[Iefac.list] Reminder of the IISE-DAIS webinar next week

[Zimo Wang]

Greetings!

This is Zimo Wang from Binghamton University. I would like to share the
information about the upcoming IISE-DAIS webinar with the community. Thank
you so much for your help!


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Dear Colleagues,

Here is a kind reminder of the upcoming IISE-DAIS webinar. This DAIS
webinar essentially brings together members from academia, industry and
government who share a common interest in topics related to research and
practice of data analytics and information systems, promoting its
applications in the emerging areas.

Our first webinar is scheduled on Jan 19, 2021. Our guest speaker would be
Dr. Na Zou from Texas A&M University (Assistant professor, Engineering
Technology and Industrial Distribution):

*Guest Speaker: *Na Zou, Ph.D.
*Topic: *An Introduction to the Fairness in Machine Learning, Fundamental
Concepts, and Real-World Examples
*Date: *Tuesday, Jan 19, 2021
*Time: *11:00am ET
*Registration link (free of charge): *
https://attendee.gotowebinar.com/register/4702397238450382093


*Abstract*

Machine learning algorithms have achieved dramatic progress nowadays, and
are increasingly being deployed in high-stake applications, including
employment, criminal justice, personalized medicine, etc. Nevertheless,
fairness in machine learning remains a critical problem. Machine learning
algorithms have the risk of amplifying societal stereotypes by over
associating protected attributes, e.g., race and gender, with the
prediction task. It may not only limit a person's opportunity that s/he is
qualified, but also might further exacerbates social inequity.

This talk is to firstly introduce the fairness in machine learning with
real-world examples and fundamental concepts. The speaker will then discuss
fairness in machine learning from a computational perspective, including
fairness categorization and measurement, interpretability for addressing
fairness problems, detection and mitigation of model bias. At the end, the
speaker will provide a concrete example to illustrate how to mitigate
gender bias in an image captioning system with details.

*Bio*

Dr. Na Zou is currently an assistant professor in Engineering Technology
and Industrial Distribution at Texas A&M University. She was an
Instructional Assistant Professor in Industrial and Systems Engineering at
Texas A&M University from 2016 to 2020. She holds both a Ph.D. in
Industrial Engineering and a MSE in Civil, Environmental and Sustainable
Engineering from Arizona State University. Her research focuses on
interpretable machine learning, transfer learning, network modeling and
inference, and brain informatics, supported by NSF and industrial sponsors.
The research projects have resulted in publications at prestigious journals
such as Technometrics, IISE Transactions and ACM Transactions, including
one Best Paper Finalist and one Best Student Paper Finalist at INFORMS QSR
section and two featured articles at ISE Magazine. She was the recipient of
IEEE Irv Kaufman Award.
Enclosed please find the detailed information about this webinar. Please
feel free to share this announcement with your colleagues/students/research
scholars/others. Thank you!

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regards,
Zimo Wang
-- 
Zimo Wang, Ph.D.,
Assistant Professor,
Department of Systems Science and Industrial Engineering (SSIE),
State University of New York at Binghamton
Office: EB-J16
Tel.: 607-777-5012
E-mail: zimowang at binghamton.edu
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