My two cents tips on PhD application for STAT

[Statmaniac]

As a person who experienced the whole application process last year and currently attending one of the top biostat Ph.D. programs, I want to drop a piece of useful advice for anyone thinking of applying for Ph.D. programs in stat/cs/biostat/data science, etc. 

Please don't restrict yourself to stat Ph.D. programs. Consider closely related programs like Electrical Engineering, Operations Research, Industrial Engineering, Applied math, Computational Math, and contact the stat/ML professors of interest if they are willing/able to advise students outside their department.

Because of the recent booms in the big data/machine learning, the competition for Ph.D. programs in Statistics has been fiercer than any other period. Even with stellar records of mathematics courses and research experience, it is tough to crack in the top 30 Ph.D. programs. Therefore, if you want to research statistics/machine learning, don't just restrict yourself in statistics or biostatistics program. In my current program, many EE, Applied Math, Mechanical engineering Ph.D. students are working with statistics/biostatistics professors. As long as you have sufficient background in mathematics and coding, you can work on the field of statistics and machine learning. In fact, my honest advice would be to pick a Ph.D. program in which you could benefit more. For example, if you have already taken all Ph.D. level statistics courses, then having more exposures on CS courses or numerical method/optimization/control theory courses would be better. I think Applied math/Computational Math/Electrical Engineering/ Industrial Engineering programs would expand one's horizons, and still, students would be able to conduct researches in theoretical statistics and machine learning. 

And for those who insist applying on stat/biostat Ph.D. programs, just because you think this would be more beneficial to become a professor in stat/biostat, I want to say this is simply false. Many stat/biostat departments have hired people from Electrical Engineering/Industrial Engineering and Applied math, recently. It is all about the research you are doing, not the department you are affiliated with. Having experienced the first semester in my program, I personally believe these programs actually equip students with more tools to develop methodologies compared to traditional stat or biostat programs.

Edited November 21, 2019 by Statmaniac

[Taxxi]

Thank you for valuable advice.

I still suppose that competition for pure mathematics is fiercer than statistics? I am not sure whether applied mathematics is easier than statistics for that matter. At least for me, considering operations research seems to be on option. What is the difference between statistics and operations research? Their focus might be different but it seems like they require equivalent courses in most cases (practically).

[Statmaniac]

8 hours ago, Taxxi said:

Thank you for valuable advice.

I still suppose that competition for pure mathematics is fiercer than statistics? I am not sure whether applied mathematics is easier than statistics for that matter. At least for me, considering operations research seems to be on option. What is the difference between statistics and operations research? Their focus might be different but it seems like they require equivalent courses in most cases (practically).

I personally believe competition for statistics is worse than pure mathematics. This is because 1) there are many well-established mathematics departments and their cohort size is usually larger than statistics. For example, Upenn Wharton and Cornell only admit 4-5 Ph.D. every year. Several top 20 statistics departments have a very small size of incoming PhD student. 2) Compared to the past, the current trend among international students is that many talented students from pure math change their careers as statisticians or machine learners. 

There is no huge difference between statistics and operations research, but my impression is that the latter tends to focus more on optimization methods. As optimization theory plays a central role in modern statistics, statistics PhD students would have to learn anyway. Oftentimes they are housed in the same department like UNC and Georgia Tech. Both schools have prominent statisticians and probabilists, but somehow I have the impression that statistics PhD applicants don't really consider Georgia Tech. 

As there is no huge difference, this is why I am telling applicants to consider a wide range of closely related programs. Besides the coursework, there is no huge difference among statistics and IE/EE, if you end up doing research in statistics or ML. Departments like EE or IE have a larger number of incoming PhD students. Some of my friends got flat out rejections from top 30 statistics PhD programs, while they have gotten into several top 10 IE/EE PhD programs easily. And I believe, there are many mathematics/applied mathematics/computational mathematics programs which have larger size than statistics programs. 

[Taxxi]

T_T I am now even more torn about which program I should pursue. It seems like I have to check each university how many Ph.Ds are admitted in each department(mathematics, applied mathematics, statistics, operations research). I have absolutely no idea how to get those numbers. I mean, I'm fine as long as I can study pure mathematics and statistics along the way.

 

Do I have to use a random generator and... ha....... that doesn't sound good. Nonetheless your insight was indeed helpful.

[bayessays]

I think the OP is really overstating the similarities here. There is overlap, but 90% of the professors in these departments do things that are not statistics and the core set of classes is very different. If you know you want to be a statistician, you will have the most options in a statistics department. It depends a lot on what you're interested in. 

[Statmaniac]

7 hours ago, bayessays said:

I think the OP is really overstating the similarities here. There is overlap, but 90% of the professors in these departments do things that are not statistics and the core set of classes is very different. If you know you want to be a statistician, you will have the most options in a statistics department. It depends a lot on what you're interested in. 

My impression is that the field statistics nowadays is very loosely-defined these days. People working on reinforcement learning are mainly from CS department and papers in NIPS, AISTATS and etc. There are tons of people working on monte carlo methods, and bayesian statistics in geoscience and applied math. They still publish papers in statistics. In fact, many professors from the top-notch statistics department had different training in their PhD. For example, look at Anrea Montanari from stanford and Michael Jordan, Martin Wainwright from berkeley and etc. They are one of the pioneers of the field of statistics. Schools like Georgia Tech, and Princeton have all of the statistics people, including Jeff Wu and Jianqing Fan, respectively, in the IE/OR department. At the Stanford, there is a bulk of students from EE and ICME working with statistics professors.

So my point is that, as there are many applicants who had sufficient background in statistics education, I think it is better to expand their horizons through taking courses in other closely related domains that would be useful for statistics research. Indeed for programs like OR, IE and even EE, students can choose statistics/machine learning track these days and take almost the same courses as in statistics phd students. Actually, I think their coursework is much more beneficial to those who want to do methodological work compared to the biostatistics program or some traditional statistics program that emphasizes sampling theory or design of experiments.

I just have seen so many applicants who finished graduate-level statistics courses who could not get into statistics PhD programs. I am just telling applicants not just to restrict themselves to the statistics program.

[bayessays]

And all that is true. But these are the exceptions to the rule. You are seeing EE students who do statistics stuff, but you're not seeing the 90% of them who don't do anything related to statistics. You're seeing the applied math student who took a class with you, but not the 90% who have no interest in stats.  You can do statistical ML research in a CS department, but the focus of the classes and research is so different that most people in such programs will have nothing in common.

[Statmaniac]

All of these are on the premise that the current popularity of statistics PhD programs is due to ML, AI, Statistical Learning and etc. So I have to disagree with you in that it is relatively easier to find someone of similar interest if one is in the statistics department.

First of all, the research focuses on each statistics departments are just vast. Schools like Columbia, still has a heavy focus on financial math. I doubt someone in financial math shares more common interest than a person working on ML in CS department or someone with an information theory background in the EE department if one wants to work on modern statistical methodologies.

Secondly, the field of statistics has changed dramatically since the emergence of big data, machine learning. Many statistics departments are trying to get affiliation with faculties outside the department. Look at Yale. They recently changed their name of the department as the Statistics and Data Science and have hired several applied mathematicians/computer scientists working on the theoretical side of statistical learning, information theory and graph theory. Look at Chicago. Almost half of faculties in the statistics department are doing things not considered as traditional statistics. I haven't seen any department without recent hiring coming from a non-statistics domain. I don't necessarily think being a statistics/biostatistics PhD students have a better chance to find someone whom you want to work with if one is interested in modern statistical methodologies. In particular, a lot of my cohorts are interested in machine learning/statistical learning, not genetics nor epidemiology. More than half of the students are taking courses in CS/EE and interact with them to learn more from them. 

And I must say your percentage looks a bit of exaggeration. Even if it is true, the incoming class PhD size of EE is like 40-50 students. Ten percent out of it is still comparable to the number of incoming Wharton or Cornell statistics PhD students. And more importantly, once you finish the coursework, students within the same research group or advisor communicate and interact more often. Look at the Jordan or Wainwright's group. A significant portion of people in their labs are from non-statistics group. And there is a relatively large group of statistics people in IE and OR in general if you look at profiles of Jianqing Fan or Jeff Wu. 

Edited November 23, 2019 by Statmaniac

[icantdoalgebra]

On 11/21/2019 at 7:51 AM, Statmaniac said:

Please don't restrict yourself to stat Ph.D. programs. Consider closely related programs like Electrical Engineering, Operations Research, Industrial Engineering, Applied math, Computational Math, and contact the stat/ML professors of interest if they are willing/able to advise students outside their department

Be careful with directly contacting ML professors since they usually have somewhere on their website that they won't respond to inquiries from prospective Ph.D. students. 

However I will throw this out, I think the acceptance for the Ph.D. program in statistics at Berkeley is roughly around 10%; the most recent numbers I have for the EECS admission are from the class entering 2017, where there were 3000 applications for 40 spots, which essentially an order of magnitude lower (however I may be remembering incorrectly since its been a few years since I've heard this number from a professor), and I would imagine that the schools that have strong ML and stats programs (Stanford, Washington, CMU) have a similar story.