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T-Shaped People and Academia

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Being on the lookout for recruiting new students for my new research
group
, I wanted to distil some of my observations
into textual form. This post deals with my impression that academia is
doing a disservice to ‘T-shaped persons,’ i.e. persons combining deep
expertise in one or more fields with an ability to collaborate across
areas
. T-shaped persons
are often contrasted with I-shaped persons, commonly known as
experts.1
Given the state of the art in the software industry,2 T-shaped people
are a sought-for commodity: the current ‘framework du jour’ might be
obsolete in a few years, but the horizontal bar of a T-shaped person
ensures that they can also contribute to other aspects of a project, and
quite likely will develop new vertical bars, i.e. new expertise over
time.3

Output above all else.
As I am looking at the many resumes and motivational letters from
excellent students all over the globe, it occurred to me that academia
is not necessarily kind to the Ts: the primary metric in academic hiring
is still the ‘scientific output,’ which is measured in publications such
as journal articles, conference articles, etc. In machine learning
research, the fast pace of the field now has created a situation in
which even aspiring Ph.D. candidates are often expected to have a few
publications under the belt already. Coming from mathematics, where
one paper every few years (!) is considered the mark of
a productive mathematician, these expectations are bonkers to me, and
I wonder how many capable candidates end up pursuing other goals because
of these invisible barriers.4

Hiring people: I before T.
In any case, many graduate schools and resume reviewers tend to ignore
the breadth of a person’s skills, focusing instead only on the depth. By
taking the scientific output, in conjunction with maybe some relevant
coursework, as the be-all and end-all, any other skills tend to
be missed. At best, it might raise an eyebrow or two in magnanimous
surprise: falling prey to precisely the issue I describe in this post,
I noticed myself doing this for candidates that already convinced me
because of their academic track record! At worst, additional skills
might be considered detrimental upon review, because a T-shaped person
may have a smaller number of papers than others.5

This is a problem for machine learning research. Given the
aforementioned fast pace in the field, excellent research necessitates
moving away from silos! There will of course always be a demand for
people that have deep domain expertise, but many facets of contemporary
research can be addressed just as well by a T-shaped person, in my
experience.

Ts as multipliers.
Throughout my career, the most impactful projects always had a T-shaped
person onboard. This person would usually not be an expert in the
subject matter, but would be able to provide the direly-needed
scaffolding and foundation of a project that is all too often ignored
in the initial phase, until it comes back later on with full swing to
wreak havoc. This includes, for instance:

  • Setting up a suitable build system.
  • Writing some unit tests for the code.
  • Creating a skeleton of the models that are to be developed.
  • Designing nice mock-ups or logos.
  • Creating animations or graphics for presentations.

In my experience, these skills—and others—are not commonly considered when hiring
someone. And to some extent, having fallen into the same trap, I fully
understand this! Paraphrasing Leonard McCoy in Star Trek,
we want to state ‘I’m a researcher, not a(n) $X$,’ with $X in$ ${$software
developer, designer, marketing person, artist, $dots}$

Progress in our interdisciplinary times, however, also requires people
that are sufficiently skilled to tackle these other tasks. Even though
such tasks might seemingly be unrelated to the overall scientific goal
of creating new knowledge, they are still a fundamental part of the
modern process of scientific inquiry.

What to do now? I am writing this post because I detected an
inconsistency in my personal observations and my actions: virtually all
the projects that contained a good mix of I-shaped and T-shaped persons
succeeded extraordinarily well. Yet, upon recruiting my own team,
I started to prefer I-shaped profiles over T-shaped ones, instead of
striving for a solid balance. I caught myself in time and will
subsequently pay more attention to this bias of mine—I hope that
others will do the same!

Machine learning and academia in general are enriched by teams
comprising diverse backgrounds and personalities. We should strive to
achieve this.

(In case more incentives are needed: the largest groups and research
divisions are already doing this—they are hiring not only for academic
output, but also based on general software skills, for instance. If it’s
good enough for them, it should certainly be good enough for the rest of
us.)

Happy recruiting for 2022 and the years to come, until next time!

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