Connectionists: ICBINB Monthly Seminar Series Talk: Cynthia Rudin

[Francisco J. Rodríguez Ruiz]

Dear all,

We are pleased to announce that the third speaker of the *“I Can’t Believe
It’s Not Better!” (**ICBINB)* virtual seminar series will be *Cynthia Rudin**
(Duke University)*. More details about this series and the talk are below.

The *"I Can't Believe It's Not Better!" (ICBINB) monthly online seminar
series* seeks to shine a light on the "stuck" phase of research. Speakers
will tell us about their most beautiful ideas that didn't "work", about
when theory didn't match practice, or perhaps just when the going got
tough. These talks will let us peek inside the file drawer of unexpected
results and peer behind the curtain to see the real story of *how real
researchers did real research*.

*When: *April 7th, 2022 at 10am EDT / 4pm CEST

*Where: *RSVP for the Zoom link here:
https://us02web.zoom.us/meeting/register/tZ0kf-GpqTksH9QNO7jTWhfOYen1kLGkH_Rz

*Title:* *Applications Really Matter (And Publishing Them Is Essential For
AI & Data Science)*

*Abstract: **Many of us want to work on real-world machine learning
problems that matter. However, it’s really hard for us to focus on such
problems because it is extremely difficult to publish applied machine
learning papers in top venues. I will argue that the lack of respect for
applied papers has several wide-ranging applications:*




*1) Benefits to Science: We are unable to leverage scientific lessons
learned through applications if we cannot publish them. Applications should
actually be driving ML methods development. It is important to point out
that applied papers *are* scientific. A boring bake-off or technical report
is not a scientific applied paper. An applied scientific paper provides
knowledge that is systematized and generalizes, just like any good
scientific paper in any area of science.2) Benefits to the Real World: We
publish overly complicated methods when simpler ones would suffice. If we
could focus on solving problems rather than developing methods, this issue
could vanish. *Much more importantly, if we actually focus on problems that
benefit humanity, we might actually solve them.*3) Broadening our
Community: By limiting our top venues mainly to methodology papers, we
limit our community to those who care primarily about methods development.
This further limits our community to those who come from narrow training
pipelines. It also limits our field to exclude those whose primary goal is
to directly improve the world. A really good applied data scientist from
any country should be able to publish in a top tier venue in data science
or AI.4) Freeing our Top Scientists: By tying promotions of our top data
scientists to publication venues that accept (essentially only)
methodology, it means our top scientists cannot focus on real-world
problems. This is particularly problematic if one wants to publish a data
science paper in an area for which a specialized journal does not exist.My
proposed fix is to have tracks in major ML conferences and journals that
focus on applications.*

*Bio: **Cynthia Rudin is a professor at Duke University. Her goal is to
design predictive models that are understandable to humans. She applies
machine learning in many areas, such as healthcare, criminal justice, and
energy reliability. She is the recipient of the 2022 Squirrel AI Award for
Artificial Intelligence for the Benefit of Humanity from AAAI (the “Nobel
Prize of AI”). She is a fellow of the American Statistical Association, the
Institute of Mathematical Statistics, and AAAI. She is a three-time winner
of the INFORMS Innovative Applications in Analytics Award. Her work has
been featured in news outlets including the NY Times, Washington Post, Wall
Street Journal, and Boston Globe.*


For more information and for ways to get involved, please visit us at
http://icbinb.cc/, Tweet to us @ICBINBWorkhop
<https://twitter.com/ICBINBWorkshop>, or email us at
cant.believe.it.is.not.better at gmail.com.

--
Best wishes,
The ICBINB Organizers
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