I left academia after my PhD. Here are a few reasons why:
-
The problems studied in TCS today are primarily of focus simply from previously being of focus,
with no
ground in eventual practical impact. In the field of data structures, for example, there have
been a host
of papers on the dynamic optimality of binary search trees, that is, finding the best BST in
terms of
visiting the fewest nodes for a sequence of element accesses (
Demaine et al., SICOMP
'07,
Iacono & Langerman, SODA '16,
Kozma & Saranurak, SICOMP '19, to name a few),
without asking why we are restricting to a
binary search tree in the first place. Our work considered instead a general data structure for
partial
orders, a new angle resulting in top tier publications but garnering few citations,
as it is not
what the community is currently focused on. At the end of the day, 42 paper folds gets you to
the
moon,
and practical efficiency must get beyond asymptotic analysis—O(log n) should be treated
more like a
factor of around 20—significant due to being a large constant, not due to its growth rate.
An honest assessment surmises theoretical computer science of practical significance was
largely
completed in the 20th century.
-
Academia is primarily a socialist institution. Papers and grants are voted upon by reviewers, rather
than
having merit tested in the market. This is how it has to be—research must be available to
all for progress to be made. But reviewers are far more fallible than consumers, and papers
are
often rejected for bad reasons. The incentive misalignment is palpable—reviewers often
write only a few sentences reviewing a 20+ page paper, after having multiple months to write a
review. It is this same structure that allows esoteric research to flourish and highly impactful
research to be undervalued—the feedback mechanism is a weak differentiator of good and bad
ideas.
I still believe academia is important—letting smart people pursue pure mathematics and
funding research without commercial ties serves a critical role, but it is not an efficient
system. The inability of the system to capture real value when it is
produced and a large supply of individuals eager to do the work regardless of the
compensation results in underpaid and overworked academics.
-
The academic community is extremely competitive and can be somewhat ruthless. It took a year to
publish our lattice paper (SWAT '20), since the
paper
corrected a mistake in an earlier paper (Talamo and Vocca, SICOMP
'99). The authors
of
the previous paper were consistently recruited as subreviewers and denied the issue in their
work, which
was too technical for other reviewers (and perhaps the authors themselves on 20-year-old work)
to discern. In
another example,
very little credit was given to our min-cut
paper (SWAT '20)
in Gawrychowski, Mozes, Weimann, ICALP '20, and
Mukhopadhyay & Nanongkai, STOC '20, the former of
which at
times copied even the format of our paper. While communication with both sets of authors helped
resolve
the issues, to which I am thankful, it is disheartening to have to fight for
academic credit.
For those wishing to pursue a PhD, I would first consider that there is typically much more to do in
applications than in core science, but if one loves learning and is mathematically gifted, I would
recommend
pursuing it in artificial intelligence, where the applications are of huge practical importance and
it is
still (currently) possible to make meaningful intellectual contributions. This may change in 5-10
years, as our AI is becoming so powerful as to start making these contributions itself.