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 solutions to empowering physical and mental healthcare, various confounding factors prevent the widespread adoption of such technologies, including the complex and heterogeneous data spaces, limited number of 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 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 bio- behaviorally-aware 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, work performance, and communication anxiety training.

Theodora Chaspari is an Assistant Professor in the Computer Science & Engineering Department at Texas 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 (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 ACII 2019, ASCE i3CE 2019, and IEEE BSN 2018 conferences. She has developed and taught several graduate and undergraduate courses in AI and machine learning, and served in various conference organization committees (ACM ACII 2017/2019/2021, IEEE BSN 2018, ACM ICMI 2018/2020). Her work is supported by federal and private funding sources, including the NSF, NIH, NASA, IARPA, AFRL, EiF, and TAMU DoR.