Announcement: Faculty Research Seminar in HBH 1002, Monday, April 22nd at noon

[Artur Dubrawski]

Indeed Karen!

Jenna is a co-founder of what we often call "Ina's Workshop" on ML
applications in Healthcare at NeurIPS, a hugely successful annual event at
which some of us have presented (for the former Autonian Ina Fiterau who
came up with the idea for the workshop).

Cheers
Artur



On Tue, Apr 16, 2019 at 11:05 PM Lujie Chen <lujiec at andrew.cmu.edu> wrote:

> This talk could be interesting to many of us.
>
> Thanks,
> Karen
>
> On Tue, Apr 16, 2019 at 3:45 PM Steven Paschke <stevenp at andrew.cmu.edu>
> wrote:
>
>> Dear seminar attendees:
>>
>>
>>
>>
>>
>> Please join us in Hamburg Hall, *Room 1002,* on *Monday, April 22nd 2019*,
>> at *noon – 1:20 PM*.  * Jenna Wiens,* Assistant Professor of Computer
>> Science and Engineering (CSE) in the College of Engineering at the
>> University of Michigan*, *will present at the Faculty Research Seminar.
>>   If you interested to meet with her, please sign up for a meeting on the Social
>> Sciences Seminar Tracker
>> <https://econ.tepper.cmu.edu/seminars/seminar.asp?sort=1&short=Y>
>>
>> Title: “*White Coat, Black Box: Augmenting Clinical Care with AI in the
>> Era of Deep Learning*”
>>
>>
>>
>> Abstract: Though the potential impact of machine learning in healthcare
>> warrants genuine enthusiasm, the increasing computerization of the field is
>> still often seen as a negative rather than a positive. The limited adoption
>> of machine learning in healthcare to date points to the fact that there
>> remain important challenges. In this talk, I will highlight two key
>> challenges related to applying machine learning in healthcare: i)
>> interpretability and ii) small sample size. First, machine learning has
>> often been criticized for producing ‘black boxes.’ In this talk, I will
>> argue that interpretability is neither necessary nor sufficient,
>> demonstrating that even interpretable models can lack common sense. To
>> address this issue, we propose a novel regularization method that enables
>> the incorporation of domain knowledge during model training, leading to
>> increased robustness. Second, machine learning techniques benefit from
>> large amounts of data. However, oftentimes in healthcare we find ourselves
>> in data poor settings (i.e., small sample sizes). I will show how domain
>> knowledge can help guide architecture choices and efficiently make use of
>> available data. In summary, there’s a critical need for machine learning in
>> healthcare; however, the safe and meaningful adoption of these techniques
>> requires close collaboration in interdisciplinary teams.
>>
>>
>>
>> Bio: Jenna Wiens is a Morris Wellman Assistant Professor of Computer
>> Science and Engineering (CSE) at the University of Michigan in Ann Arbor.
>> Her primary research interests lie at the intersection of machine learning,
>> data mining, and healthcare. Dr. Wiens received her PhD from MIT in 2014,
>> was named Forbes 30 under 30 in Science and Healthcare in 2015, received an
>> NSF CAREER Award in 2016, and was recently named to the MIT Tech Review's
>> list of Innovators Under 35.
>>
>>
>>
>> *Lunch will be served.*
>>
>>
>>
>>
>>
>>
>>
>> Steven Paschke
>>
>> Faculty Assistant
>>
>> Heinz College of Information Systems and Public Policy
>>
>> Carnegie Mellon University
>>
>> 2107 Hamburg Hall
>>
>> 5000 Forbes Avenue
>>
>> Pittsburgh, PA 15213
>>
>> 412.268.1185
>>
>>
>>
>
>
> --
>
> ==================
>
> Karen (Lujie) Chen
> Ph.D. Candidate in Information Systems, Heinz College
> PIER Fellow (Program of Interdisciplinary Educational Research)
> Member of Auton Lab, Robotics Institute
> Newell-Simon Hall 3124
> Carnegie Mellon University
> Pittsburgh, PA 15213
>
>
>
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