Communications and Signal Processing Seminar

Enhancing Fairness in Deep Learning from Data and Model Perspectives

Xiaoqian WangAssistant ProfessorPurdue University
WHERE:
1311 EECS Building
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Abstract: With the widespread use of deep learning models in practical applications, there are increasing concerns that these models could perpetuate or amplify societal biases and discrimination if not properly regulated. In this talk, I will discuss approaches to enhance the fairness of deep learning models from both data and model perspectives, highlighting their applications in natural language processing and computer vision tasks. Our framework is theoretically grounded, effective in balancing model performance and fairness, and computationally efficient.

Xiaoqian Wang is an Assistant Professor of Electrical and Computer Engineering at Purdue University. She received her Ph.D. degree from the University of Pittsburgh in 2019 and her B.S. degree from Zhejiang University in 2013. Her research focuses on designing novel machine learning models for interpretability, fairness, and robustness. She also works at the intersection of machine learning, bioinformatics, and healthcare. She is the recipient of an NSF CAREER award in 2022, an AAAI Distinguished Paper Award in 2023, and is an IEEE senior member.

*** This Event will take place in a hybrid format. The location for in-person attendance will be room 1311 EECS. Attendance will also be available via Zoom.

Join Zoom Meeting: https://umich.zoom.us/j/93679028340

Meeting ID: 936 7902 8340

Passcode: XXXXXX (Will be sent via e-mail to attendees)

Zoom Passcode information is also available upon request to Kristi Rieger([email protected])

Faculty Host

Liyue ShenAssistant Professor Electrical Engineering and Computer Science