Breakdown of CS faculty hires in 2017

Craig E. Wills of WPI recently wrote a report that describes the outcomes of advertised CS faculty positions across institutions in 2017. It was a follow-up to a previous report on the CS positions advertised in 2017.  The report contains a wealth of information about the number of faculty positions filled at different institutions.

In summary, 244 of the 323 advertised positions were fulfilled, giving an aggregate 75% success rate.  Not surprisingly, this success varied by institution type: 90% of the positions advertised by the top 100 graduate schools according to U.S. News Rankings were filled, whereas other PhD-granting institutions, Masters-granting institutions, and Bachelors-granting institutions had 67%, 66%, and 69% success rates, respectively.

Wills also looked at the faculty positions by research area.  I’ll focus on three:

  • AI/DM/ML: artificial intelligence, computational linguistics, data mining, machine learning, natural language processing, text analytics
  • CompSci: computational biology, computational life science, computational medicine, computational neuroscience (you get the picture…)
  • Security: cryptography, forensics, information assurance, privacy, security

The figure below shows the percent of faculty positions sought for each field on the x-axis and the percent of faculty positions filled for each field on the y-axis:


Points that lie on the red x=y line indicate that the percent of faculty positions filled exactly matched the percent of faculty positions sought.  Let’s look at the three largest outliers:

  • AI/DM/ML was sought for 11% of the positions by area, but ended up filling 21% of the positions.
  • DataSci was sought for 16% of the positions by area, but ended up filling only 7% of the positions.
  • Security was sought for 23% of the positions, but ended up filling only 12% of the positions.

Wills cited many factors associated with these discrepancies, including the fact that nearly a quarter of the positions did not specify an area of interest in their ad.  Additionally, institutions simply did not end up hiring in the areas of interest, either because they could not find candidates in that area or they found better candidates in other areas.  Areas could also be satisfied with multiple fields (for example AI/DM/ML or DataSci accounted for 27% of the positions sought and ended up filling 28% of the positions when combined).

Another factor that Wills considered was the number of Ph.D.s produced by area (based on Taulbee Survey results):


It’s good to be in Security, since only 6% of the Ph.D.s produced are in this area compared to the demand of 23% of the positions sought in this area.  It’s also good to be in AI/DM/ML because over 20% of the faculty positions were filled in this area, even if the job ads didn’t specify it.

Overall, the report was an interesting read – I’m looking forward to seeing these trends over time.


Female Code Breakers

Here’s a fascinating story about the women who helped break codes during WWII.  The article appeared as part of ACM TechNews, and is excerpted from the book Code Girls: The Untold Story of the American Women Code Breakers of World War II by Liza Mundy.

via The Secret History of the Female Code Breakers Who Helped Defeat the Nazis – POLITICO Magazine

(Thanks to Barbara Ryder, emeritus professor and former chair of Computer Science at Virginia Tech, for the pointer)

New Reed CompBio Blog

As part of a recently-funded collaborative REU (generously supported by the CRA-W), my colleague Derek Applewhite and I are working with undergraduates to study machine learning methods to predict genes that regulate cell movement patterns in schizophrenia.  The team will post their work on new a Reed College blog, The Pathway Not Taken, and I may re-post selected pieces here.  The first post gives a general idea of the problem we will work on, and how biology and computer science are intertwined in the project.

Summer Reading List

For the first time in years, I’m making an effort to read some books for fun this summer.  I even made a list! There’s a theme, though – I might throw in a mystery novel for good measure.

  1. The Gene: an Intimate History by Siddhartha Mukherjee.  This book, by the author of The Emperor of all Maladies (which I wrote a bit about in an earlier post), is a detailed history of genes – from initial theories to current events. image from Google Books.
  2. Inventing the Mathematician: Gender, Race, and Our Cultural Understanding of Mathematics by Sara N. Hottinger.  The author is a professor of Women’s and Gender Studies.  I first of her book after reading her piece on the Inside Higher Ed blog, where she described why she decided to pursue a degree in feminist studies despite her passion and aptitude for mathematics. inventingbook
  3. The Fuzzy and the Techie: Why the Liberal Arts Will Rule the Digital World by Scott Hartley.  The author is a venture capitalist who writes about a new generation of entrepreneurs with a mix of STEM and liberal arts training.  fuzzy


Fixing science, one researcher at a time

A recent column in Nature caught my attention today.  The piece, No researcher is too junior to fix science by John Tregoning, talks about the problematic competitiveness of science.  Tregoning ends the column with an appeal to combat this current scientific culture.

Let’s strive instead to stand together. One science historian called last month’s science march unprecedented in its scale and breadth. That energy and optimism need not dissipate — it should be funnelled into making the system function better. The pay-off might not be immediate, but let’s play the long game so that all can win.

I couldn’t have put it better.