trynagetby

Parroting @StatsG0d point, I think you're really on the wrong forum. The people in this forum are fundamentally interested in statistical inference and probabilistic modeling. NYU DS (I have researched the department extensively, and even wrote a specific SOP for it and then I realized I wasn't a good fit after I realized how bad the SOP was) and what you seem to be interested in are more in developing computational tools that push the bounds of what is learnable. Rather than being concerned with proving consistency/convergence or statistical estimation problems they're more interested in solving problems like computational tractability, gradient zeroing, algorithmic correctness/efficiency, good representation for efficient information retrieval (See Dynamic Programming Algorithm for Chomsky Normal Form),methods for compressing neural network . Tbh for developing algorithms like EM and MCMC and even impactful NN work which is just optimization, proofs of convergence are extremely important in both fields and ya gotta be good at Analysis. You should ask around whatever CS/Bioinformatics forums are out there. But to get into programs that attack these problems, you'd need demonstrated competency in CS topics like data-structures, systems programming, analysis of algorithms, numerical analysis. With your research background , which is on the weaker side for CS, I think you'd need a good theoretical math background. If you're interested in it, I'd encourage you to apply, shoot for the stars man/gal. But if you want to do DL research, Statistics departments are not for you. On a philosophical note that I hope you feel free to ignore as I don't know your entire situation: judging from the thread, it seems like you're seeing a PhD as a silver bullet for the existential pain of working in late capitalism. Unfortunately no matter what you do (yes, even most professors who aren't Michael Jordan or tibirashiani, and definitely most grad students) 80% of your time will be spent doing menial pretty frustrating work, but you have to find the other 20% to make it worth it. And even if the actual job all sucks there's almost always a silver lining in a job if you have masters (pay which you mentioned, work life balance etc..). If your job is super interesting, it's probably going to have bad work-life balance and the contrapositive is also true. Having a lot of life suck is just unfortunately part of life and being happy is an explicit effort you have to make. Not to say, you shouldn't try to change, but just having a PhD won't make things better, worse, harder, easier. It won't make you smarter or dumber, it'll just make things different. Seeing things like NYU DS PhD is an attractive solution because it seems so simple, do X get Y. But life doesn't work like that and having a PhD creates a whole host of new problems that you might not be happy dealing with if your primary motivation for a PhD is just that you hate your current job. For context, I work as a datascientist at a Fortune 100 financial services company, and I hate it so much. Everyday when I wake up I curse Bill Gates for spawning Excel/Powerpoint from the 10th circle of hell. I have to use incredible amounts of MBA jargon, but the second I use the words "conditional on" the MBAs lose their minds. I can say with confidence that my job is probably worse than yours. The job tortured my very soul for a while, until I saw the finale of the office while slacking off from work: I realized that although my entire job sucks, I have the work life balance to spend more time with my family, my aging dog, my girlfriend. I've gotten pretty decent at classical guitar and picked up a bunch of other stupid hobbies (e.g latte art and fishing). I realized that when I'm a graduate student drowning in qualifying exams and research, I'll definitely miss this job that I currently hate. Sorry, this probably wasn't helpful, but I just want to warn that a PhD shouldn't be viewed as a solution to a problem. It's a luxury and a privileged that you should deeply want.

NYU DS admissions tends to be more focused on research track record or if you go the mathematical maturity route, you have to be extremely mathematically mature. Just a note, NYU DS is very computational NLP, Neural Network type stuff, (or super theoretical ML/Computational theory). This is very different from your standard Biostats PhD/Stats PhD track.

Depends on your matsers institution. If your masters at was like UIUC/UCDavid/UTAustin/Rochester type of schools and your recs are good I'd say apply to most of the Top 10 why not, you have a great math background. If it was at like wakeforest, you should be more conservative.

I think for biostatistics you just have to be more discerning when on the job market. There are definitely a lot of advanced pharma/biotech jobs that do super cool things. But I am 100% certain that out of the jobs that require biostat PhD there are waaaaaaaaayy more jobs that are your "check the pvalue/regulatory mess" than your Apple Health research AI type stuff. The sinecure statistics jobs are probably less prevalent for standard PhD statistics positions because getting a Statistically trained PhD to perform simple methods is only profitable and necessary in pharma/biotech. There's just no other industry where millions of R&D and next-gen science research hinges on the reputation of a crack PhD team sending a jaded bureaucrat who hasn't read a statistic paper since 1995, a sas7bdat file that computes a ANOVA table. That same effect doesn't exist for Biostat/Stat PhDs in tech/finance (in finance regulatory/compliance needs more finance background than Stats). Therefore it becomes way more important to say no to jobs as a bio-statistician. If Facebook/Citadel offers you a statistician position, it's probably going to be okay and fairly interesting, but if Pfizer does the same thing that could range from doing crazy causal inference research to formatting the shadows of a pie chart on a report. I interviewed for some biostats type jobs out of undergrad, and it's very clear from the interview what type of job it's going to be. So you just have to say no when the interview is clearly just a joke. I also suspect that all the cool jobs probably go to prestigious biostat programs and if you're from a school outside of Harvard, Michigan, Berkley, Upenn, JHU, UNC it'll be very difficult to get your foot into the door to top bio-statistician jobs. FWIW, off a gut feeling I feel like the impact of a Biostats vs Stats would matter more as prestige of program goes down. E.g Harvard Biostats vs Harvard Stats probably doesn't make a different in industry. Duke Stats vs Duke Biostats probably matters quite a bit more. Rutgers Stats vs Rutgers Biostats is probably huge difference.

Word of warning, UT Austin is way more competitive than its rankings would indicate (probably as competitive as like TAMU/UNC/Winsconsin/NCSU).

I'd add an opinion that on some things UCLA is stronger than UW, so I don't think its as one sided as the forum would say (this forums tends towards the standard statistics crowd). If you're interested in the more CSey side of Statistics (ML, Bayes Nets, HMM, Computer Vision, Generative modelings etc...) I think UCLA is stronger, especially now that Emily Fox left for Stanford. Still for the record I voted for UW.

Marrying a Upenn Med School doctor while in PhD could effectively quintuple your stipend, so I would seriously consider this perk if you got game. In all seriousness though, if you're sure you want to do genetics Michigan is the best program hands down. I've talked to friends at JHU Biostats, visited Umich Biostat as part of an REU, and talked to Profs at Harvard biostats visit day. Every one of them mentioned Michigan Bio-statistics Department as being the dominant program in statistical genetics. About working with Doctors in Med-School and such, I wouldn't worry about it as the only benefit of this is having lots of data and potential problems to solve. It's clear professors in Michigan profs don't worry about finding data/problems/collaborations and Michigan itself has a very good medical school. If you like super applied work, than maybe Upenn might be better? Michigan's center for statistical genetics is at the point where people nationally come to them to solve the big problems in genetics rather than just "how do I analyze this study". For example Michael Boehnke pretty much mapped out the major genetic pathway for type II diabetes (i'm too biologically challenged to understand what this means and not sure if i'm saying it right, but the thing he did related to this was apparently a big breakthrough in genetics in the 21st century).

Your tier of choices are way above my pay grade, but I'll throw in my two cents that it seems that Stanford doesn't seem to be the best choice. I'd recommend taking a look at their stats phd dissertations to see if you're interested in that type of work. Stanford posts dissertations publicly.

I think you can check out my profile and results for a comparable example. I had a little more math and a lot more research than you, but also a lot of Bs, plus your school's reputation is slightly higher than mine. I also really only had 1 strong rec letter and a rec letter where I just asked a prof who taught a class I did well in. I still got into the lower top tier range of Stat PhDs (UMich, UW, Duke,etc..) and I suspect that you would too (at least one of them if you applied to all). Cracking Harvard/Berkely/CMU will be hard but you should definitely apply. In terms of classes, I'd take whatever interests you and you think you can do well in. Many schools won't even ask for fall grades. Good Luck!

As someone who applied to both programs and got into some of both, I would pay more attention to the type of work that professors in Bio-statistics and statistics programs do and the coursework rather than the label. Biostatistics programs generally do more applied/methodology work that is focused on being immediately applicable to biomedical problems (hypothesis testing, Causal inference, etc...). Statistics programs generally do a wider range of stuff and if you want to do crazy theory like crazy asymptotic statistics or high-dimensional theory then you'll likely only be happy in a statistics department. Im still speaking in generalities because if you go to somewhere like Harvard you have people like Rajarshi Mukherje who are more theoretical than most people even in a pure Stats department like Duke. Personally I think if you're truly interested in biological applications then biostats departments like Harvard/UWashington are the way to go as you get so much exposure to biomedical problems that its probably academic heaven. Otherwise it might be a little torturous as departments like Harvard, depending on the training grant you're on, will force you to take many classes in your field of specialization (e.g cancer, genetics, environmental health). Also it'll kinda suck if everyone around you is super passionate about genetics and you're still confused about what a chromosome is (I speak from my experience at a statistical genetic REU). Unlike me, I think you're a strong enough applicant where you don't need to hedge your bets with biostatistics program unless you're really interested in biology stuff. So it really comes down to what you want.

@DanielWarlock gave a pretty comprehensive answer and I'm not knowledgeable about theoretical stats at all. I'll just throw in an observation that at all 4 different Stats/Biostats visit days at top 10 programs I've been to, someone has asked about how schools stack up in theoretical statistics and a professor said a variation on "oh for theoretical statistics, you should definitely consider (insert other Uchicago prof), and Chao Gao that guy is (insert superlative)".

Sounds like Stanford's a real possibility dude, so I'd just apply to all the top places. Because you're domestic I really wouldn't bother applying lower than Michigan/UWashington/Duke. Actually should probably apply to like 2 lower schools in the top 20 out of an over-abundance of caution.

I almost chose Michigan and I applied/reviewed UCLA (didn't get in lol, but I don't think I'm being sour grapes). But If you take a look at the top professors at UCLA and their fields of work, (with a few exception like handcock) you'll see that Michigan has multiple professors working in the same area and the best one is often of the same reputation as the UCLA counterpart (if not higher). I wouldn't consider Michigan weaker than Washington for comparison perspective.

I mean it's possible to go from Duke Biostatistics to Upenn Biostatistics PhD to Harvard Biostatistics Professor. I met this professor during Harvard Biostats visit day: https://sites.google.com/view/ruiduan/biography?authuser=1 She's super smart ofc, but I don't think Duke biostatistics would limit you. I think professors from the Statistics department like Amy Herring and Fan Li now advise Biostatistics Masters. So if you can work with them and get their recommendations you'd be in great shape.

If you don't take Columbia housing, it'll be rough for sure. Expect to pay 1.5-2k + even with a room mate. On safety, the Columbia area is very safe right now. I've walked alone at 1am from Manhattanville (if you work with Paninski/Cunningham you'll be up there) back to my Dorm near Columbia and have felt pretty safe because its pretty well lit and restraunts will be open. But NYC is a big city so you have to use common sense and be alert. There will be the same homeless people and some of them may have mental health issues that will cause them to harass you, but they won't cause you any serious problems. If you aren't really street smart (as in lived in a high-crime city like Baltimore, Bad parts of Chicago, Detroit...) I'd highly recommend you stay south of 125th street and on the Columbia side of Morningside (known by the locals as muggingside) park. The incident you're referring to was due to the student walking through the park at night which is not advisable. The park isn't lit at all at night and the nearby housing projects have had some problems with drug-related crime and muggings. But stick near Broadway/Amersterdam Avenue and Below 125th and you'll be fine. There really isn't a reason to go anywhere else for school/research. I make it sound like its dangerous but its really not. I came from a very safe rural Suburb and didn't feel at all at danger any time at Columbia. NYC has a crime spike now, but I suspect that's because subways are so empty and places are so depleted. So please don't worry about it. Imo Columbia/Morningside is waay safer than Duke/Durham.

An important consideration is that Berkley is an extremely graduate (read: PhD) focused school. Generally aside from Masters programs that are pipelines to PhDs (which I don't think Berkely MA in Stats is), Masters students are on the same priority as undergrads if not lower. Schools like Stanford which value teaching and education are more likely to provide more ample research experiences outside of PhD students. I speak as someone who did their BA from a very graduate orientated school.

Sorry, was talking about Stats. The starter of the thread I think is applying to Stats programs. Funny how you get to know posters on the thread haha.

No, PhD. Look up that email they sent way back where they ask you to fill out the google form about which profs you're interested in working with. Someone replied with asked a question and the graduate coordinator answered the question reply-all to the effect of "if you haven't heard back, you're on the waitlist"

Judging from the email they replied all to a while back, it seems like they put everyone not accepted on the waitlist and rejected no one lol.

I can't comment on the quality of the programs, but I went to Columbia undegrad so I can comment on location and housing. People have different feelings about the location because while Columbia is in Manhattan, it doesn't really have the Manhattan feel. It's definitely more residential and family orientated (which I like). If you live around the campus you have really easy access to the 1/2 subway line which gives super good access to downtown Manhattan and Brooklyn. If you're Asian, there's an H-Mart super close so you can get Asian groceries and Westside market is great. Central and Riverside park are nearby which is also very nice if you like jogging. Disadvantages are that its really not the Manhattan experience (I lived in East Village one summer, and its a completely different feel). Restaurant/Bar scene are meh (but you'll likely be broke anyway). The Affordable student housing that the previous poster mentioned is affordable, but still kinda sucks as you will likely have a roomate (roommate in the sense of sharing a room, like as a freshman in undergraduate). Many students decide to rent a place in Brooklyn but then you have to commute an hour to school. Morningside rent is pretty pricey. Winters are kinda sad (probably better than Boston lol). But Springs/Summers are super nice. I don't really have any advice, just wanted to give some honest location info.

It's the 15th as per standard

woops double post

For what its worth I could confirm 2 Umichigan Biostats masters at Harvard Biostats Interview day. There were probably more.

Undergrad Institution: Ivy League Major(s): Mathematics-Statistics Minor(s): GPA: 3.8999 Type of Student: Domestic Asian GRE General Test: Q: 169 V: 164 W: 5.5 GRE Subject Test in Mathematics: M: NA TOEFL Score: NA Grad Institution: NA Concentration: NA GPA: NA Programs Applying: PhD: Statistics, Biostatistics, ML Research Experience: Freshman research in social work examining social media data, publication with name far back, Sophomore Biostatistics REU: First author paper at PSB, 2 year of research in statistical Neuroscience at home Institutio, publication with name far back. Awards/Honors/Recognitions: Nothing really Pertinent Activities or Jobs: Tutor/TA Letters of Recommendation: 1 from Assistant Professor in Linear Algebra Class I did well in, probably not well known in stats (just a did well in class rec probably, but I was pretty interested in his research and we talked a bit). 1 recommendation from Sophomore REU prof. Probably extremely good, but he's a prof at a pretty unknown school. 1 recommendation from statistical neuroscience prof. He's extremely well known but I didn't do too hot in his lab so probably rec was just "this kid is persistent and can grind". Math/Statistics Grades: Bunch of lower division classes (A). Real Analysis I/II - A Abstract Algebra I - B+ Fourier Analysis - B Measure Theoretic Probability - A- Numerical Analysis - A+ Analysis of Algorithms - A Artificial Intelligence - A Statistical Machine Learning - A- Bayesian Statistics - A Statistical Inference -A Causal Inference (CS) - P (covid) Abstract Linear Algebra - P (covid) Applying to Where: (Color use here is welcome) Stats: School - University of Michigan School - University of Washington School - Duke School - CMU School - University of Washington School - Wisconin-Madison School - University of Texas Austin (waitlisted- then accepted) School - UCLA (ghosted probably rejected) School - UNC (ghosted probably rejected) School - Cornell (ghosted probably rejected) School - Rice Biostats: School - University of Washington School - Harvard Misc: School - MIT (SES) School - Georgia Tech ISYE (ML Program) School - Northwestern IEMS Advice for Posterity and Reflection: Overall I'm pretty happy with my results and I'm pretty excited to be attending Duke. One caveat is that I severely underestimated my application (thought I would get into maybe one of UWashington, Duke, Mich). I didn't think I had a chance at Harvard/Berkeley because of my so-so performancein upper division math classes and lack of graduate coursework , I didn't apply. My rec writers put a cap on schools beforehand (totally reasnoble given my paranoia) so I overspent my school allowance on safety schools. So future applicants please take risks because the regret is real. Tbh I probably wouldn't have gotten in to Harvard/Berkely but now I'll never know. Some general advice - all 3 of your letters don't have to be crazy strong. I think I only had 1 really strong letter that sang about my potential as a top researcher. While he was from a pretty unknown school he had sent quite a lot of students to top programs so that might have helped. The other letter from the neuroscience prof I did research for probably wasnt that great as I wasn't stellar and probably one of his worse students (but he has pretty good students). this letter probably said I was competent, hardworking and easy to work with. My last letter likely just spoke to my interest in math and that I was mathematically competent. So not crazy strong letters. - undergrad school prestige matters a lot. So if you're from a top grade deflated school and have bad grades still shoot for the top! Hopefully my ehh profile encourages ya'll. - Harvard/UWashington Biostatistics are not safety schools (typing this sentence, it seems obvious). I thought Biostatistics programs are much less competetive than statistics. But obviously that's not really the case as I got rejected by UWashington biostat and into UWashington Stat. Also I went to Harvard Interview day and people were super accomplished even before getting cut. I'd put those tier of biostat programs on the same competitiveness as UMich-Duke-UWashington Stats (probably difference that they place more emphasis on undergrad research and a little less on math). - That being said if you're interested in Stat seriously consider top Biostat programs they do super cool work and I feel like I would have been very happy at Harvard if I didn't certain offers I did. - Don't take math classes you're not interested in just to have more math classes. That's how my B in fourier analysis happened. - Operations research programs are much less competetive (outside like MIT/Berkely) if you're interested in applied stats stuff. - Hot take: I disagree with the prevailing wisdom that the SOP doesn't matter. I think Recs + Grades are much more important but with so many people applying I think that demonstrating that you can coherently express your research experience and that you know what type of work academic statisticians do via your research interests can move your application from the "consideration pile" to the "accept" pile.

Thank you all for you super helpful posts. I took everyones advice (over many different threads) and focused on the publications/student capacity of the advisors I'd like to work with. Ultimately I settled on Duke, but after hearing so much support for UWashington I hope I don't regret my decision haha.