Abstract: Humans have evolved to have highly adaptive behaviors that help us survive and thrive. As AI prompts a move from computing interfaces that are explicit and procedural to those that are implicit and intelligent, we are presented with extraordinary opportunities. In this talk, I will argue that understanding affective and behavioral signals presents many opportunities for building more natural user interfaces that extend our capabilities. I will present novel methods for physiological and behavioral measurement via ubiquitous hardware, and highlight approaches that use ordinary ubiquitous sensors (e.g., webcams) to measure physiological and autonomic signals (e.g., peripheral blood flow, heart rate and HRV, respiration, blood pressure) without contact with the body. Following this, I will give examples of novel human-computer interfaces that leverage these signals to improve health, wellbeing and communication. Finally, I will discuss guidelines for assessing and minimizing the risks of emotion recognition applications.

Bio: Daniel McDuff is a Principal Researcher at Microsoft where he leads research and development of affective technology.  Daniel completed his PhD at the MIT Media Lab in 2014 and has a B.A. and Masters from Cambridge University. Daniel’s work on non-contact physiological measurement helped to popularize a new field of low-cost health monitoring using webcams. Previously, Daniel worked at the UK MoD, was Director of Research at MIT Media Lab spin-out Affectiva (acquired by Smart Eye) and a post-doctoral research affiliate at MIT.  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 and an ACM Future of Computing Academy member.  Daniel has published over 100 peer-reviewed papers on machine learning (NeurIPS, ICLR, ICCV, ECCV, ACM TOG), human-computer interaction (CHI, CSCW, IUI) and biomedical engineering (TBME, EMBC).

Homepage:  https://www.microsoft.com/en-us/research/people/damcduff/

Sponsored in part by:   Facebook Reality Labs Pittsburgh