BigCHat 2014 - 2014 Workshop on Connected Health at Big Data Era

The availability of big data and the emergence of network science as an area of inquiry has been changing the landscape how we decipher our lives, our social interactions, and our day-to-day activities. This well-connected world has proposed novel requirements on transforming healthcare from reactive and hospital-centered, to preventive, proactive, evidence-based, person-centered and focused on well-being rather than ailment recovery. Various types of data are involved in this broader context of healthcare:
Clinical data, mainly the patient records from clinical institutions, such as medical imaging, patient electronic health records, clinical trial data, etc.
Genotype data, basically the genetic makeups of the individuals, such as DNA and protein.
Social media data, which is the information the individuals posted on online social platforms such as Facebook, Twitter, PatientsLikeMe, etc
Environmental sensory data, which are the information sampled from the surrounding environment where the individuals are living in, such as air pollution and humidity information
Behavioral and sentiment data, which could be the data recorded by the wearable devices on patient’s activities
Mobile data, which are sampled from individuals’ mobile devices
Integrating all these different kinds of information to make people healthier bears huge potential, but is also is a problem of vital importance and requires a lot of efforts from different parties where data miners play a major role. This makes this workshop highly relevant to KDD, the premier conference of data mining. Moreover, last year the National Science Foundation of United States set up a novel program on smart and connected health, which makes this workshop an in-time event for people to share their opinions and experiences on this topic.
Topics of Interests
The topics of this workshop include, but not limit to, the following:
Integration and matching of different data sources
Quality assessment and improvement of different data
Disease modeling and early intervention
Data-drive methods for personalized medicine
Care coordination and pathway analysis
Behavioral modeling and sentiment analysis
Mobile health
Social media and public health
Comprehensive risk prediction
Community based elder care
Large scale and longitudinal analysis of multi-faceted information
Visual analytics and interactive computation
Program Committee Chairs
Fei Wang. Research Staff Member. IBM T. J. Watson Research Center. fwang-AT-us.ibm.com
Hanghang Tong. Assistant Professor. Department of Computer Science. City College. City University of New York. tong-AT-cs.ccny.cuny.edu
Munmun De Choudhury. Assistant Professor. School of Interactive Computing. Georgia Institute of Technology. mchoudhu-AT-cc.gatech.edu
Zoran Obradovic. Laura H. Carnell Professor. Computer and Information Sciences Department, Temple University. zoran.obradovic-AT-temple.edu
Publicity Chair
Xiang Wang. Research Scientist. IBM T. J. Watson Research Center. wangxi-AT-us.ibm.com
Invited Speakers
Shahram Ebadollahi photo
Shahram Ebadollahi: Dr. Shahram Ebadollahi is the Director of Health Informatics at IBM Research. In this capacity he is responsible for defining and driving the research agenda in the broad area of Healthcare Informatics for IBM Research. He and his colleagues have pioneered technologies in the area of data-driven healthcare, which is the applications of data mining, machine learning, and advanced visual analytics to large patient population data for deriving insights and evidence for decision support in healthcare. His work in this area has led to software and services offerings by IBM (see: IBM Patient Care and Insights ).
GQ Zhang: Dr. Zhang is Division Chief of Medical Informatics, Co-Director of Biomedical Research Information Management of the NCATS-funded Case Western CTSA, and PI of the NHLBI-funded National Sleep Research Resource Center. His research spans large-scale, multi-center data integration, ontological engineering, query interface design and information retrieval. Dr. Zhang has led a group of faculty, student and developers which has deployed a half dozen tools for data capturing, data management and data integration, effectively bringing cutting-edge computer science and informatics methodology to address data challenges.
Joydeep Ghosh: Dr. Ghosh is currently the Schlumberger Centennial Chair Professor of Electrical and Computer Engineering at the University of Texas, Austin. He joined the UT-Austin faculty in 1988 after being educated at, (B. Tech '83) and The University of Southern California (Ph.D’88). He is the founder-director of IDEAL (Intelligent Data Exploration and Analysis Lab) and a Fellow of the IEEE. Dr. Ghosh has taught graduate courses on data mining and web analytics every year to both UT students and to industry, for over a decade. He was voted as "Best Professor" in the Software Engineering Executive Education Program at UT. Dr. Ghosh's research interests lie primarily in data mining and web mining, predictive modeling / predictive analytics, machine learning approaches such as adaptive multi-learner systems, and their applications to a wide variety of complex real-world problems. He has published more than 300 refereed papers and 50 book chapters, and co-edited over 20 books. He has received 14 Best Paper Awards over the years. Dr. Ghosh hasalso served as a co-founder, consultant or advisor to successful startups (Stadia Marketing, Neonyoyo and Knowledge Discovery One) and as a consultant to large corporations such as IBM, Motorola and Vinson & Elkins.
Henry Kautz: Dr. Kautz is Chair of the Department of Computer Science and Director of the Institute for Data Science at the University of Rochester. He performs research in social media, machine learning, pervasive computing, search algorithms, and assistive technology. His academic degrees are 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 joined University of Rochester in 2006. He was President (2010-2012) of the Association for the Advancement of Artificial Intelligence (AAAI), and is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), a Fellow of the American Association for the Advancement of Science (AAAS), a Fellow of the Association for Computing Machinery (ACM), and a recipient of the IJCAI Computers and Thought Award.