Human-centered computing for healthcare and education: From robust machine intelligence to trustworthy human-technology partnership
This event is free and open to the publicAdd to Google Calendar
Zoom link for remote participants, passcode: 897525
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 (ML) algorithms have been heralded as promising solutions to addressing pressing societal challenges, public and expert determination of whether this integration is a good prospect is widely debated. 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. This talk will describe new ML algorithms for trustworthy human-centered computing focusing on four main pillars of trustworthiness, namely robustness, privacy preservation, explainability, and fairness. We will first present our work on personalized, generalizable, and context-aware ML models for reliably quantifying
behavioral outcomes. Following that, we will discuss a privacy-preserving emotion recognition framework through user anonymization and examine factors of socio-demographic bias in AI systems that may perpetuate social disparities in human-centered analytics. Finally, we will present our recent work on human-AI collaboration by shedding light on how human stakeholders (e.g., clinicians) interact with AI/ML along dimensions of trust formation, maintenance, and repair. We will demonstrate the effectiveness of the proposed approaches through examples in mental health, public health, workforce training and re-skilling, and team science.
Bio: 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. Theodora’s research interests lie in the areas of health analytics, affective computing, data science, and machine learning. She is a recipient of the NSF CAREER Award (2021), TAMU Montague Teaching Award (2021), TAMU Dean of Engineering Excellence Award (2022), and TAMU Young Faculty Fellow Award (2022). 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, Guest Editor in IEEE Transactions on Affective Computing, 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). She has further developed and taught several graduate and undergraduate courses in AI and ML. Her work is supported by federal and private funding sources, including the NSF, NIH, NASA, IARPA, AFRL, AFOSR, General Motors, Keck Foundation, and Engineering Information Foundation.