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I have been thinking about a second PhD for the last year. I am currently a doctoral candidate in civil engineering at University of Texas at Austin (ranked 6th in my discipline) and working as a student statistical consultant at the university consulting center. I am working on spatio-temporal modeling of count data using Bayesian hierarchical models, with computationally efficient techniques for my dissertation. (I will have publications on this very soon; two of them are in review). I do have 2 publications in my area but their topic is the application of statistical models. I am also going to get a Masters in statistics next semester along with my PhD in civil engineering. I have done many courses related to Bayesian statistics including graduate level mathematical statistics, theoretical MCMC, stochastic volatility and time series models, statistical consulting, advanced econometrics (non-Bayesian), discrete choice modeling and one course on data mining (graduate level). In my field, I see massive datasets but very minimal statistical expertise, particularly on the big data side. This has motivated me to pursue something beyond my Phd and beyond my discipline.

I am very interested in handling large datasets and perhaps, machine learning applications. I would like to know whether a PhD in machine learning is going to help me realise my dreams. I do not have any formal research experience in data mining or machine learning. But, I do a lot of Bayesian hierarchical modeling on smaller datasets. Given my experience, I am not sure whether I can secure admission to a good program in the machine learning area. I appreciate any suggestions and advice on whether to pursue another PhD and the feasibility of securing admission to a good program in a PhD machine learning track. I am assuming basic financial assistance for any PhD program.

Thanks much in advance.

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    I think the answers to this question will be similar to those for this question. A PhD is mainly about being able to work independently and do novel research. Doing it twice is not that helpful. – Marc Claesen Jan 31 '14 at 11:21
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    You have missed out a critical part: What is your objective? Do you want to go into academic research or industry research. Do you want to do domain specific research (e.g. big data within an engineering context) or go to a completely different area? Also econometrics and machine learning are drastically different areas. – Jase Jan 31 '14 at 12:10
  • @Jase: Sorry for my incomplete question. But, My major goal is to work in industry research in machine learning. I do not want to worry too much about the domain though. Yah, I see your point on the huge gap of econometrics and the machine learning. I just mentioned the course name but, I really work on Bayesian hierarchical predictive models. Please comment back if you want to add more to discussion. Thanks much for your inputs. – user1245 Jan 31 '14 at 17:53
  • @user1245 Just to be clear, is your plan to move away from civil engineering after your PhD? Or are you planning to somehow combine these areas? – Faheem Mitha Jan 31 '14 at 18:58
  • @FaheemMitha: I have been really working on data modeling since last 4 years, although the domain is civil engineering. I developed lot of interest in this and I am even ready to move away from civil engineering. Ideally, a combination of both will be great, but its not a constraint for me. Thanks. – user1245 Jan 31 '14 at 19:12
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As other people already stated, multiple PhD's is almost certainly not a good idea. How to transition into machine learning depends on where you want to work:

  • academia, you can try to get a postdoc position in machine learning, in that way you can build your career by writing publications and get up to speed with machine learning during your postdoc research. This could be problematic as you might have to compete with people that have a PhD in machine learning. But this can be overcome during the interview stage.
  • industry, you can simply apply to entry level machine learning positions (look for data science positions) and take it from there. Once you convince a company that you are a worthwhile addition, you can learn the ropes on the job.

In general, I would recommend already getting up-to-speed a bit in machine learning, try some online tutorials, etc. This can really help in securing a new position in either academia or industry.

  • Thanks a lot for your inputs. I will definitely try implementing your suggestions. – user1245 Jan 31 '14 at 18:05
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This site has had such questions come up before. Usually (as in this case), I think the correct answer is that doing a second PhD is not necessary, and not a good use of your time. In my opinion, shared by many (most?), multiple PhDs are very rarely a good idea. As people have remarked elsewhere, a big part of a PhD is learning how to do research (hopefully) under supervision and guidance. The supervision and guidance do not necessarily happen in practice, anyway. Usually much of a PhD is deadwood and bureaucracy, like required courses.

My personal opinion are that PhDs are not a necessary qualification in any case. Before they existed people did just fine. Check out the history of the PhD on Wikipedia

If you know enough statistics to write research papers, you just go ahead and write papers. Once you know enough to do so, it is not rocket science. Machine learning is just statistics done by computer scientists for some reason, maybe because the statisticians are not interested in doing it.

I think your general aims and perspective are sensible. Knowing things like Bayesian statistics and modelling is useful when working with data.

In my field, I see massive datasets but very minimal statistical expertise, particularly on the big data side.

Yes, this sounds accurate. Statistical expertise is not very commonly available or applied by non-statisticians, but it would be useful to them if they knew how to do it correctly.

A couple of suggestions wrt things you could usefully focus on:

  1. English language skills. Very important for a researcher. This is particularly important if one does not speak English natively.

  2. Computational skills. This is getting to be also a truism, but people in academia don't usually know what they are doing re programming and software development. A better understanding in these areas will probably pay dividends for researchers in the applied sciences, though there are differences of opinion as to how much.

Neither of these skill sets would be usefully served by getting a second PhD.

I have a PhD in Statistics, but I don't think I need to have one to offer the preceding opinions. Mostly they are just common sense.

  • Thanks a lot for your patient and informative reply. I am currently working on polishing my English and computational skills. I totally understand and agree with you on the whole issue. However, I am a bit concerned about justifying the essential skills for applying to a machine learning based research position with my current experience. This was the primary reason to pursue another Phd, which can be a platform to gain hands on experience on solving machine learning research problems and probably, add some published works to my resume. – user1245 Jan 31 '14 at 18:03
  • Could you please advise me whether it is wise to apply for industry based research positions with my current experience ? Thanks much in advance. – user1245 Jan 31 '14 at 18:04
  • @user1245 I'm not really qualified to advise about applying for the CS industry positions you are interested in, sorry. I don't know a thing about CS industry hiring. However, from what you have written, it does not sound to me like the right thing to do is go for a second PhD. If you are concerned about your qualifications, the obvious thing is to talk to people who are connected to such positions; either people who work in such positions or people that hire for them, and ask what you need to do to make yourself more attractive for such jobs. – Faheem Mitha Jan 31 '14 at 18:10
  • It's unlikely they will suggest a second PhD, I think, but if they do, this would make a reasonable question for this site. :-) "Industry insiders suggest a second PhD in machine learning, what you do you think?" One more general comment - work on your basic theory. Often people, even quite senior people, are poor on basic theory. I mean statistical/mathematical theory in your context. Oh, and wrt to people in these jobs, you could also talk to them about the employer expectations re background for people coming into these jobs. – Faheem Mitha Jan 31 '14 at 18:11
  • That sounds very reasonable. I will try reaching industry guys and post back with such a question (if necessary) :-) Thanks again. – user1245 Jan 31 '14 at 18:14

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