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"Hot" areas in Biostats/Stats?


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Hi all, I was wondering if any areas within Biostats/Stats are considered especially "hot" these days? This shouldn't dictate one's research interest of course, but I feel like it should play a factor (with the caveat that a hot field now may be all but forgotten in a decade).

 

From doing some cursory research it seems like:

 

Biostats:

 

- statistical genetics--with human sequencing becoming ever cheaper, I think statistical geneticists will be in great demand

- computational neuroscience (especially with Obama's BRAIN initiative in the US)--how much room is there for biostatisticians in comp. neuroscience though? Seems like its more dominated by applied mathematicians and computer scientists

 

Stats:


- machine learning--no brainer.

- empirical bayes--trying to estimate baysian hyperparameters from data is becoming ever more important with larger datasets... (or is it?)

- artificial intelligence.

 

Would appreciate any thoughts, especially from faculty/PhD students.

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  • 1 month later...

I would also add causal inference (which isn't exactly "new" but i suppose it's still a research field very opened, as a lot of researchers are on it), and the quantile approach for conditional models (the latter may be biased a little bit since it is my research of interest)!

If I had to "choose" one research field I'd definetly lean towards one of these two, assuming that I am interested in either the inference or conditional models (and survival analysis) areas.

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  • 4 years later...

I was browsing through and that that this is an important topic for current and prospective students alike. From my field/department (biostatistics), I can tell you that there is a large emphasis on:

  1. Genomics
  2. Precision medicine
  3. Imaging
  4. Causal inference

What I would be interested in knowing / discussing is which "hot" areas in biostatistics are more theoretical. Some students prefer theory over applications or vice versa. From the above, I would argue that from more applied to more theoretical, it would go genomics, imaging, precision medicine, causal inference.

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In mathematical statistics, one of the hottest areas of research currently is high-dimensional data analysis and "big data" (especially the case where p >> n, p = # of covariates, n = sample size). Some top journals in statistics flat-out reject any submissions that deal with regression and/or variable selection unless the p>>n scenario is examined. 

I would also say that unsupervised learning (particularly clustering and deep learning/neural networks), graphical models, and ensemble methods are hot right now.

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6 hours ago, footballman2399 said:

I was browsing through and that that this is an important topic for current and prospective students alike. From my field/department (biostatistics), I can tell you that there is a large emphasis on:

  1. Genomics
  2. Precision medicine
  3. Imaging
  4. Causal inference

What I would be interested in knowing / discussing is which "hot" areas in biostatistics are more theoretical. Some students prefer theory over applications or vice versa. From the above, I would argue that from more applied to more theoretical, it would go genomics, imaging, precision medicine, causal inference.

I hope this isn't lazy of me but do you know where I could find a good topic paper or get a taste of what is being done in those fields?

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19 hours ago, statbiostat2017 said:

I hope this isn't lazy of me but do you know where I could find a good topic paper or get a taste of what is being done in those fields?

The journal Statistical Science has quasi-review articles; you might want to browse some recent issues.

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On 11/13/2017 at 3:13 PM, Applied Math to Stat said:

In mathematical statistics, one of the hottest areas of research currently is high-dimensional data analysis and "big data" (especially the case where p >> n, p = # of covariates, n = sample size). Some top journals in statistics flat-out reject any submissions that deal with regression and/or variable selection unless the p>>n scenario is examined. 

I would also say that unsupervised learning (particularly clustering and deep learning/neural networks), graphical models, and ensemble methods are hot right now.

I should have added dimension reduction. Thanks for adding it. That's an important discipline in stats / biostats alike.

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  • 2 months later...
  • 4 weeks later...

There are two questions I always want to know the answer: 

If I want to work for a pharmaceutical company in the future, which research topics should I choose? 

How does statistical genetics compare with statistical genomics? And which one is hotter?

Any suggestions?

 

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On 2/24/2018 at 5:37 AM, Laoceberg said:

There are two questions I always want to know the answer: 

If I want to work for a pharmaceutical company in the future, which research topics should I choose? 

How does statistical genetics compare with statistical genomics? And which one is hotter?

Any suggestions?

 

The "classical" area for this field is clinical trials. 

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I think it is worth noting that doing a PhD is about YOU and your personal interests. It is a bit difficult to predict what will or will not be "hot" years from now. For example, Dirichlet processes did not explode in the field of machine learning/computer science until about a decade after they were first introduced. SVM used to be a very hot topic and neural networks lost some of their appeal, but now it's totally the opposite: currently there is a lot of renewed interest in neural networks/deep learning, but not so much in SVM. So trying to go into what's most "fashionable" at the moment can be a tricky game, because the fields of stat and biostat are constantly changing and evolving.  

I wouldn't recommend doing a PhD on a topic that is extremely archaic to the point that only a tiny number of old profs are still conducting research in it. But I also would prioritize personal interests above anything else. A PhD is a time-consuming commitment, and you don't want to be miserable the whole time you're doing research. So skim a few papers by faculty members and find something that you are truly passionate about! 

Edited by Applied Math to Stat
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Also,if you're interested in faculty jobs, you can always change your research area when you do a postdoc or when you become a professor. Many people don't stay in the same area as their PhD thesis topic. For example, one of my advisor's former students wrote a thesis on small area estimation, but she abandoned it shortly after and now is an assistant professor working on computational social science and record linkage problems (at a top school, I might add). I myself have had postdoc interviews in fields as diverse as causal inference, electronic health records, pseudolikelihood methods, nonparametrics, etc., and I did not write my thesis on ANY of these topics.

By the time you get to the postdoc or faculty stage, most people will assume that you have enough maturity to teach yourself a new field and conduct independent research in new areas. So this just suggests to me that your thesis research should be on something that personally interests you above all else -- since you can always switch to a different area later, if you want.

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