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.