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Date: Wednesday, May 11, 2022
Time: 11:00 a.m. - 12:30 p.m.
Location: TSRB 509
Virtual: Zoom - See additional instructions below
Speaker: Theodora Chaspari
Speaker’s Title: Assistant Professor, Computer Science & Engineering Department
Speaker’s Affiliation: Texas A&M University
Seminar Title: Trustworthy human-centered machine intelligence for well-being and healthcare
Abstract: Recent converging advances in sensing and computing allow the ambulatory long-term tracking of individuals yielding a rich set of real-life multimodal bio-behavioral measurements, such as speech, physiology, and facial expressions. While bio-behavioral measurements coupled with artificial intelligence (AI) and machine learning algorithms have been heralded as promising solutionsto empowering physical and mental healthcare, various confounding factors prevent the widespread adoption of such technologies, including the complex and heterogeneous data spaces, limited numberof labels, and high inter-individual variability. At the same time, interactions between humans and AI are increasingly moving away from simple diagnosis of human outcomes to collaborative relationships, in which humans work side-by-side with AI systems for carrying out a set of common goals. The first part of this talk will present approaches to address computational challenges related to robust human-centered machine learning, including the design of subject- and group-specific machine learning models with emphasis on generalizability to unseen conditions. The second part of the talk will focus on AI-assisted decision making across two main pillars of trustworthiness, namely privacy-preservation and fairness. Specifically, we will present a privacy-preserving emotion recognition framework through user anonymization and discuss factors of socio-demographic bias in AI systems that may perpetuate social disparities. We will demonstrate the effectiveness of the proposed approaches through examples in daily well-being, mental health, communication anxiety, and workforce training and re-skilling.
Speaker Bio: Theodora Chaspari is an Assistant Professor in the Computer Science & Engineering Department atTexas A&M University. She has received her Bachelor of Science (2010) in Electrical & Computer Engineering from the National Technical University of Athens, Greece and her Master of Science (2012) and Ph.D. (2017) in Electrical Engineering from the University of Southern California. Between 2010 and 2017 she worked as a Research Assistant at the Signal Analysis and Interpretation Laboratory at USC. She has also been a Lab Associate Intern at Disney Research (2015). Theodora’s research interests lie in the areas of signal processing, machine learning, data science, and affective computing. She is a recipient of the TEES Dean of Engineering Excellence Award (2022), TEES Young Faculty Award (2022), NSF CAREER Award (2021), TAMU Montague Teaching Award (2021), USC Women in Science and Engineering Merit Fellowship (2015), and USC Annenberg Graduate Fellowship (2010). Papers co-authored with her students have been nominated and won awards at the ASC 2021, ACM BuildSys 2019, IEEE ACII 2019, ASCE i3CE 2019, and IEEE BSN 2018 conferences. She is serving as an Editor of the Elsevier Computer Speech & Language, and in various conference organization committees (ACM ICMI 2023/2020/2018, ACM IUI 2021, ACM KDD 2022, IEEE ACII 2022/2021/2019/2017, IEEE BSN 2018).Her work is supported by federal and private funding sources, including the NSF, NIH, NASA, IARPA, AFRL, AFOSR, General Motors, Keck Foundation, and the Engineering Information Foundation.
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