Improving application for biostat PhD programs post-graduation

[MeepMeep92]

Hi all,

I graduated a few years ago and recently decided that I'd be interested in doing a biostats PhD. Unfortunately, I didn't have a PhD in mind at all during undergrad, and this coupled with some depression resulted in some lackluster grades. I've been taking / am planning to take some more classes as a non-degree student to fill in gaps / improve my app, but I wanted to get an idea of where I stand and what further classes I should take.

Undergrad Institution: One of Harvard / Yale / Princeton

Major: Computer Science

GPA: 3.6x in major, 3.5x overall

 

Type of Student: Domestic, ORM

GRE General Test: Expecting 167 Q, 167 V, 5 W

Programs Applying: PhD in Biostats 

Research Experience: Currently doing image processing research with a

CS professor. I'll hopefully get a paper and letter from it, but not sure yet.

Also have 3 years of work experience as a data scientist + software engineer (but

not research work)

Letters of Recommendation (3 letters): One from my real analysis professor in undergrad (bit of an

odd case, since I got a B in the class because I timed myself badly on the final, but

he said he would write me a good letter since I wrote very clear proofs and did well

on midterms). One from my advanced linear algebra professor, but the class was taken

at a school ranked ~100 as a non degree student (got an A+). Hopefully one from the

CS prof I'm doing research with.

Relevant Course Work: 

Math / Stat

Calc II (high school, A)

Multivariable Calculus - B+

Probability and Statistics (200 level, but used multi var calc) - B+

Multivariate Statistics for the Social Sciences - A

Linear Algebra - credit (but took advanced LA after graduating, see line below..)

Advanced Linear Algebra (proof based, taken at GMU) - A+

Real Analysis - B (but will get a letter of rec from the professor, as said above)

CS

Algorithms - B+

Math for CS - A-

Numerical Computation - A

Introduction to Data Mining - A

Intelligent Robotics - B+

Data Mining and ML - Credit

Artificial Intelligence - A

Advanced Topics in AI - A

Systems Programming - B+

 

I'm hoping the recommendation letters from my real analysis professor and my advanced linear algebra professor will help (I think both of them will be good based on conversations I had with my professors when I requested letters). However, my lack of research experience and relative lack of math / stats coursework seem to be pretty problematic (especially with the B in real analysis...not sure how much the professor's letter will do to make that B look less bad). 

I'm thinking about spending another year to improve my application before applying. I believe I could take a couple more proof based math classes and get A's, though I'm not sure how significant that will be if I take them at a school ranked ~80-100 (I need to stay at my job full time for financial reasons, so I can only take about 1 class per semester).

I'd be interested in any thoughts on what range of biostat PhD schools I'd be competitive for with the current profile above, as well as any thoughts on how I can improve my application over the next year (if I decide to delay a year, which I feel like I should do..)

Edited April 4, 2019 by MeepMeep92
Clean up formatting

[Geococcyx]

I'm just another applicant, and I'm unsure enough with this evaluation that I was pretty hesitant to post, but I can give it a (potentially inaccurate) shot.

Your GRE scores and upper-Ivy League background will clearly help you a lot, and it probably doesn't hurt to have a good computer science background in both theory and practice.  A 3.5 GPA isn't great, but I'm not sure how much it hurts you at an Ivy -- there's been some grade inflation issues at top private schools, but you're also competing against top students.  If I'm comparing against myself, I don't actually know that your Real Analysis grade even hurts much -- I got a B in Real Analysis at a less prestigious school and had no math professors write letters for me, and still got into UNC biostat two weeks after I applied.  I am assuming that you're doing more applied research, which ultimately doesn't mean that much, although it's still probably worth having your adviser for that write a letter of recommendation for you.  I suspect the more learned members of the forum will correct my assessment later, but I would guess that you'd be competitive at biostat programs 4-10 (UNC through MD Anderson/UCLA level), with an OK but not great chance at the top 3.  My reason for thinking you'd be competitive at 4-10 is that I thought I'd be competitive in that range, and clearly UNC thought so too.  Meanwhile, you go to a much better school, should have similar GRE scores, and strong computation and coding experience that could make you attractive to higher-up programs even though you have a lower GPA/grades overall (for instance, Harvard biostat in particular seems to care a lot about numerical analysis and computing).  

That said, I have a couple questions.  One -- what exactly does it mean to get "credit" for a course grade?  Is that a C?  Did you take this class as a pass/fail, and if so, was that required or did you choose that?  If the latter, why?  I'm super unfamiliar with taking actual academic classes and getting a non-letter grade for them (as you can tell), so I'm curious as to what an admissions committee would make of "credit" in a linear algebra class.  If that's standard, then no problem, but otherwise you should definitely have that advanced linear algebra professor be a letter writer.  The reason I make that sound like not just fait accompli is because if you have had a close supervisor with a stat/similar PhD or background at your job, you may want to consider them as a potential letter writer.  I have no idea if that's the sort of place you work at, though, just something to keep in mind.  

For stuff you can do, I would say taking more proofs-based classes and getting A's or A+'s wouldn't hurt.  Even so, I'm not sure how much you stand to gain by doing that for a year before applying -- you yourself note that the school in question isn't super highly ranked, and you have an Ivy League real analysis professor recommending you already.  You shouldn't need to worry about getting involved in research beyond what you're already doing, nor would you need to (say) take a biology or genetics class -- you can learn that in grad school if you really need it.  

I have a hard time with this profile, because it seems similar to mine except with a lower GPA from a better school, plus better computation and work experience.  That said, I also feel like I really overachieved my qualifications during this admissions cycle, so your results may vary.

 

[MeepMeep92]

@Geococcyx Thanks very much for the detailed response.

I think your post definitely gives me some very useful perspective, even if you're not sure whether it's entirely accurate.

Yes, the CS research I'm doing is applied. It involves some applied algorithm design but no new theory.

If I'm somewhat competitive for 4-10, then that's great and I'd almost definitely apply this cycle, so I'd definitely appreciate it if anyone else reading this can chime in on whether that assessment sounds right. 

For your questions:

The credit means I elected to take the class (credit/D/fail), where "credit" is given for C- or higher. I wasn't planning to go to grad school at all + didn't realize linear algebra was so important + had a busy semester, so I just took it for credit. But I'm hoping the A+ in the advanced linear algebra class remedies that, and I'll definitely have the advanced LA professor be a letter writer. (I think her letter will say pretty positive things about my proof writing abilities, and I don't have any other letter that I'd use anyway)

What you said about not needing biostat specific research experience and classes is very useful, since I'm lacking both of those - would also appreciate if any other readers can weigh in on those points. 

 

[bayessays]

Yes, you are definitely competitive for schools ranked 4-10.

[MeepMeep92]

@bayessays - Great, thank you for the review! This is much better than I was expecting.