Keynote Speakers

There will be two keynote speeches, as well a short tutorial.

 
keynote Talk Title: "In Sickness and in Health: Predicting Disease from Social Network Data"
Speaker: Henry Kautz

Bio:
Henry Kautz is Chair of the Department of Computer Science at the University of Rochester. He performs research in knowledge representation, satisfiability testing, pervasive computing, and assistive technology. His academic degrees include an A.B. in mathematics from Cornell University, an M.A. in Creative Writing from the Johns Hopkins University, an M.Sc. in Computer Science from the University of Toronto, and a Ph.D. in computer science from the University of Rochester. He was a researcher and department head at Bell Labs and AT&T Laboratories until becoming a Professor in the Department of Computer Science and Engineering of the University of Washington in 2000. He left Seattle in 2006. He is President (2010-2012) of the Association for the Advancement of Artificial Intelligence, Fellow of the AAAI, a Fellow of the American Association for the Advancement of Science, and a recipient of the IJCAI Computers and Thought Award.

Talk Abstract: 
The notion of ubiquitous sensor networks makes one think of distributed hardware devices transmitting data to services in well-defined formats.  However, sensor networks can also be built from ordinary people communicating informally through social media.  We show how "people as sensors" can be applied to the task of tracking and predicting disease transmission in large-scale populations.  This is joint work with Adam Sadilek.



Keynote Talk Title: "Using Smartphones to Sense, Assess, and Improve Well-Being "
Speaker: Tanzeem Choudhury

Bio:
 Tanzeem Choudhury is an associate professor in Information Science at Cornell University. She received her PhD from the Media Laboratory at MIT. Tanzeem directs the People-Aware Computing group that develops systems that can reason about human activities, interactions, and social networks in everyday environments. MIT Technology Review recognized her as one of the top 35 innovators under the age of 35 (2008 TR35). Tanzeem has also been selected as a TED Fellow (2009), PopTech Science and Public Leadership Fellow (2010), and is a recipient of the NSF CAREER award.  For more information, please visit: http://www.cs.cornell.edu/~tanzeem  

Talk Abstract: We have been developing techniques to cheaply, accurately, and continuously collect behavioral and contextual data that is subsequently leveraged to provide targeted, personalized and effective feedback to promote better mental and physical health in individuals. In this talk I will give an overview of our work on turning sensor-enabled mobile phones into well-being monitors and instruments for administering real-time/real-place intervention strategies. 



Tutorial Title: "Activity Recognition in Real-World Health Scenarios -- A Practitioner's Tutorial"
Speaker: Thomas Pleotz
Bio:
 Thomas is a Lecturer in "Context Aware Computing" at Newcastle University, UK. His research is centered on "Computational Behaviour Analysis" by building computational models that describe and shall help assessing human behavior.

Tutorial Abstract:
Activity recognition, i.e., the automatic detection, classification, and (to some extent) understanding of human activities based on ubiquitous sensing and sophisticated data analysis techniques, represents probably the most prominent part of context-aware computing systems. Such systems have a multitude of applications in a large variety of
domains. For health-related tasks they promise non-intrusive and robust monitoring and analysis of human behavior in natural settings. That way practitioners have a very powerful
tool at their disposal, which offers a broad range of opportunities for both fine-grained and
longitudinal analysis of the phenomenological phenotypes of certain diseases.

In this one hour tutorial I will give a practice-oriented tour of activity recognition with focus
on real-world health scenarios. The tutorial will orient on the general workflow: from 
sensor selection for effective activity monitoring to robust sensor data analysis techniques.
I will give pointers to relevant literature, links to necessary and useful hard- and 
software, overviews of key algorithms, and exemplary case-studies. Together with hints
for practical deployments in health-related scenarios, this tutorial shall serve as a
primer for practitioners working in the field of sensor-based human behavior analysis.
Comments