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Undergrad Institution: Ivy

Major: Math + Joint masters in Statistics 
GPA: 3.85 Undergrad, 3.8 grad
Type of Student: Domestic Asian Female

GRE General Test: Have not taken yet
 
Programs Applying: Statistics PhD or Math PhD
 
Research Experience: One year with an assistant professor in Biostatistics studying evolutionary processes, One year with a full professor in Biostatistics. 4 months in a government lab of applied research with computer vision. No papers.
 
Letters of Recommendation: The two professors that I've done research which should be strong. Also one from my undergrad advisor which isn't as strong.
 
All are undergrad level unless otherwise indicated.
 
Math Courses:  Multivariable Calculus, Linear Algebra, Applied Linear Algebra, Differential Equations, Intro to Analysis, Analysis I, Analysis II, Functional Analysis, Measure Theoretic Stochastic Processes, Measure Theoretic Probability  (PhD Level), PhD Level Analysis (will be in progress when I apply).
 
Statistics Courses (Master's Level):  Introduction to Stochastic Processes I, Introduction to Stochastic Processes II, Mathematical Statistics, Independent Study, Linear Models and ANOVA.
Statistics Courses (PhD Level): Statistical Learning I, Statistical Learning II, Linear Models I, Linear Models II, Linear Models III, Information Theory, Bayesian Stats I, Bayesian Stats II. Mathematical Statistics (will be in progress when I apply)
 
I'm pretty clueless about the whole process. I've looked at the websites for a few programs thus far and it appears as if I meet the basic requirements for most of them, but I'm not sure which I should apply to given my profile (nor am I sure about the distinction between the biostats and stats programs at some schools. Can/should you apply to both?) 
 
Thanks for your help!
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Posted (edited)

Specifically I'd like to know the most competitive school that I would have a good shot at. This was the crux of my question. Schools that I've spoken to simply refer me to the basic requirements, which is unhelpful as most applicants ought to have met these requirements in the first place. Also I realized I've made a typo; I'm only considering applying to Stats PhDs (I copy pasted another thread to use as a template haha).

Edited by kimmy
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You probably need to start by figuring out if you want to do a PhD in stat/biostat or math. While there are some differences between stat and biostat programs, they are tiny compared to the gulf between stat and math programs. From your background (coursework & research experience), you seem like a much better fit for (bio)stat than math, and would likely be competitive for a lot of very good stat PhD programs. 

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3.85 GPA from an Ivy and a bunch of math classes including functional analysis, measure-theoretic probability, and stochastic processes, plus solid research exprerience. This is a very strong profile.

If I were you, I would apply to mainly top 15 USNWR Statistics/Biostat PhD programs. With strong letters of recommendation, I think you will be able to get into virtually all Biostat programs including Harvard and JHU. For statistics, you have a very good shot at UC Berkeley, UChicago, Carnegie Mellon, UPenn Wharton, Duke, etc. and I wouldn't be surprised if you also got admitted to Stanford too (I heard Stanford Statistics Dept is waiving the math subject GRE requirement this year for its PhD program?).

Edited by Stat Assistant Professor
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An important omission on the above suggestions is MIT. MIT does not have a statistics department but it is possible to study statistics there through EECS, math, OR, or CSE track. The matter fact is that when you talk about the "hot areas" such as statistical/machine learning, inference algorithms, high dimensional statistics, MIT is as strong as (or is probably stronger than) Stanford or UCBerkeley. A list of "emerging superstars" there: Elchanan Mossel, Sasha Rakhlin, Philippe Rigollet, Guy Bresler, David Gamarnik, Ankur Moitra, and many many more. 

I'm surprised that this forum doesn't even mention MIT when it has one of the most powerful stats communities there. Not to mention that when you get into MIT, you virtually get into Harvard because you can have supervisors/collaborators at both schools and take any courses, joins any reading groups you like at both places. 

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33 minutes ago, DanielWarlock said:

I'm surprised that this forum doesn't even mention MIT when it has one of the most powerful stats communities there.

How many people on this forum could get into or would want to go to MIT'S math or EECS PhD programs?

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For OP - pick whatever programs you are interested in going to, irrespective of ranking, and apply to 5-10 of them. You won't get into all of them, but you'll certainly get into some. If you want stats programs with a decent applied group (which I'm guessing you do, based on your research), I'd particularly recommend Berkeley, UW, CMU. Maybe UW in particular, if you want strong biostats exposure.

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1 hour ago, DanielWarlock said:

An important omission on the above suggestions is MIT. MIT does not have a statistics department but it is possible to study statistics there through EECS, math, OR, or CSE track. The matter fact is that when you talk about the "hot areas" such as statistical/machine learning, inference algorithms, high dimensional statistics, MIT is as strong as (or is probably stronger than) Stanford or UCBerkeley. A list of "emerging superstars" there: Elchanan Mossel, Sasha Rakhlin, Philippe Rigollet, Guy Bresler, David Gamarnik, Ankur Moitra, and many many more. 

I'm surprised that this forum doesn't even mention MIT when it has one of the most powerful stats communities there. Not to mention that when you get into MIT, you virtually get into Harvard because you can have supervisors/collaborators at both schools and take any courses, joins any reading groups you like at both places. 

Is there any insight that can be given into this? Specifically, is it feasible for me with my profile to be applying to schools' Math PhD programs if they don't have a statistics program? (also, why was this downvoted?)

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Just now, kimmy said:

Is there any insight that can be given into this? Specifically, is it feasible for me with my profile to be applying to schools' Math PhD programs if they don't have a statistics program? (also, why was this downvoted?)

It depends on the math department. This situation is very rare. MIT and UCSD have good statisticians in math departments, but you don't have a profile to get into math programs like this. In the other end of the spectrum, University of Arkansas has some fine statisticians in their math program, and you could apply to a program like that. Texas Tech is another math department with statisticians, I believe. There are very few cases where people should be applying to math PhD programs if they want to be statisticians - so very few, that it is not generally worth mentioning. The comment was downvoted because MIT is not an important statistics program that needs to be mentioned nor is it a useful suggestion given your profile. 

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6 hours ago, kimmy said:

Is there any insight that can be given into this? Specifically, is it feasible for me with my profile to be applying to schools' Math PhD programs if they don't have a statistics program? (also, why was this downvoted?)

This guy has a history of posting... offbeat takes. (The last time I downvoted one of them, he actually went back through my history and downvoted every one of my posts).

If you want to be a statistician, it's probably a good idea to go to a stats program. While your advisor is important, so are your required courses/quals/classmates/seminars/etc.

Most of the profs he listed are pretty theoretical (makes sense they're in a math dept), OP seems more applied. For stat ML generally, they're a fine school but putting them at the same level as Berkeley/Stanford is a bit much.

I hear MIT's OR department is great, is pretty applied, although I don't think that's quite what you're looking for. Their EECS dept may be worth a look if you want to do that type of ML, it'd be a reach admissions-wise, but not completely crazy (though the advisors you'd be looking at are entirely separate from the listed advisors)

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37 minutes ago, insert_name_here said:

This guy has a history of posting... offbeat takes. (The last time I downvoted one of them, he actually went back through my history and downvoted every one of my posts).

If you want to be a statistician, it's probably a good idea to go to a stats program. While your advisor is important, so are your required courses/quals/classmates/seminars/etc.

Most of the profs he listed are pretty theoretical (makes sense they're in a math dept), OP seems more applied. For stat ML generally, they're a fine school but putting them at the same level as Berkeley/Stanford is a bit much.

I hear MIT's OR department is great, is pretty applied, although I don't think that's quite what you're looking for. Their EECS dept may be worth a look if you want to do that type of ML, it'd be a reach admissions-wise, but not completely crazy (though the advisors you'd be looking at are entirely separate from the listed advisors)

Ah, I see. Thanks for this; it clears things up.

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Hi, @kimmy! When I first started researching programs for my Ph.D. in English, I had my thesis advisor/mentor help me. Do you have a professor that you were close with that could maybe help you? I found that my advisor knew a lot of the faculty in many Ph.D. in English programs, so that helped me out a lot. It's always good to have someone who has experience who can assist you in the application process. Good luck with everything, and stay safe! 🙂

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On 9/3/2020 at 12:32 PM, bayessays said:

How many people on this forum could get into or would want to go to MIT'S math or EECS PhD programs?

OP is very strong and is considering pursuing a math phd. There are many other people here like OP who are very interested in the kind of research happening at MIT, e.g. see MIT's statistics and data science institute: https://stat.mit.edu/people_categories/core/

We always suggest them to try Stanford, UCB or even CMU. But no one mentions MIT ever. I think this is an important omission for our community. No one even mentions MIT as one of the "reach schools" when they can totally apply to EECS or Math stating that they are interested in statistics.

Yes, a lot of them will not be good enough--but the same applies to Stanford etc. For those who DO have a chance, e.g. OP, MIT deserves serious consideration. 

Edited by DanielWarlock
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On 9/3/2020 at 8:33 PM, insert_name_here said:

This guy has a history of posting... offbeat takes. (The last time I downvoted one of them, he actually went back through my history and downvoted every one of my posts).

If you want to be a statistician, it's probably a good idea to go to a stats program. While your advisor is important, so are your required courses/quals/classmates/seminars/etc.

Most of the profs he listed are pretty theoretical (makes sense they're in a math dept), OP seems more applied. For stat ML generally, they're a fine school but putting them at the same level as Berkeley/Stanford is a bit much.

I hear MIT's OR department is great, is pretty applied, although I don't think that's quite what you're looking for. Their EECS dept may be worth a look if you want to do that type of ML, it'd be a reach admissions-wise, but not completely crazy (though the advisors you'd be looking at are entirely separate from the listed advisors)

I must confess that I downvoted some of your posts, but you also downvoted all of my posts in retaliation. All of this is a bit childish, but also largely benign. I trust that both of us are in good faith, especially in terms of contributing to this forum which we all love.

OP obviously has interest in theory research and has taken very theory-leaning classes and is even considering applying to math. That's why I picked that particular list of professors. 

What's great about MIT is the range of options you have beyond statistics in a traditional sense--there are areas where MIT does not focus (e.g. biostatistics) but you have to admit a lot of research happening there is cutting edge, and cannot be find anywhere else, not to mention most of the resources at harvard is available to mit students also. Why would it be off-beat to consider applying to MIT as one of the "reach schools"? 

Edited by DanielWarlock
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I think @DanielWarlock has a point. MIT has a great list of faculties; one could research in statistics. Let me share my perspective here. 

Many statistics programs are getting a lot of attention because of the big data, machine learning, etc. However, one should note that there are so many programs that offer outdated curriculums. Honestly, who uses UMVUE, complete statistics? I haven't seen any of these in any papers I have read in top statistics journals published within 20 years. What's worse is that these programs still teach courses like survey sampling, generalized linear models(GLM), which had little impact on the data science's current emerge. I am not looking down on these two subjects, but one should note that these courses have almost nothing to do with the current data boom. In machine learning, you spend at most one lecture on GLM, but these outdated curricula still insist students take a full semester-length of GLM/survey sampling and other outdated topics. Now that I am working on so-called hot or emerging statistics fields, I feel my past education from statistics program was completely useless. Courses like Information Theory, Optimization, Graphical models that were not the core curriculum in the statistics program have become essential in modern statistics research. These are somehow more often taught in EECS/CS/Math departments.

Aligned with what I said, I think if one wants to have a better edge in applications in the IT industry or new methodological works in statistics journals, it would be better to choose EECS/applied math/ORFE programs like MIT or Princeton. Please take a look at the new Stanford/Berkeley faculties profiles, many of them were not trained in the Statistics PhD program. I think those on the level to get admitted to Stanford/Berkeley stats are on the level to gain admittance on MIT EECS/Princeton applied math. If not, programs like Georgia Tech IE/Upenn Applied Math have successfully yielded top students who acquired tenure track positions in top statistics programs. As far as I know, oftentimes, these programs require applicants to contact potential supervisor first, so with your background, I think it is worth considering. That being said, compared to the IT industry, biomedical applications are somewhat slowly accepting these new machine learning methods. I think this is why top biostatistics departments are still teaching outdated methodologies. In terms of the recent statistical methodological work, EECS departments like MIT have far more contributed than many other statistics programs, which cannot get out of their old fame. Also even at MIT, there are a lot of people working on computational biology.

Therefore, as @cyberwulf said, you would have to decide between traditional stat programs(many biostat programs and some stats) vs. data-sciency programs(stat programs like Stanford, Berkeley, CMU, Yale, Columbia, and CS/OR/applied math programs). Fields like genetics are highly computational, so even if you go to the latter program, the chance to work in biomedical fields is quite high. However, given the current training offered by biostat or traditional stat programs, I think the other way would be quite challenging. One way to distinguish these two types of programs would be to ask if the collaborations between departments(CS/applied math/OR) are frequent or have a lot of faculties with joint appointments. Having a separate Data Science institute or Initiative is also a sign of more data-sciency program. Lastly look into the curriculum they offer.

Edited by Statmaniac
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12 hours ago, Statmaniac said:

I think @DanielWarlock has a point. MIT has a great list of faculties; one could research in statistics. Let me share my perspective here. 

Many statistics programs are getting a lot of attention because of the big data, machine learning, etc. However, one should note that there are so many programs that offer outdated curriculums. Honestly, who uses UMVUE, complete statistics? I haven't seen any of these in any papers I have read in top statistics journals published within 20 years. What's worse is that these programs still teach courses like survey sampling, generalized linear models(GLM), which had little impact on the data science's current emerge. I am not looking down on these two subjects, but one should note that these courses have almost nothing to do with the current data boom. In machine learning, you spend at most one lecture on GLM, but these outdated curricula still insist students take a full semester-length of GLM/survey sampling and other outdated topics. Now that I am working on so-called hot or emerging statistics fields, I feel my past education from statistics program was completely useless. Courses like Information Theory, Optimization, Graphical models that were not the core curriculum in the statistics program have become essential in modern statistics research. These are somehow more often taught in EECS/CS/Math departments.

Aligned with what I said, I think if one wants to have a better edge in applications in the IT industry or new methodological works in statistics journals, it would be better to choose EECS/applied math/ORFE programs like MIT or Princeton. Please take a look at the new Stanford/Berkeley faculties profiles, many of them were not trained in the Statistics PhD program. I think those on the level to get admitted to Stanford/Berkeley stats are on the level to gain admittance on MIT EECS/Princeton applied math. If not, programs like Georgia Tech IE/Upenn Applied Math have successfully yielded top students who acquired tenure track positions in top statistics programs. As far as I know, oftentimes, these programs require applicants to contact potential supervisor first, so with your background, I think it is worth considering. That being said, compared to the IT industry, biomedical applications are somewhat slowly accepting these new machine learning methods. I think this is why top biostatistics departments are still teaching outdated methodologies. In terms of the recent statistical methodological work, EECS departments like MIT have far more contributed than many other statistics programs, which cannot get out of their old fame. Also even at MIT, there are a lot of people working on computational biology.

Therefore, as @cyberwulf said, you would have to decide between traditional stat programs(many biostat programs and some stats) vs. data-sciency programs(stat programs like Stanford, Berkeley, CMU, Yale, Columbia, and CS/OR/applied math programs). Fields like genetics are highly computational, so even if you go to the latter program, the chance to work in biomedical fields is quite high. However, given the current training offered by biostat or traditional stat programs, I think the other way would be quite challenging. One way to distinguish these two types of programs would be to ask if the collaborations between departments(CS/applied math/OR) are frequent or have a lot of faculties with joint appointments. Having a separate Data Science institute or Initiative is also a sign of more data-sciency program. Lastly look into the curriculum they offer.

This is exactly what I thought. The recent hires at my department all work on the "modern statistics topics", especially concerning high-dimensional problems and statistical learning. Some senior professors who used to work on "outdated" stuff have also "switched field" and started to publish in some of these emerging subfields. Even so, when I look at MIT, our department still doesn't quite measure up on this kind of research.

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On 9/4/2020 at 5:53 PM, DanielWarlock said:

I must confess that I downvoted some of your posts, but you also downvoted all of my posts in retaliation. All of this is a bit childish, but also largely benign. I trust that both of us are in good faith, especially in terms of contributing to this forum which we all love.

OP obviously has interest in theory research and has taken very theory-leaning classes and is even considering applying to math. That's why I picked that particular list of professors. 

What's great about MIT is the range of options you have beyond statistics in a traditional sense--there are areas where MIT does not focus (e.g. biostatistics) but you have to admit a lot of research happening there is cutting edge, and cannot be find anywhere else, not to mention most of the resources at harvard is available to mit students also. Why would it be off-beat to consider applying to MIT as one of the "reach schools"? 

Lol so uhhhh this guy just spam downvoted all my recent posts again? 

If you agree this is childish, can you just stop it?

Or maybe some mods can do something...

I sincerely have no interest in this sideshow.

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On 9/5/2020 at 4:43 AM, Statmaniac said:

I think @DanielWarlock has a point. MIT has a great list of faculties; one could research in statistics. Let me share my perspective here. 

Many statistics programs are getting a lot of attention because of the big data, machine learning, etc. However, one should note that there are so many programs that offer outdated curriculums. Honestly, who uses UMVUE, complete statistics? I haven't seen any of these in any papers I have read in top statistics journals published within 20 years. What's worse is that these programs still teach courses like survey sampling, generalized linear models(GLM), which had little impact on the data science's current emerge. I am not looking down on these two subjects, but one should note that these courses have almost nothing to do with the current data boom. In machine learning, you spend at most one lecture on GLM, but these outdated curricula still insist students take a full semester-length of GLM/survey sampling and other outdated topics. Now that I am working on so-called hot or emerging statistics fields, I feel my past education from statistics program was completely useless. Courses like Information Theory, Optimization, Graphical models that were not the core curriculum in the statistics program have become essential in modern statistics research. These are somehow more often taught in EECS/CS/Math departments.

Aligned with what I said, I think if one wants to have a better edge in applications in the IT industry or new methodological works in statistics journals, it would be better to choose EECS/applied math/ORFE programs like MIT or Princeton. Please take a look at the new Stanford/Berkeley faculties profiles, many of them were not trained in the Statistics PhD program. I think those on the level to get admitted to Stanford/Berkeley stats are on the level to gain admittance on MIT EECS/Princeton applied math. If not, programs like Georgia Tech IE/Upenn Applied Math have successfully yielded top students who acquired tenure track positions in top statistics programs. As far as I know, oftentimes, these programs require applicants to contact potential supervisor first, so with your background, I think it is worth considering. That being said, compared to the IT industry, biomedical applications are somewhat slowly accepting these new machine learning methods. I think this is why top biostatistics departments are still teaching outdated methodologies. In terms of the recent statistical methodological work, EECS departments like MIT have far more contributed than many other statistics programs, which cannot get out of their old fame. Also even at MIT, there are a lot of people working on computational biology.

Therefore, as @cyberwulf said, you would have to decide between traditional stat programs(many biostat programs and some stats) vs. data-sciency programs(stat programs like Stanford, Berkeley, CMU, Yale, Columbia, and CS/OR/applied math programs). Fields like genetics are highly computational, so even if you go to the latter program, the chance to work in biomedical fields is quite high. However, given the current training offered by biostat or traditional stat programs, I think the other way would be quite challenging. One way to distinguish these two types of programs would be to ask if the collaborations between departments(CS/applied math/OR) are frequent or have a lot of faculties with joint appointments. Having a separate Data Science institute or Initiative is also a sign of more data-sciency program. Lastly look into the curriculum they offer.

I'm sorry, I just can't let this stand unchallenged. It is complete nonsense to say that GLMs have had little impact on data science. Talk to any practicing data scientist and they'll tell you that a lot of the models actually being used in practice are relatively simple regression models. And survey sampling? That's a special case of weighting, which is heavily used in machine learning in the case of rare events (and also to increase algorithmic fairness). 

If all you're interested in doing is creating algorithms that do something faster or more accurately, sure, maybe you don't need a ton of statistical training. But, if that's all you're interested in doing, you're not really interested in being a statistician! Statisticians seek to develop tools for better data analysis, which includes quantifying uncertainty, carrying out inference, and improving model interpretability. It's impossible to do that without a solid grounding in the kind of old-fashioned statistics you look down your nose at.

Lastly, your conclusion that it is better to attend EECS/ORFE programs like MIT/Princeton because graduates from these programs have obtained positions in top stat departments is flawed. Top departments are often looking to find the smartest people they can hire, on the logic that they'd rather have a rock star who does something a little bit outside the norm than an "excellent-but-not-exceptional" faculty member who fits easily within the field. Sometimes, those brilliant people are in non-stat programs, but they're being hired because of their brains not because of their training. Indeed, if they were equally brilliant but had been trained in a stat department, they might be even more attractive candidates! Most people in EECS/ORFE programs will end up in those disciplines; entering such a program with the goal of entering a different field upon graduating is taking a huge gamble that you'll be so exceptional that hiring committees will overlook the fact that your research and training is unorthodox.

OK, rant over.

Edited by cyberwulf
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In addition to the above: I will also add that a lot of Statistics and Biostatistics programs do recognize the need to "update" the graduate curriculum to include more "modern" topics. Most statistics/biostatistis departments are aware of this and have either already done so or are in the process of doing so. So while you might still encounter a lot of 'classical' topics such as UMVUE, UMP test, James-Stein estimator, etc., the coursework often *does* give a splattering of more recent topics too, like high-dimensional regression, multiple testing with FDR control rather than FWER, etc.

But also, there is only so much that you can cover in classes. The subject matter of each class (e.g. probability theory, linear models, etc.) has enough material that you could easily spend a whole year or two covering subtopics in depth. You have to pick and choose what to emphasize and trust that once you give some basic foundation, the students will be able to learn other things on their own and pick up what they need for their own research.

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On 9/4/2020 at 5:31 PM, DanielWarlock said:

We always suggest them to try Stanford, UCB or even CMU. But no one mentions MIT ever. I think this is an important omission for our community. No one even mentions MIT as one of the "reach schools" when they can totally apply to EECS or Math stating that they are interested in statistics.

Isn't that more due to the fact that MIT primarily only has people in either ML or high-dimensional statistics (and in this case I feel like the opportunity of working with Candes at Stanford or Wainwright at Berkeley is probably better than being at MIT), but there's more to statistics than these two fields: what if the person wanted to do something in causal inference or post-selective inference? MIT would clearly be not as great compared to other top stats programs. Additionally what makes a good EECS application is pretty different from what makes a good stats application: applying to EECS in ML usually revolves around having published somewhere in ICML or NeurIPS (sometimes even multiple times) as an undergrad, and comparatively grades/coursework really don't matter that much. 

 

On 9/5/2020 at 2:43 AM, Statmaniac said:

Many statistics programs are getting a lot of attention because of the big data, machine learning, etc. However, one should note that there are so many programs that offer outdated curriculums. Honestly, who uses UMVUE, complete statistics? I haven't seen any of these in any papers I have read in top statistics journals published within 20 years. What's worse is that these programs still teach courses like survey sampling, generalized linear models(GLM), which had little impact on the data science's current emerge. I am not looking down on these two subjects, but one should note that these courses have almost nothing to do with the current data boom. In machine learning, you spend at most one lecture on GLM, but these outdated curricula still insist students take a full semester-length of GLM/survey sampling and other outdated topics. Now that I am working on so-called hot or emerging statistics fields, I feel my past education from statistics program was completely useless. Courses like Information Theory, Optimization, Graphical models that were not the core curriculum in the statistics program have become essential in modern statistics research. These are somehow more often taught in EECS/CS/Math departments.

Perhaps this is true in general about stats program curricula being outdated but if we are comparing Stanford/Berkeley to MIT this isn't really that true nowadays. People in the stat department who are interested in ML are encouraged to go take courses in optimization and information theory (which is what I am doing) because as you said, they are very useful. In fact one of the core courses at Berkeley gives a brief introduction to information theory because of how useful it is. But again, not everybody wants to do ML/data science/what-ever gets hyped up in the media nowadays and places like MIT EECS/Math or Princeton ORFE are really niche recommendations since they tend to lean very heavily towards a small subset of areas within statistics and have virtually no presence elsewhere. Also about your point for professorship, I know Berkeley gave out 3 offers for faculty last year, and 2 of them are for somebody with a traditional statistics background.

@kimmy sorry for the sidetracking but the people on this forum are pretty good at giving advice on applying to stats programs (probably because there are a few stats professors running around on these forums). I'd be somewhat more skeptical of any advice you'd receive here about applying to math or EECS (as to the best of my knowledge) because there aren't any similar such people on this forum and you should probably go look elsewhere for advice but do consider these options if that is something you are interested about. 

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I am sorry @kimmy if these posts were distracting to you. I just wanted to widen your perspective when applying for a graduate program. As a person who believes this forum reflects more of opinions from classical statistics/biostatistics programs, I just wanted to tell you how one PhD student feels about these programs. Of course, you need to search more in detail of these programs and carefully assess which program best fits you. My last piece of advice is that there is actually a substantial amount of people who work on something very similar or exactly the same as what classical statisticians claim to do. For example, many econometricians/professors outside the statistics department do causal inference or high-dimensional inference(like Chernozhukov in MIT). Uncertainty quantifications in weather forecasting/design of experiments have been substantially done by applied mathematicians/IE. Bayesian statistics and MCMC were heavily influenced/substantiated by people from physics/CS background(like Ryan Adams in Princeton). There have been establishment of data institute or data initiative to bring all these people together and many quantitative programs allow students to freely choose advisors no matter what department their advisor belongs to. It is unfortunate or somewhat paradoxical if they are not considered as or included into the group of statisticians given the fact that these people consistently publish papers in top statistical journals. Perhaps some statisticians/biostatisticians are not ready to embrace them, which I think is highly detrimental to the field itself. 


I have seen people with similar profiles as you get into applied math/EE/IE programs. I agree with the above post that EECS particularly CS programs are even more difficult in terms of the admission competition. In fact, I was like you, and I got accepted to several different quantitative PhD programs, including Biostat/IE/stat/applied math/Data Science with a research statement indicating I want to do modern statistical learning. You may want to check my past posts to verify this. I just want to tell you to carefully select the program not only by name but also on their actual curriculum and research. For the curriculum, pick the one that is the most flexible or up-to-date, which could help you read recent papers. The main issue with classical statistics/biostatistics programs is that the gap between coursework and the research is unnecessarily huge. I even wished I had spent the whole year self-studying without taking courses. You wouldn't be able to follow most of high-dimensional statistics/inference papers with zero background in optimization. Causal inference with no graphical model is also very hard these days. In my perspective, spending 6-8 months for taking classical statistics courses and preparing for the qualifying exams is a considerable time loss, given the fact that you need to embark on research as soon as possible to determine your advisor. There are many quantitative programs that are flexible enough in terms of the choice of advisors, not restricting to its own department or program. Besides, as I have said before, EE/Applied Math/IE are huge fields, and many programs require you to contact your potential supervisor first, so perhaps people in the Biostatistics programs are not familiar with this. Otherwise, faculties doing statistics research outside the statistics department would have no chance to recruit students. Actually, several biostatistics programs I was admitted did not allow advisors outside the department, so you may want to double-check. I hope this gives some of the things which were not seen in the above.

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4 hours ago, Statmaniac said:

I am sorry @kimmy if these posts were distracting to you. I just wanted to widen your perspective when applying for a graduate program. As a person who believes this forum reflects more of opinions from classical statistics/biostatistics programs, I just wanted to tell you how one PhD student feels about these programs. Of course, you need to search more in detail of these programs and carefully assess which program best fits you. My last piece of advice is that there is actually a substantial amount of people who work on something very similar or exactly the same as what classical statisticians claim to do. For example, many econometricians/professors outside the statistics department do causal inference or high-dimensional inference(like Chernozhukov in MIT). Uncertainty quantifications in weather forecasting/design of experiments have been substantially done by applied mathematicians/IE. Bayesian statistics and MCMC were heavily influenced/substantiated by people from physics/CS background(like Ryan Adams in Princeton). There have been establishment of data institute or data initiative to bring all these people together and many quantitative programs allow students to freely choose advisors no matter what department their advisor belongs to. It is unfortunate or somewhat paradoxical if they are not considered as or included into the group of statisticians given the fact that these people consistently publish papers in top statistical journals. Perhaps some statisticians/biostatisticians are not ready to embrace them, which I think is highly detrimental to the field itself. 


I have seen people with similar profiles as you get into applied math/EE/IE programs. I agree with the above post that EECS particularly CS programs are even more difficult in terms of the admission competition. In fact, I was like you, and I got accepted to several different quantitative PhD programs, including Biostat/IE/stat/applied math/Data Science with a research statement indicating I want to do modern statistical learning. You may want to check my past posts to verify this. I just want to tell you to carefully select the program not only by name but also on their actual curriculum and research. For the curriculum, pick the one that is the most flexible or up-to-date, which could help you read recent papers. The main issue with classical statistics/biostatistics programs is that the gap between coursework and the research is unnecessarily huge. I even wished I had spent the whole year self-studying without taking courses. You wouldn't be able to follow most of high-dimensional statistics/inference papers with zero background in optimization. Causal inference with no graphical model is also very hard these days. In my perspective, spending 6-8 months for taking classical statistics courses and preparing for the qualifying exams is a considerable time loss, given the fact that you need to embark on research as soon as possible to determine your advisor. There are many quantitative programs that are flexible enough in terms of the choice of advisors, not restricting to its own department or program. Besides, as I have said before, EE/Applied Math/IE are huge fields, and many programs require you to contact your potential supervisor first, so perhaps people in the Biostatistics programs are not familiar with this. Otherwise, faculties doing statistics research outside the statistics department would have no chance to recruit students. Actually, several biostatistics programs I was admitted did not allow advisors outside the department, so you may want to double-check. I hope this gives some of the things which were not seen in the above.

There are *many* faculty in Statistics/Biostatistics departments conducting research and publishing in the top journals and the top ML conferences in the areas you mentioned (high-dimensional statistics, causal inference, etc.). Most students are capable of self-teaching themselves these topics (or taking electives to gain some exposure to them) after they begin their research.  I think it is somewhat unreasonable to expect programs to teach students all they need to know for their research through courses (when a PhD is largely about teaching yourself and contributing new research that isn't already covered in classes) or to tailor coursework around what's "trendy" at the moment. For one, not all students are interested in the same things. Classes on optimization likely have little relevance to students who are interested in applied probability/stochastic processes, for example. Nevertheless, as I also mentioned above, most Stat and Biostat programs are taking it upon themselves to 'update' the curriculum to also include the more current topics. 

Secondly, the other fields you mentioned might have a lot of coursework too that isn't directly applicable to students' research. For example, in an Applied Math PhD, students might need to take two semesters of graduate-level Analysis with measure theory, Hilbert spaces, functional analysis, etc., as well as a lot of classes like numerical analysis, partial differential equations, etc. These students typically also need to pass several written qualifying exams. An EECS student might need to take classes on computer architecture, theoretical analysis of algorithms, etc. But if these Applied Math or EECS students then go on to conduct research in machine learning or global optimization, then it's not like all of their classes are immediately relevant to their research.

Now, some of the top programs in these other fields (like Stanford CS, Princeton Applied Math, Berkeley EECS) likely do keep the coursework requirements to a minimum (so most students are largely done with classes by the end of their first year, and students also have greater flexibility in what classes to choose -- so they probably do only take a few classes that are immediately relevant to their research). But that's mainly because the types of students that are admitted to these kinds of programs have already completed extensive graduate-level coursework as an undergrad and have already done research as an undergrad that got published in major journals or conferences. But these are exceptions rather than a general rule. Most PhD programs in Applied Math, EECS, and IE have at least two years of coursework, and certainly, not all of it is relevant to every student's research.  

Edited by Stat Assistant Professor
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