Invited Speakers

Title: Computational Approaches for Assessing Emotional Alignment in Mental Health Research

Abstract: In my talk, I will discuss the importance of emotions in psychological therapies, particularly in emotional disorders like depression. In addition to the patient’s emotions, the interplay of the patient’s and the therapist’s emotions needs to be considered in the therapeutic process and outcome. Recent developments in machine learning offer new opportunities to efficiently assess emotional alignment in mental health research. The assessment is based on facial expression analysis, posture and gesture recognition, emotional speech analysis and multimodal combinations therefore. In my talk, I will discuss the ability of multimodal machine learning approaches to predict emotional states, symptoms of the psychological disorder, and processes of change in dyadic interactions, emotional coping, and experience. In addition to analyzing patient-therapist dyads, I will report on the dynamics of interaction patterns in conversations with mental health coaches on the patient’s mobile phone. Finally, I will share lessons learned when conducting interdisciplinary projects on mental health research.

Bio: Elisabeth André is a full professor of Computer Science and Founding Chair of Human-Centered Artificial Intelligence at Augsburg University in Germany. Elisabeth André has a long track record in multimodal human-machine interaction, embodied conversational agents, social robotics, affective computing and social signal processing. Her work has won many awards including the Gottfried Wilhelm Leibnitz Prize 2021 of the German Research Foundation (DFG), with 2.5 Mio € the highest endowed German research award. In 2017, she was elected to the CHI Academy, an honorary group of leaders in the field of Human-Computer Interaction. To honor her achievements in bringing Artificial Intelligence techniques to Human-Computer Interaction, she was awarded a EurAI fellowship (European Coordinating Committee for Artificial Intelligence) in 2013. In 2019, she was named one of the 10 most influential figures in the history of AI in Germany by National Society for Informatics (GI). Elisabeth André is a member of the Bavarian Academy of Sciences and Humanities, the Academy of Europe, the Asia-Pacific Artificial Intelligence Association and the German Academy of Sciences Leopoldina.

Title: Human Pose and Gestures as Digital Biomarkers for Cognitive Aging and Neurodegenerative Diseases

Abstract: Digital biomarkers are data that can be directly collected about health or disease management through digital health technologies to explain, influence, and/or predict health-related outcomes. Recent developments in AI and computer vision present an exceptional opportunity for automating digital biomarker discovery and development. In this talk, I will go over these concepts and present some of our recent work on computer vision-based biomarker discovery.

Bio: Dr. Ehsan Adeli is an Assistant Professor at the Department of Psychiatry and Behavioral Sciences and is affiliated with the Department of Computer Science. He primarily leads research at the Computational Neuroscience (CNS) Lab and the Partnership in AI-Assisted Care (PAC). PAC is a partnership between Stanford AI Lab (SAIL),Stanford Vision and Learning (SVL), and Clinical Excellence Research Center (CERC). His research interests include computer vision, ambient intelligence, computational neuroscience, medical image analysis, and AI-assisted healthcare. Dr. Adeli is an Executive Co-Director of Stanford AGILE Consortium (Advancing technoloGy for fraIlty & LongEvity), and a faculty member of Stanford Wu Tsai Neurosciences Institute, Stanford Institute for Human-Centered AI, and Stanford Center for AI in Medical Imaging. He is an Associate Editor of the IEEE Journal of Biomedical and Health Informatics and the Journal of Ambient Intelligence and Smart Environments. He is a Senior Member of IEEE and has recently served as area chair or associate editor for several top conferences.

Title: Seeing Beneath the Skin with Computational Photography

Abstract: For more than two decades, telehealth held the promise of greater access to healthcare services in the home but remained as a niche opportunity due to a combination of regulatory, economic, and cultural barriers that prevented the expansion and innovation of digital health services. The SARS-CoV-2 pandemic promoted a rapid and explosive growth of telehealth that provided an effective mechanism for safely providing care without risk of exposure. Remote patient monitoring is now gaining adoption and new physiological and medical imaging modalities that leverage recent advances in computational photography are emerging. These methods use everyday sensors to non-invasively inspect and measure the internal state of the body. I will present research on physiological and behavioral measurement via ubiquitous hardware, and highlight approaches that capture multimodal signals (e.g., facial and motor movements, heart rate and HRV, respiration, blood pressure) without contact with the body. I'll show examples of state-of-the-art, on-device neural models and a synthetics data pipeline to help us learn more robust representations and achieve performance close to that of contact sensors. Following this, I will give examples of novel human-computer interfaces that leverage these signals to improve health, wellbeing and communication.

Bio: Daniel McDuff is a Staff Research Scientist at Google and Affiliate Professor at the University of Washington. Daniel completed his PhD at the MIT Media Lab in 2014 and has a B.A. and Masters from Cambridge University. Previously, Daniel worked at the UK MoD, was Director of Research at MIT Media Lab spin-out Affectiva. His work has received nominations and awards from Popular Science magazine as one of the top inventions in 2011, South-by-South-West Interactive (SXSWi), The Webby Awards, ESOMAR and the Center for Integrated Medicine and Innovative Technology (CIMIT). His projects have been reported in many publications including The Times, the New York Times, The Wall Street Journal, BBC News, New Scientist, Scientific American and Forbes magazine. Daniel was named a 2015 WIRED Innovation Fellow, an ACM Future of Computing Academy member and has spoken at TEDx and SXSW. Daniel has published over 150 peer-reviewed papers on machine learning (NeurIPS, ICLR, ICCV, ECCV, ACM TOG), human-computer interaction (CHI, CSCW, IUI) and biomedical engineering (TBME, EMBC).