The application cycle for Ph.D. programs in Biostatistics is finished and I am thrilled to join Brown’s Biostatistics department in the fall!

When I was preparing my applications, I profited from other folks sharing their experiences, especially Kat Hoffman and Simon Couch. I am a first-generation college student, a community college grad, and an international student, so I understand how valuable testimony like this can be. With this blog post, I want to pay it forward to the next generation of aspiring Biostatisticians.

If you are getting ready to apply, I hope my experiences can help you out. Please don’t hesitate to reach out to me at posmikdc@gmail.com. If you are a non-traditional and/or underrepresented applicant, I would be happy to glance over your work (availability permitting). In your email, briefly describe why you are reaching out and what specific advice you are seeking.

A Preliminary Disclaimer:

Please note that this post represents my opinions alone. My situation and my circumstances may be quite different from yours. Therefore, please take all my opinions with a grain of salt. Please reach out to me if you find any factual inaccuracies.

My Background

I am originally from Munich, Germany and I graduated from a large public state school with degrees in Economics, Business Analytics, and a minor in Mathematics. Before that, I completed an Associate’s degree in Business Administration at a community college.

After graduation, I briefly worked at an economic research consultancy focusing on energy economics. Coming out of undergrad, I was dead set on pursuing a Ph.D. in Economics, so when I was offered a pre-doc position at the Energy & Environment Lab at the University of Chicago, I jumped on it. I worked at the intersection of causal inference and machine learning there, although my work was not very “technical” (i.e. I was applying existing methods to new data sets rather than developing new statistical methods). Throughout this time, I became more and more fascinated with Statistics.

After 8 months, I decided to leave the E&E Lab and start my studies as a non-degree-seeking graduate student at the University of Chicago. The rationale was that I needed some more rigorous coursework (e.g., analysis) under my belt to be a competitive candidate for Ph.D. programs in (Bio-)Statistics.

If you want a more comprehensive overview, check out my CV here (Note that this is my current CV, not the one I submitted. Keep scrolling if you are interested in the CV I submitted).

My Interests and Goals

I am interested at the intersection of causal inference and statistical network analysis. In particular, I care about causal inference in high-dimensional networks, non-parametric and assumption-lean methodology, and dynamic treatment regimes. Ultimately, I would like to apply my work at the interface of public health and climate, helping policymakers make more informed decisions in response to environmental disasters and global warming.

With respect to my post-Ph.D. goals, I oscillate around the following numbers:

  • A career in academia: 55%
  • A research career in a public/ think tank role: 43%
  • A career in private industry: 2%

When it came down to sending out my applications, I ended up applying exclusively to Biostatistics Ph.D. programs (with the exception of one program). Here is why:

  • Biostatistics allows me to be a statistician while maintaining my applied interest in public health and environment
  • Biostatistics programs tend to be much more causal inference focused than traditional Statistics Ph.D.’s
  • Biostatistics programs are highly interdisciplinary, something I really value

My Weaknesses

  • Undergraduate background: I majored in neither Statistics nor Math in undergrad. Although my coursework was certainly not completely unrelated to (Bio-)Statistics, I know that competing with math and stat majors from Ivy+ universities was going to be challenging. On that note, my undergraduate institution is not particularly renowned in Statistics.
  • Recommendations: This is a mixed bag but I actually didn’t end up with a single UChicago recommender. Only one of my recommenders (who was my professor for my graduate ML course senior year) was a Statistician (+1 Economist, +1 Operations Researcher). (Not so) fun fact: I was going to get a recommendation from a UChicago professor but I did so poorly on the midterm that we decided a letter from him wouldn’t be wise lol (So keep your head up; you’re allowed to have accidents). Another thing that was less than ideal for me is that I did not get a letter from the UChicago lab I worked at. Lab policy dictates that you have to stay a certain amount of time to be eligible for a letter and I did not do that–that probably wasn’t great.
  • Graduate coursework: I was enrolled in a full-time non-degree-seeking program taking graduate coursework, but that is not the same as an MS in Statistics.

My Strengths

  • My Publication: I have a single-author peer-reviewed publication from undergrad. Though the paper doesn’t propose any new statistical methodology, it does focus on the application of some interesting methods in novel ways and applies them to data. I think that paper really helped my case. Moreover, the professor who supervised this thesis (an economist) wrote me a kick-ass letter of recommendation.
  • Research Experience: Ironically, my application profile was the inverse of most other (“traditional”) applicants. Usually, applicants to (Bio-)Statistics Ph.D.’s are math/stat undergrad majors from very respectable institutions with little to no research experience. My profile was the opposite of that. I had almost two years worth of full-time research experience but a lack of formal preparation. I think my research background in causal inference and ML (in addition to my publication) were my strongest assets.
  • Grades: I had excellent grades throughout my undergrad. During my first quarter at UChicago, I got two “A-“ and one “Pass” (Injury related). The latter is respectable though certainly not outstanding.
  • Leadership Experience: I do think that my leadership roles–e.g., founding a successful data science / social justice org in undergrad–was a big bonus on my application.

The Application Process

I went into this application cycle with the mentality of “giving it a shot”. I was fully prepared to get rejected by all programs because I thought my strengths did not quite outweigh my weaknesses. With that in mind, I applied to both masters degrees (Only in Canada for funding and personal reasons) and my top Ph.D. programs. Here is the list of the places I applied to:

Type Institution Program
Ph.D. UC Berkeley Biostatistics
Ph.D. University of Washington Biostatistics
Ph.D. Columbia Biostatistics
Ph.D. Harvard University Biostatistics
Ph.D. Yale University Biostatistics
Ph.D. University of Michigan Biostatistics
Ph.D. Johns Hopkins University Biostatistics
Ph.D. University of Chicago Data Science
Ph.D. Brown University Biostatistics
M.Sc. University of British Columbia Statistics
M.Sc. McGill Biostatistics
M.Sc. McGill Statistics
M.Sc. University of Toronto Statistics

I applied to the UChicago Data Science Ph.D. mostly because I was currently working at the UChicago Data Science Institute and I knew a good amount of the faculty. I applied to Masters only in Canada because (1) I was eligible for German government funding in Canada but not the U.S., (2) For personal reasons, and (3) Overall cost and quality of life. Additionally, I was a finalist for a full-ride leadership-based scholarship at McGill.

You may also notice that I didn’t apply to many other top-ranked departments. This is because I either found little research fit or (this was mostly the case) I didn’t want to live wherever the school was located. I encourage you to not sweep this factor under the rug.

I also had an internal ranking. The following factors were most important to me:

  • Substantial research in causal inference and networks: #1 JHU, #2 Brown, #3 UW, #4 Yale
  • Location and access to nature: #1 UW, #2 Berkeley
  • Research fit with individual faculty: #1 JHU, Berkeley, Brown, Yale, #2 Harvard

One more thing: Since I was ready to get rejected from all programs, I was working towards being a more competitive applicant during the next cycle while I was applying. In that sense, I had started a graduate research assistantship at UChicago in causal inference methodology and started TA’ing. This forward-looking approach really helped me mentally since I kept reassuring myself that I can just try again next year.

Preparation

First things first, I decided to take the GRE. I performed slighly above average, but nothing outstanding. Overall, I found the GRE to be a colossal waste of time, money, and energy. If I could redo my application cycle, I would have opted to not take it and scratch the programs that require it (Only two of them) off my list. After all, with all the evidence that is out there showing how the GRE puts marginalized students at a disadvantage, merely requiring the GRE is a huge red flag for me.

I started preparing my application materials very early, around June, because I knew I had a ton of time over the summer (as opposed to little to no time in the fall). One thing I did that made life significantly easier for myself and my recommenders is to start a GitHub repository called grad-apps that contained all my application materials. At the time I was writing my applications, this repo was public, so all my recommenderes and mentors could have easy access to it. It is now private (and will remain private) for privacy reasons.

That being said, I am happy to share how I set it up. Here is content of the README.md file that sketches out the basic setup:

Hi! If you are reading this, I want to thank you for helping me in my graduate school application process. Thank you so much for your support.

  • You can navigate this repository via the branches
  • Please access my most up-to-date CV and this README via this main branch
  • Each program I am applying to has a corresponding branch with the following format: [institution]-[type]-[program], e.g., “uw-phd-biostatistics” for the University of Washington’s Biostatistics Ph.D.
  • Since this is a public repo, please note that you do not have editing access. If you have comments/ suggestions, please do not directly edit these materials. Instead, please use the comment function and/or let me know separately.

Please note that I update this repo every time I make local changes. If you have any questions at all, please reach out to me via email or via cell.

[Followed by a table of programs with every deadline]

My Application Materials

I have decided to post my application materials for Brown’s Biostatistics Ph.D., the program I will be attending in the fall. It should go without saying that you should under no circumstances copy and paste from my materials. That being said, I know that sometimes it is difficult to find good examples of SOP’s, personal statements, and CV’s. If there are any additional programs that you would like to see my application materials for, please reach out to me via email.

Now, I will provide some commentary and context on these application materials. I was extremely lucky to have a handful of faculty and UChicago’s GRAD advising staff give me feedback. With that being said, please don’t treat these materials as the gold standard. They are by no means perfect.

CV

  • Education, Coursework, Awards: I initially had grades on there as well (Nothing worse than an A-) but a professor recommended against that because the “A-“ could catch somebody’s eye early. I know how nitpicky this sounds but I am just echo’ing what he said here.
  • Research Experience and Community Service: Some folks said that my CV boasts a lot of details but that was on purpose. My research experience was my strongest asset and I wanted to highlight everything I did.
  • Presentations, Professional Service, Skills: Having sections for this is not necessary for Ph.D. applicants because you may not have gathered any substantial experiences yet. I did have some space to spare though, so I decided to include some of the presentations and talks I had given. One more thing: Don’t forget to include your programming skills! I know some programs, e.g., UW Biostatistics, explicitly require that on your CV.

SOP and Personal Statement

  • General: My SOP’s were identical across all programs I applied to with the exception of two paragraphs (Of course I adjusted the name of school in the other paragraphs lol). Generally though, I wanted to lead with an eye-catcher (the bolded sentence in the first paragraph). Then, I wanted to get addressing my main weakness (the lack of rigorous coursework) out of the way (that’s paragraph #2). Up to paragraph #6, I describe my research experience and how it led to my current interest.
  • Paragraphs #7 and #8: I use paragraph #7 to talk about how my interests tie into the program/department/faculty members. A lot of times, people will put this paragraph as the second paragraph which may work very well with your application. The last paragraph is similar to seventh paragraph but focuses more on research community, centers, and personal fit.
  • Personal Statement: This was identical across all schools I applied to.

Overall, I think that being very neat and submitting “pretty” application materials was a tiny Brownie point for me. I would recommend submitting neat materials to anyone and if you can use something like LaTeX you may make an extra good impression.

Interviews

After submitting all my applications and trying to financially recover, the waiting game began. Pretty soon after winter break was over, I heard back from Brown. They wanted to have my autumn quarter transcript. About two days after I sent that over, I was officially invited to interview day.

The interview day (virtual) was an all-day thing, filled with various info sessions and interviews. I know that about 30 people out of ~380 applicants (which is an insane number) were invited to interview day. Here is a rough outline of my schedule:

  • Info session for all the shortlisted candidates across all the Ph.D. programs in the School of Public Health (incl. Epidemioligy, Biostatistics, etc.)
  • Departmental info session specific to Biostatistics
  • 3-4 interviews with faculty. One interview was with a member of the admissions committee (30min). Additionally, you were allowed to choose up to 3 faculty members for a 15min interview. Since my 30min interview happened to be with a professor I was interested in working with, I ended up choosing only two additional faculty. Thus, I had a total of 3 separate faculty interviews.

I talked to some folks around preparation and decided to go a bit “lighter” than usual. When I am nervous in interviews (which you bet I was), I tend to jump into rabbit holes and try to impress with my knowledge when I feel uncomfortable. That is generally not a good strategy since (1) you’re being interviewed to assess how you are as a person, (2) the faculty member you’re interviewing with could choose to grill you on something you said, and (3) you probably won’t woo any faculty members with any subject-matter knowledge you have anyway (Remember: They’re the experts; you’re the aspiring Ph.D. student). Keep in mind that you made it to the interview stage because the admissions committee is already impressed with your qualifications on paper. With that in mind, here is how I prepared:

  • Researched each interviewer’s active grants (Important in Biostatistics especially) and what the project was about. Wrote out 2-3 questions about that work.
  • Researched each interviewer’s fields of interest and recent work and crystallized out one or two overlapping interests. I didn’t fully read through any person’s paper because of the reason I stated above.
  • Looked into each interviewer’s story to look for common ground (If there is something striking that could be a phenomenal ice breaker)

After having prepared, here is how I experienced the interview:

  • Very relaxed and cordial atmosphere. I was not grilled on any technical questions though I was asked a specific (but very fair) question about my published paper.
  • It felt like a true conversation where faculty seemed to be most interested in who I am as a person and less about my qualifications.
  • It seemed that my interviewers really appreciated that I knew about their active grants. Of course, they don’t expect you to know everything about it (that’s their job, after all) but just showing that you did your “homework” makes a great impression.

After I got done with the interviews, I had a feeling that I did really well.

The Waiting Game

Ironically, despite an overwhelming feeling that did super well, I did’t hear back for a really long time. In the previous stages, Brown had been very quick, so I expected a decision within 2 weeks of the interview. One month, then two months passed–and nothing. It was really tough on my mental health because I had only heard back negative news from the other programs so far and I was losing hope. Please make sure to take care of yourself while you wait. For me, working out and going to therapy were two great outlets.

Then, around mid March, I got the email and call that I was admitted. I had gotten off the waitlist.

My advice to you while you wait: Please don’t tie your personal value into these applications. I knew coming in that I was an excellent candidate (in my eyes and that’s all that matters) but also how stiff the competition was. Ph.D. admissions truly are a blackbox and as long as you do everything in your power to maximize your chances, you should be very proud of yourself.

Decision Time

My Final Results

As of April 15th (Decision day), these are my results from the admissions process:

Type Institution Program Decision
Ph.D. UC Berkeley Biostatistics Rejection
Ph.D. University of Washington Biostatistics Rejection
Ph.D. Columbia Biostatistics Rejection; Admitted to MS without funding
Ph.D. Harvard University Biostatistics Rejection
Ph.D. Yale University Biostatistics Rejection
Ph.D. University of Michigan Biostatistics Rejection; Admitted to MS without funding
Ph.D. Johns Hopkins University Biostatistics Rejection
Ph.D. University of Chicago Data Science Rejection
Ph.D. Brown University Biostatistics Accepted; From waitlist
M.Sc. University of British Columbia Statistics Accepted; With full funding
M.Sc. McGill Biostatistics Accepted; With full funding
M.Sc. McGill Statistics Rejection
M.Sc. University of Toronto Statistics Did not hear back by 04/15

As you can see, it was a tough cycle. I was very surprised by some rejections and less so by others. One important thing I want to mention: I believe I got rejected from most programs because I was not a “traditional” math/stat undergrad from a great school. There is only so much I can do to remedy that ex-post. I decided to still share this post because I had been told from faculty and mentors that the materials themselves were strong. Remind yourself that it only takes one singular program to admit you.

My Decision

Brown ended up flying me out and organizing a visit day. I felt like it was a particularly strong match because of the cordial and down-to-earth department culture. I was at the time still debating between McGill and Brown–since I could have (with an almost certain guarantee) transferred into the Ph.D. program after Year #1.

My decision ended up coming down to:

  • Research fit: I can work at the intersection of causal inference and networks.
  • Stipend and benefits: Brown pays one of the highest stipends I have heard of.
  • Departmental/ Culture fit: The department seems amazing and my gut is telling me I will be very happy there.

Funding

Funding is an important thing to consider, as well. Brown was amongst the top-paying (if not the top-paying) schools out of my list. In my admissions offer, alongside health insurance and full tuition, I am guaranteed a stipend of $49,012 and a one-time first-year supplement stipend of $1,750. When evaluating funding decisions, it does sometimes help looking at whether the school’s graduate students are unionized (Brown’s are).

Final Thoughts

Overall, I am very happy with how things went. I came into this process having two clear favorites–UW and Berkeley Biostatistics–and am coming out very satisfied despite getting rejected by both. This process is very intimidating and I am hoping that my thoughts add a little bit of clarity. If you are a non-traditional applicant, I invite you to reach out to me to have a chat (Caveat: Come fall, I will likely have very limited availability, but I would be happy to chat over the summer). Lastly, please reach out to me if you have any burning questions so I can answer them here.