Keynote Speakers

Shrikanth (Shri) Narayanan

University of Southern California

Signal Analysis and Interpretation Laboratory

https://sail.usc.edu/people/shri.html

Current approaches for assessing and tracking individual mental and behavioral well-being, and potential risk factors, through self-reports or behavioral interviews can be incomplete, unreliable, or simply unavailable to be of use in critical situations. Converging developments across the machine intelligence ecosystem, from human-centered multimodal sensing, signal processing and machine learning methods, are enabling new possibilities both in advancing science and in the creation of technologies supporting health research and its translation to practice.

Sensing, computing and AI-driven interface technologies are creating unprecedented opportunities for acquisition, analysis and sharing of diverse, information-rich data that allow causal and multimodal characterization of an individual’s mental state with granularity, context, and scale not possible before. This includes behavioral machine intelligence—approaches for quantitatively and objectively understanding human behavior—with a specific focus on multimodal communicative, affective and social behavior.  The talk will introduce aspects of multimodal signal driven machine intelligence and its applications using case study highlights spanning a range of domains from workplace health and wellbeing to those related to depression, suicide, and neurocognitive health across life span including Autism and Dementia.

Biography of the Speaker:

Shrikanth (Shri) Narayanan is University Professor and Niki & C. L. Max Nikias Chair in Engineering at the University of Southern California (USC), where he is VP for Presidential Initiatives, Professor of Electrical & Computer Engineering, Computer Science, Linguistics, Psychology, Neuroscience, Pediatrics, and Otolaryngology—Head & Neck Surgery, Director of the Ming Hsieh Institute and Research Director of the Information Sciences Institute. Prior to USC, he was with AT&T Bell Labs and AT&T Research. He is a Visiting Faculty Researcher with Google DeepMind.  His interdisciplinary research focuses on human-centered sensing/imaging, signal processing, and machine intelligence centered on human communication, interaction, emotions, and behavior.  He is a Fellow of the Acoustical Society of America, IEEE, ACM, International Speech Communication Association (ISCA), the American Association for the Advancement of Science, the Association for Psychological Science, the Association for the Advancement of Affective Computing, the American Institute for Medical and Biological Engineering (AIMBE), and the National Academy of Inventors. He is a recipient of awards for research and education including the 2025 IEEE James L. Flanagan Speech and Audio Processing Award, 2024 Edward J. McCluskey Technical Achievement Award from the IEEE Computer Society, the 2023 Claude Shannon-Harry Nyquist Technical Achievement Award from the IEEE Signal Processing Society, 2023 ISCA Medal for Scientific Achievement from the International Speech Communication Association, the 2023 Richard Deswarte Prize in Digital History and a 2022 Guggenheim Fellowship. He has published widely and his inventions have led to technology commercialization including through startups he co-founded: Behavioral Signals Technologies focused on AI based conversational assistance and Lyssn focused on mental health care and quality assurance.

Michelle Khine, Ph.D. is a Professor of Biomedical Engineering and Associate Dean of Undergraduate Education at UC Irvine. She is the founding Director of Faculty Innovation at the Samueli School of Engineering and founding Director of BioENGINE (BioEngineering Innovation and Entrepreneurship) at UC Irvine. Prior to joining UC Irvine, she was an Assistant & Founding Professor at UC Merced. Michelle received her BS and MS from UC Berkeley in Mechanical Engineering and her PhD in Bioengineering from UC Berkeley and UCSF. She is the Scientific Founder of 6 start-up companies. Michelle was the recipient of the TR35 Award and named one of Forbes ’10 Revolutionaries’,  by Fast Company Magazine as one of the ‘100 Most Creative People in Business’, and by Marie‐Claire magazine as ‘Women on Top: Top Scientist’. She was awarded the ‘Entrepreneurial Leader of the Year’ in 2025 at UC Irvine. Michelle is a Fellow of AIMBE (American Institute of Medical and Biological Engineering) and a Fellow of the National Academy of Inventors. 

Abstract:

We know that physiological signals precede clinical deterioration. Yet our antiquated healthcare system is still rooted in episodic and reactionary-based care, in which patients are expected to travel to a centralized location for a healthcare provider to provide a snapshot-in-time assessment when they are overtly ill. Unless the symptoms are apparent at the time of examination, the subjective evaluation relies heavily on the patient’s self-reporting of symptoms. This often results in delayed, inconclusive, or improper diagnoses. In response, we have developed a suite of soft, low-cost, unobtrusive, Band-Aid © like physiological sensors to continuously monitor patients’ cardiovascular and pulmonary functions. We seek to continuously quantify subtle physiological changes to predict – and eventually prevent — the onset of acute clinical events.  

Dr. Eric Hekler, PhD, is Professor in the Herbert Wertheim School of Public Health and Human Longevity Science and the Design Lab in the University of California, San Diego (UCSD). Eric is a transdisciplinary researcher, educator, and practitioner who works at the intersection of health psychology, design, systems science, and public health. His mission is to advance methods and processes that equitably serve people and practice towards a more vital, just, and resonantly diverse society and planet. He has numerous publications that span the many disciplines he contributes, has active federal and foundation funding – including as an NIH R01-funded PI, has played an integral role in creating new transdisciplinary educational opportunities and programs at UCSD, and is an active public health practitioner in the San Diego region. He is recognized internationally as an expert in applied health science methods and digital health.

Abstract

Digital health interventions for behavioral change face a fundamental challenge: traditional research methods assume population-level effects remain stable across individuals and time, while human behavior is inherently dynamic, context-dependent, and individually variable. This keynote traces a 15-year research journey that began with recognizing this methods-problem mismatch and culminated in successfully deploying adaptive, controller-based interventions in real-world settings.