processing time tremendously. Will be submitted to a journal, but will have to be later in my senior year. Other than that, I had two internships, both in data science and machine learning. Particularly for the first one, I built a predictive model using DS/ML algorithms and also did a lot of literature review of relevant research/articles.

 

Skills

Programming languages: Python, SQL, R, Java, Swift, HTML, CSS, Bash 

Teaching experience

- Tutoring student-athletes for 2 years in from Pre-cal to Linear Algebra, Intro Micro/Macro, and Intro to Programming

- Founded a club that teaches mobile development at my school that aims to increase diversity and inclusion in tech so I was teaching a class alongside with a few other student-lecturers

 

Letters of recommendation: 3 in total

First one is from my research advisor, a very strong one

Second and third ones are from math professors (Can't tell if they are strong or not, but positive)

 

Misc Activities (but Relevant):

- Attended IDDEAS@Wharton program (a two-day predoctoral seminar to learn about Ph.D. programs in business, including stats, and career in academia. Wharton selected only 14 scholars from a national pool based on transcripts, letters, and research ability)

- Selected for NYU AI School (explored machine learning and AI topics in computer vision, deep learning, NLP, etc. through hands-on labs and workshops instructed by NYU profs or PhD candidates)

- Won 3rd out of 17 teams in DataFest by ASA. Best scores for creativity and robust predictive models.

- 2 side projects in COVID-19 deep learning mask detection model and AirBnB housing price.

Schools

Still pretty unsure. I don't have a huge geographic preference but would prefer being in a city (not a deal-breaker). I started thinking of schools like:

- UPenn (top choice, way out of reach though lol)

- NCSU, Ohio State, Indiana Bloomington, Uni of Connecticut, UT Austin, TAMU (where my research advisor did postdoc), Southern Methodist University (my research advisor used to work here and advised some Ph.D. here), Uni of Iowa, Iowa State

I'm interested in research in applied statistics, such as applications of statistics in economics, urban analytics, etc and also in statistical machine learning. However, I still don't have a clear sense of what areas I will go into so I don't think much into this as of now. I believe I still have time to study and find out what areas I like.

Appreciate any and all advice about school choice! If there's anything I can improve on, let me know as well. Thanks a lot and best of luck to everyone applying this fall 2022!

Edited June 25, 2021 by minhbui

[StatsG0d]

If you are interested in applied statistics, I don't think UPenn would be a very good fit. 

The other schools on your list seem reasonable (maybe not Iowa state though).

Have you considered biostatistics? I think you would have a very strong profile.

[stat1709]

@StatsG0d I heard that Biostats is very very competitive and not sure how to express my interest in biostats when I don't have any research experience relevant to this field. What biostats program would you recommend I should look into? Thanks!

 

[StatsG0d]

On 7/16/2021 at 9:19 PM, minhbui said:

@StatsG0d I heard that Biostats is very very competitive and not sure how to express my interest in biostats when I don't have any research experience relevant to this field. What biostats program would you recommend I should look into? Thanks!

 

I don't think biostats is more competitive than stats. Probably more the contrary. Very few students have any relevant research experience coming in. To me, it sounds like your research experience does relate to biostats, as high dimensional data is common (e.g., genomics) and so is shrinkage (e.g., Bayesian analysis).

To formulate your interests, I recommend you read some papers or just google some fields that are popular in biostatistics and relate to the branch of statistics you are interested in, e.g.

• Computation - genomics
• Machine learning - precision medicine
• Spatial/temporal - Image analysis (e.g., diagnosing cancers based off of an MRI)
• Bayesian - design/analysis of clinical trials

Virtually every subfield of statistics has applications in biostatistics.