Event Location: NSH 1507
Bio: Hayley Hung is an Assistant Professor and Delft Technology Fellow in the Pattern Recognition and Bioinformatics group at TU Delft, The Netherlands, since 2013. Between 2010-2013, she held a Marie Curie Intra-European Fellowship at the Intelligent Systems Lab at the University of Amsterdam. Between 2007-2010, she was a post-doctoral researcher at Idiap Research Institute in Switzerland. She obtained her PhD in Computer Vision from Queen Mary University of London, UK in 2007 and her first degree from Imperial College, UK in Electrical and Electronic Engineering. Her research interests are in social computing, social signal processing, machine learning, and ubiquitous computing.She is local arrangements chair for ACM MM 2016, Workshop co-chair ACM ICMI 2015, area chair of the area on emotional and social signals at ACM MM (2014-2015), co-panel organiser for the panel on Emotional and Signals in Multimedia (ACM MM 2014), Doctoral Symposium co-chair ACM MM (2013). She has organized workshops on human behavior understanding (InterHUB ( AmI 2011), Measuring Behaviour in open spaces (MB 2012), HBU (ACM MM 2013). She is also a special issue guest editor for ACM Transactions on Interactive Intelligent Systems. She has received first prize in the IET Written Premium competition 2009, was nominated for outstanding paper at ICMI 2011, and was named outstanding reviewer at ICME 2014.

Abstract: As mobile and wearable devices become ever more pervasive, their role in shaping, guiding and informing multimedia systems creates huge potential for improving people’s quality of life. In this talk, I will describe my recent work that focuses on how far we can push pervasive sensing systems for the implicit tagging of real life events. Exploring methods of real-world implicit tagging is particularly interesting as it frees up the need for users to make explicit declarations of how they are feeling or what they are doing via social media for example. Specifically, I will describe how by even using a single triaxial accelerometer (commonly available in smart phones), it is possible to estimate whether an audience enjoyed a modern dance performance. I will also describe how body motion information alone can be used to predict socially relevant phenomena such as attraction or who is talking with whom. Finally, I will bring all these components together and speculate on the open challenges for such research.