PITTSBURGH SECTION EXECUTIVE AND ADMINISTRATIVE MEETING (October HYBRID)
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Location for this event is virtual. Meeting link and call-in information will be emailed one hour before the event.
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For those new to the Pittsburgh Section Executive and Administrative Meeting, we welcome all IEEE members in the area to attend! This is a standing meeting held on the 3rd Thursday of every month. We review our upcoming activities relevant to engineers in our section.
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Date and Time
Location
Hosts
Registration
- Date: 19 Oct 2023
- Time: 05:00 PM to 09:00 PM
- All times are (UTC-04:00) Eastern Time (US & Canada)
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- 101 Research Way
- Morgantown, West Virginia
- United States 26505
- Building: Advanced Engineering Research Building
- Room Number: 120/256
- Starts 03 October 2023 08:31 PM
- Ends 18 October 2023 11:59 PM
- All times are (UTC-04:00) Eastern Time (US & Canada)
- No Admission Charge
- Menu: Meat, No Meat, Virtual (No Food)
Speakers
Prashnna Gyawali
AI in Healthcare: Challenges and opportunities
Over the last decade, there has been phenomenal success in AI algorithms, driven mainly by
supervised deep learning, across various domains, including computer vision and NLP. However, similar
performance in health and medicine remains challenging due to current models’ ineptness to generalize across
unseen data (e.g., new patient cohort). First, unlike other domains, it’s expensive to curate training datasets with
well-annotated labels for the healthcare domain. Second, depending upon data modalities, different data
attributes such as patient’s age, sex, race, and anatomical variations often confound with the predictive task,
exhibiting disparities in performance across various patient subgroups. Furthermore, adopting AI models in a
real-world clinical setting would require robust explanation and interpretation. In this talk, I will present work
on these critical aspects of healthcare AI. First, I will present my work on learning unbiased deep learning models
by disentangling (or separating) representations. Second, I will discuss the importance of interpretability in
healthcare and the challenges current deep learning models face. I will then present my recent work to address
some of the difficulties around interpretability. Finally, I will envision future opportunities such as foundation
models and federated multimodal learning.
Biography:
Prashnna K Gyawali is an Assistant Professor in the Lane Department of Computer Science and
Electrical Engineering at West Virginia University. Previously, he was at the School of Medicine at Stanford
University as a postdoctoral scholar. He did his Ph.D. Ph.D. in Computing and Information Sciences from the
Rochester Institute of Technology (RIT). His research lies at the intersection of machine learning and healthcare.
He is interested in developing robust and fair AI models for real-world problems in health and medicine –
primarily to improve models’ generalizability to diverse patient groups. During his Ph.D., he interned at Google
Health and Verisk Analytics. He has published over 20 peer-reviewed research articles, including at premier
conferences such as ICLR, ICDM, MICCAI, and IPMI, and in impactful journals like Nature Medicine, Nature
Communications, IEEE Transactions of Biomedical Engineering, and Computers in Biology and Medicine.
Address:West Virginia University, , Morgantown, West Virginia, United States, 26505
Agenda
First 30 minutes of the event are reserved for socializing. The meeting portion starts at 7 pm (eastern time zone).
The meeting agenda will be emailed on Monday the week of the event for early registrants.
Prior to the meeting, a lecture will be held in AERB room 120 (details of the lecture below). This will take place from 5:00-6:00, and afterwards we will migrate to room 256 for the meeting.
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