Designing Personalized and Privacy-Aware Machine Learning for Promoting Well-Being

Recent converging advances in sensing and computing, including ambulatory technologies, allow the unobtrusive 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 can afford us useful insights into human behavior empowering physical and mental health, the available data in such applications involve various challenges related to the scarce amount of labels, the high variability across individuals, and the strong presence of privacy-sensitive information. This prevents machine learning systems from making reliable predictions degrading their performance and compromising user trust. This talk will present approaches to address these challenges by: (1) incorporating the inherent inter-individual variability through subject- and group-specific models of human behavior; (2) designing generalizable models of human-related outcomes through novel weakly supervised algorithms; and (3) learning bio-behavioral signal representations that preserve facets of information related to the human state (e.g., emotion), while eliminating information related to a person’s identity. We will demonstrate the effectiveness of the proposed approaches through examples in public speaking training, family well-being, and work performance.

Dr. Theodora Chaspari

Assistant Professor, Computer Science and Engineering, Texas A&M on September 4, 2020 at 10:00 AM in Zoom Webinar
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Theodora Chaspari is an Assistant Professor at the Computer Science & Engineering Department in Texas A&M University. She has received the Bachelor of Science (2010) in Electrical and Computer Engineering from the National Technical University of Athens, Greece and the Master of Science (2012) and Ph.D. (2017) in Electrical Engineering from the University of Southern California. Between 2010-2017 she was working as a Research Assistant at the Signal Analysis and Interpretation Laboratory at USC. She has also been a Lab Associate Intern at Disney Research (summer 2015). Theodora's research interests lie in the areas of affective computing, signal processing, data science, and machine learning. She is a recipient of the USC Annenberg Graduate Fellowship 2010, USC Women in Science and Engineering Merit Fellowship 2015, and the TAMU CSE Graduate Faculty Teaching Excellence Award 2019. Papers co-authored with her students have been nominated and won awards at the ACM BuildSys 2019, IEEE ACI I 2019, ASCE i3CE 2019, and IEEE BSN 2018 conferences. She has served in various conference organization committees (ACM ACII 2017/2019, IEEE BSN 2018, ACM ICMI 2018/2020) and her work is supported by federal and private funding sources (NSF, IARPA, AFRL, EiF, TAMU DoR).

Electrical and Computer Engineering Colloquia

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