Connectionists: ICBINB Monthly Seminar Series Talk: Javier Gonzalez

[Francisco J. Rodríguez Ruiz]

Dear all,

We are pleased to announce that the next speaker of the *“I Can’t Believe
It’s Not Better!” (**ICBINB)* virtual seminar series will be *Javier
Gonzalez** (**Microsoft Research**)*. 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: *September 15th, 2022 at 7am PDT / 10am EDT / 4pm CEST

*Where: *RSVP for the Zoom link here:
https://us02web.zoom.us/meeting/register/tZItf-ihqTsrH9AkDiT8GrvQXgZ1C9oSUV_D

*Title:* *I can’t believe my machine learning system is not better*

*Abstract:** Deploying and maintaining machine learning models in
real-world scenarios is hard. But why? In this talk I will talk about
several real anecdotes in which a good model (or what it was supposed to be
a good model) failed to have an impact in the real-world. We will explore
the bitter lesson that having a good machine learning model does not
necessarily imply having a good machine learning system. We will visit
examples that cover the design of microfluidic chips to study aging in
yeast cells, the use of electronic health records to predict the effects of
medical interventions and the design of large decision making systems with
many interconnected nodes. Although every application is different, I will
share the lessons learned in these areas that I hope will be useful for the
audience of the talk when addressing real-word questions in the future.*

*Bio:* *Javier González is a Principal Researcher in the Biological
NLP/Real World Evidence group
<https://www.microsoft.com/en-us/research/group/biomedical-nlp-group/>
at Health
Futures
<https://www.microsoft.com/en-us/research/lab/microsoft-health-futures/>,
Microsoft. Javier works in machine methods for healthcare with special
focus on uncertainty quantification and causal inference for precision
medicine. Before joining Microsoft, Javier was leading a team in Amazon
that developed and deployed machine learning methods for Prime Air, Alexa
and the Amazon's supply chain. Before that, he was a researcher associate
at the machine learning group of the University of Sheffield where he
worked on Bayesian optimization methods to scale the production of drugs
compounds using hamster cells. He was also the main developer of GPyOpt, a
widely used library for Bayesian optimization in the community. Javier
co-founded Inferentia Ltd. together with Andreas Damianou, Zhenwen Dai and
Neil Lawrence, a machine learning start-up that was acquired by Amazon in
2016. Between 2011 and 2013, Javier was a postdoc at the University of
Groningen where he worked on machine learning approaches to understand the
dynamics of biological systems, in particular the causes of aging in yeast.
Javier got his PhD in 2010 at Carlos III university of Madrid.*

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
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/connectionists/attachments/20220909/8ac0b8df/attachment.html>