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trynagetby

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  1. Upvote
    trynagetby got a reaction from u8a4 in Fall 2022 Stat/Applied Math Ph.D profile seeking advice(school recommendations appreciated)   
    I think you have a pretty good chance to get into them. I'm don't know much about those programs, but in general for domestic students the competitiveness of PhD programs falls drastically once you get out of the union of top 30 Stat Schools and top 30 general schools.
  2. Upvote
    trynagetby got a reaction from Ryuk in Fall 2022 Stat/Applied Math Ph.D profile seeking advice(school recommendations appreciated)   
    I think you have a pretty good chance to get into them. I'm don't know much about those programs, but in general for domestic students the competitiveness of PhD programs falls drastically once you get out of the union of top 30 Stat Schools and top 30 general schools.
  3. Upvote
    trynagetby got a reaction from Ryuk in Fall 2022 Stat/Applied Math Ph.D profile seeking advice(school recommendations appreciated)   
    If you got A's in Real Analysis/Applied Analysis (which I'm guessing is like baby functional analysis?) I think you should apply to the range of NCSU/Wisconsin Madison/UIUC/Rice/Texas A&M and below. NCSU/Wisconsin might be a little of a reach but its pretty possible :).  I'd also think about applying to programs like Umichigans Biostat Masters (which is funded) to give yourself a leg-up before going PhD. If Biostats is an option, UNC biostats and below should be a good chance. Good Luck!
  4. Like
    trynagetby reacted to physics2stats in 2021 Applicant Profiles and Admission Results for Statistics/Biostatistics   
    Undergrad Institution: Top 10 Private University
    Major(s): Physics
    Minor(s): Chemistry
    GPA: 3.88/4.00 Type of Student: Domestic white male

    GRE General Test:
    Q: 169
    V: 169
    W: 5.0

    Grad Institution: Top 15 Public University Concentration: Computer Science MS
    GPA: 4.0/4.0
      Programs Applying: Statistics PhD   Research Experience: One paper published in undergrad on biological optics. Two other research projects -- one on reinforcement learning (paper on arxiv), one on uncertainty quantification (still in progress when I applied).
    Awards/Honors/Recognitions: A few merit scholarships; undergrad physics honor society; deans lists.
    Pertinent Activities or Jobs: I took five years off between undergrad and the above MS in CS. During that time I taught high school math and completed a master's in teaching (3 years) and then worked for an education tech company (2 years). Letters of Recommendation: One from a Biology professor, one from a researcher at Facebook AI Research (who was a physics postdoc when we worked together), and one from a Materials Science professor. I had done research with all of them. I think it was the first time any of them had written a rec letter for a statistics program. Math/Statistics Grades:  Calculus Sequence (A's), Linear Algebra (A), Differential Equations (B), Analysis I (A), Complex Analysis (A), Numerical Analysis (A), Geometry (A), Probability (A), Stochastic Processes (A), Undergrad Math Stat (A), Linear Optimization (A), Nonlinear Optimization (A), Assorted CS courses (A's), Assorted Physics courses (A's, A-'s, and B+'s).
    Any Miscellaneous Points that Might Help: N/A

    Applying to Where: (All Stats PhD programs)
    Duke - Accepted
    NCSU - Rejected
    UNC - Rejected Harvard - Rejected
    Penn State - Accepted
    TAMU - Accepted   Thoughts: I don't have any super coherent thoughts, other than I'm totally satisfied with where I wound up (Duke)! I had a preference to be in North Carolina, and thought I'd have the best shot at NCSU over UNC or Duke, so the NCSU rejection was definitely disconcerting. But Duke was my top choice and I couldn't be more excited to start in the fall! I just wish I could have been a fly on the wall when these committees were reading my application haha. Really, I felt pretty good about my odds at all these schools other than maybe Harvard, but I guess my changing fields multiple times plus the random work experience in education may have generated mixed reactions (e.g., interesting/well-rounded vs. chaotic/unfocused)? Or maybe that's reading way too much into things, who knows   I guess I'd say at a philosophical level -- don't be afraid to aim high and put yourself out there, even if it's scary to change fields or go back to school. Two years ago, I had an ok job but knew I wanted more school at some point, so I figured what the hell and applied to some CS programs (despite my entire CS experience being one semester of intro to python). I got rejected by 7 out of 8 of them, but the one I got in was an awesome experience. This cycle, I felt way more confident, since I had waayyyy more relevant coursework under my belt plus some additional research and a more solid SOP, but I still had mixed results. So I guess just go for you want, roll with the rejections/acceptances (and expect both as a necessary part of life), and make the most of the opportunities you do get. And I'd say for grad apps specifically also cast a wide net, since there's a whole lot of randomness to the whole process. Good luck everyone!
  5. Upvote
    trynagetby got a reaction from aeiou12345 in Profile Evaluation for Operations Research PhD Programs + Some Questions   
    Check out my profile from way back which I think is similar to yours but less OR focused. I also applied having no idea what OR was like and I got into Northwestern and GaTech ML (ISYE) with special fellowships for both. I only applied to the two because my main interests was Statistics PhDs. I think you have a good shot at all of them except MIT because, well, MIT (still worth applying IMO tho)
  6. Like
    trynagetby got a reaction from aeiou12345 in Profile Evaluation for Operations Research PhD Programs + Some Questions   
    No worries, you can scroll down to my post in this thread:
    Good Luck!
  7. Upvote
    trynagetby reacted to StatsG0d in Biostatistics PhD profile evaluation - Fall 2022   
    I think you're selling yourself really short. First, I wouldn't bother applying to UCSD, TAMU, Rutgers, or Rice. NCSU doesn't have a PhD in biostatistics, although they do have a Biostatistics concentration in their Statistics department. Also, not sure if UW refers to Wisconsin or Washington.
    You're competitive for any of the biostats programs in the top-5. In fact, I would be shocked if you didn't at least get into one of UNC or Michigan. I would say apply to all the top-7 biostats programs and maybe those Canadian schools if you're interested. I'd maybe add McGill if you're interested in precision medicine. I'd also add Berkeley if you're interested in causal inference.
    It sort of depends. If you've taken all the qualifying exam courses, the department *might* let you take the qualifying exam the summer you arrive. Otherwise, they may force you to retake their versions of the courses and then take the qualifying exam at the end of the first year. It usually depends on both the department and the specific case.
    If the qualifying exam is taken in the 2nd year (e.g., how it is at UNC), they'll let you skip the first year curriculum, but you'll have to take the 2nd year curriculum and take the qualifying exam the summer after your first year.
  8. Like
    trynagetby got a reaction from cyberwulf in Best PhD programs for Causal Inference   
    Harvard, Berkley, UW Stats all have at least 3 very top/rising star type people doing causal inference research. An important distinction you have to make is whether you want to do "classical" causal inference  (propensity scores, average treatment effects, instrumental variables, potential outcomes framework) or "modern" causal inference (dags,judea pearl causal discovery,  reinforcement learning, adaptive designs etc...). Both are pretty hot right now but the flavor of research is extremely different.
     
  9. Like
    trynagetby got a reaction from StatsG0d in Best PhD programs for Causal Inference   
    Harvard, Berkley, UW Stats all have at least 3 very top/rising star type people doing causal inference research. An important distinction you have to make is whether you want to do "classical" causal inference  (propensity scores, average treatment effects, instrumental variables, potential outcomes framework) or "modern" causal inference (dags,judea pearl causal discovery,  reinforcement learning, adaptive designs etc...). Both are pretty hot right now but the flavor of research is extremely different.
     
  10. Like
    trynagetby got a reaction from bayessays in Best PhD programs for Causal Inference   
    Harvard, Berkley, UW Stats all have at least 3 very top/rising star type people doing causal inference research. An important distinction you have to make is whether you want to do "classical" causal inference  (propensity scores, average treatment effects, instrumental variables, potential outcomes framework) or "modern" causal inference (dags,judea pearl causal discovery,  reinforcement learning, adaptive designs etc...). Both are pretty hot right now but the flavor of research is extremely different.
     
  11. Upvote
    trynagetby got a reaction from Aspiring_stats_student2312 in Letter of recommendation question   
    I'd say depends on the professor/class. If your Real Analysis class was super basic and your Complex Analysis class was like Stein and Shakarchi or Papa Rudin, I'd pick Complex Analysis. But if they're similar and you got into a competitive REU, you know apriori that the rec has to be somewhat decent right?
  12. Upvote
    trynagetby got a reaction from bayessays in Letter of recommendation question   
    I'd say depends on the professor/class. If your Real Analysis class was super basic and your Complex Analysis class was like Stein and Shakarchi or Papa Rudin, I'd pick Complex Analysis. But if they're similar and you got into a competitive REU, you know apriori that the rec has to be somewhat decent right?
  13. Like
    trynagetby got a reaction from untzkatz in Is Biostatistics becoming outdated in the industry, outside regulatory writing?   
    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.
  14. Like
    trynagetby got a reaction from bayessays in Is Biostatistics becoming outdated in the industry, outside regulatory writing?   
    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.
  15. Upvote
    trynagetby reacted to StatsG0d in PhD Application for Fall 2022 (Applied Math or Statistics)   
    I don't agree with this at all. Wake Forest is a very reputable school and there's a list of institutions that their Master's graduates end up attending, many of which are very prestigious. Not sure if you're trying to actually take a knock at Wake Forest in particular or if you were just oblivious to this fact.
    I think the OP has a great chance at top-20 programs and a small but nonzero chance at a top-10 stats. I could see them getting into any/all of the top-5 biostats programs. Their mathematics knowledge is extremely deep--far deeper than the vast majority of domestic students. They have a letter writer with connects at the institutions in which they are applying. Got a perfect GRE Q and writing score. This is a really strong profile IMO.
    The biggest problem is going to be that it seems the OP has quite specific research interests. I think it will be difficult to find a good program that aligns with these interests.
    OP: I recommend you apply broadly (a couple in the top-10, most in the 11-30 range, some in the 30+ range to be "safe"). I do NOT advocate for speaking about your specific research interests in your statement of purpose, because I think if it's not a departmental interest they will be likely to reject you. Simply say you're interested in high dimensional statistics or something.
  16. Upvote
    trynagetby got a reaction from StatsG0d in Is Biostatistics becoming outdated in the industry, outside regulatory writing?   
    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.
  17. Upvote
    trynagetby reacted to statsguy in Is Biostatistics becoming outdated in the industry, outside regulatory writing?   
    You may or may not find the position you're looking for. To be frank, a lot of problems in industry just don't need the latest cutting-edge methods or complicated simulations. There is a reason why tools like linear regression and the two sample t-test have been around forever - they are quick and easy, and they work. 
    Many years ago I was talking to a PhD data scientist at a FAANG company who was doing A/B testing. I'm pretty well-versed in experimental design and assumed they would be using the latest and greatest computer-general designs. Turns out their bread-and-butter technique was the full two-level factorial design analyzed using standard ANOVA, something a competent undergraduate could probably do.  This was probably 7 years ago so things may have changed... but maybe not because they seemed really happy with their results.
    Your best bet is to learn as much coding as possible (R + Python) in your free time. A PhD in Stats would be good although it's probably going to be a grind. I'm not sure how much your MS in Biostats will get you if you start fresh at a PhD Stats program. You'll also have to consider 5+ years at low pay, no benefits like 401k, missed raises/promotions you would've gotten in industry... but that's a personal decision. Financially the PhD may not be the clear winner at all in your case.
    Another path you can consider is perhaps sticking it out a few years, and maybe getting an MBA later? If the management track would ever be of interest to you. 
     
     
  18. Like
    trynagetby reacted to statenth in 2021 Applicant Profiles and Admission Results for Statistics/Biostatistics   
    Undergrad Institution: QS Asian U top 100
    Major(s): Statistics
    GPA: 3.78 after conversion Type of Student: International male

    GRE General Test:
    Q: 169 (94)
    V: 152 (53)
    W: 4 (55)

    TOEFL Score: 100=25+25+24+26

    Grad Institution: Same with undergrad Major: Statistics
    GPA: not good
      Programs Applying: Statistics/Biostatistics Ph.D. programs   Research Experience: one methodology publication in CSAM, two application publications in domestic journals (text analysis and GLM), one theory paper under review
    Awards/Honors/Recognitions: honorable mention for a poster presentation
    Pertinent Activities or Jobs: TA at grad school, part-time lecturer at a corporation Letters of Recommendation: two from profs with whom I worked on papers, one from advisor (strongest in terms of the personal relationships) Math/Statistics Grades:  Calculus I & II, Linear Algebra, Mathematical Statistics I & II, real analyses, 20 statistics major courses in total (took about 95% of available major courses until graduation), database, digital logic  
    OSU - Statistics / Admitted in late Mar / Accepted MSU - Statistics / Admitted in early Mar / Declined U Iowa - Statistics / Admitted in early Mar / Declined U of SC - Statistics / Admitted in mid Feb / Declined UCSB - Statistics / Waitlisted - Admitted on Apr 14 / Declined   CSU, UCR - Withdrawn   ... AND 15 REJECTIONS   I'm pretty happy in that I got into one of my very best options even though I did not earn MS in prestigious US programs and get recommendations from well-known American professors. I can say that the profile evaluations and program recommendations people make in this forum are quite accurate and helpful even for minority in the applicant population like me. @bayessays@Stat Assistant Professor@StatsG0d I truly appreciate your help and advices!!
  19. Like
    trynagetby got a reaction from Stat01243 in 2021 Applicant Profiles and Admission Results for Statistics/Biostatistics   
    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.
  20. Upvote
    trynagetby got a reaction from SunSean in 2021 Applicant Profiles and Admission Results for Statistics/Biostatistics   
    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.
  21. Upvote
    trynagetby got a reaction from stat magic in 2021 Applicant Profiles and Admission Results for Statistics/Biostatistics   
    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.
  22. Upvote
    trynagetby got a reaction from Aspiring_stats_student2312 in Fall 2022 Evaluation for Stats / biostats phd?   
    Word of warning, UT Austin is way more competitive than its rankings would indicate (probably as competitive as like TAMU/UNC/Winsconsin/NCSU).
  23. Upvote
    trynagetby reacted to bayessays in Stats Program Comparison: UCLA vs University of Washington   
    Not even close, these programs are not in the same tier.  UW
  24. Upvote
    trynagetby reacted to DanielWarlock in Choosing Stats PhD Program: Harvard, Stanford, Columbia, MIT EECS, MIT ECON   
    Given your interest, I think Harvard is best fit. Imai has affiliation at Kennedy school, Neil Shephard is affiliated at economics. Murphy is also a big name here doing causal inference and reinforcement learning affiliated to CS department. There is no problem that you seek additional advisors at MIT or other Harvard departments. You can easily find someone at MIT to supplement for (3) (4). Everyone is saying Stanford stats but they are mainly about highly mathematical/theoretical high-dimensional stats and probability theory. So I guess you will need to go to their CS department to find advisors? Stanford probably is not that of a good fit for you. 
  25. Upvote
    trynagetby reacted to statsguy in Choosing Stats PhD Program: Harvard, Stanford, Columbia, MIT EECS, MIT ECON   
    It's hard to go wrong with Stanford. Assuming you actually got in to Stanford... go to Stanford. 
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