In this talk I will present our work on machine learning and data mining with semantics to enable better personalization for health empowerment. Our current research aims at empowering people to make sense of and derive value from the vast amounts of personal fitness as well as relational and textual data. In particular, our focus is on personalized search and recommendation that uses semantics-based machine learning to address the data-to-knowledge gap. The challenge lies in the coupling of curated knowledge and individual data to provide users with information customized for their specific situation and health needs. Ultimately, this vision will be realized via a continuous learning system that combines machine learning with NLP and dialog systems.
Mohammed J. Zaki is a Professor of Computer Science at RPI. He received his Ph.D. degree in computer science from the University of Rochester in 1998. His research interests focus on developing novel data mining and machine learning techniques, especially for applications in text mining, social networks and bioinformatics. He has over 250 publications, including the Data Mining and Analysis textbook published by Cambridge University Press, 2014. He is the founding co-chair for the BIOKDD series of workshops. He is currently an associate editor for Data Mining and Knowledge Discovery, and he has also served as Area Editor for Statistical Analysis and Data Mining, and an Associate Editor for ACM Transactions on Knowledge Discovery from Data, and Social Networks and Mining. He was the program co-chair for SDM'08, SIGKDD'09, PAKDD'10, BIBM'11, CIKM'12, ICDM'12, BigData'15, and CIKM'18. He is currently serving on the Board of Directors for ACM SIGKDD. He received the National Science Foundation CAREER Award in 2001 and the Department of Energy Early Career Principal Investigator Award in 2002. He received an HP Innovation Research Award in 2010, 2011, and 2012, and a Google Faculty Research Award in 2011. He is an ACM Distinguished Scientist and a Fellow of the IEEE. His research is supported in part by NSF, NIH, DOE, IBM, Google, HP, and Nvidia.