Deepak Ganesan, Associate Professor of Computer Science at UMASS Amherst, will speak about computer sensing as a tool for understanding human behavior.
Our ability to continuously monitor activities, health, and lifestyles of individuals using sensors has reached unprecedented levels. On-body sensors enable continuous sensing of our physiological signals, smartphones have a plethora of sensors to monitor activity and location, and a growing number of sensors embedded in the physical world enable monitoring of our living spaces. Such ubiquitous sensing promises to revolutionize our understanding of the social, environmental, and behavioral determinants of a wide range of human activities and health conditions.
Despite its promise, there are fundamental challenges in designing such systems in terms of data processing, sensing, and power. How can we make reliable inferences despite the noisy, uncertain nature of natural environments? How can we expand our understanding of human behavior through more sensors that fully capture our actions, attention, and environmental cues? How can we cope with the burden of having to re-charge a growing ecosystem of wearable sensors?
This talk discusses our ongoing work to address these challenges. From a data perspective, Ganesan will talk about leveraging machine learning techniques to detect use of addictive drugs with wearable ECG sensors, and methods to fuse information across diverse continuous sensor sources. From a sensing perspective, he will address the design of computational eyeglasses, a wearable sensor that continuously tracks eye and visual context. From a power perspective, he will discuss work on RF-powered sensor devices that can sense, process and communicate at orders of magnitude less power than a typical battery-powered sensor.
Deepak Ganesan is currently an Associate Professor in the Department of Computer Science at UMASS Amherst. He received his Ph.D. in Computer Science from UCLA in 2004 and his bachelors in Computer Science from IIT, Madras in 1998. He received the NSF CAREER Award in 2006, the IBM Faculty Award in 2008, and a UMass Lilly Teaching Fellowship in 2009. His publications have received awards at various conferences, most recently, a Best Paper Award at ACM CHI 2013, and an Honorable Mention for Best Paper Award at ACM Ubicomp 2013. He was a Program co-chair for ACM SenSys 2010 and IEEE SECON 2013.
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