Jianying Hu, Ph.D – USA
Dr. Jianying Hu is IBM Fellow and Global Science Leader, AI for Healthcare at IBM Research. In this role, Dr. Hu is responsible for working across IBM Research to define and drive an AI science leadership strategy for healthcare at IBM. She also serves on the Computational Science Advisory Board Member for The Michael J. Fox Foundation. Dr. Hu leads the Center for Computational Health at IBM Research, located in New York and Cambridge. The center consists of a multidisciplinary team of over 30 Ph.D. and MD researchers working on applying data science to Healthcare. It conducts scientific research and technical innovations in the broad areas of data driven healthcare analytics, with focuses on predictive analytics, disease modeling, real world evidence generation, translational informatics, connected health and computational health behavior. In early 2019 - IBM Research Healthcare and Life Sciences announced a partnership with the Michael J. Fox Foundation, which included a grant for an undisclosed amount from the New York City-based foundation, as well as access to data the Foundation has collected for years. The key to progress lies in analyzing this data, known as the Parkinson’s Progression Marker Initiative (PPMI). That capability dovetails with work IBM has been doing in the area of neurodegenerative diseases. Dr. Hu has published over 120 peer reviewed scientific papers and holds 32 patents. She chaired the American Medical Informatics Association (AMIA) Knowledge Discovery and Data Mining Working Group from 2014 to 2016. Dr. Hu is also Visiting Chair Professor of the Taipei Medical University & on the Advisory Board of the Journal of Healthcare Informatics Research. She is a fellow of IEEE (elected in 2015), a fellow of the International Association of Pattern Recognition (elected in 2010), and a recipient of the Asian American Engineer of the Year Award (2013). Prior to joining IBM in 2003 she was with Bell Labs at Murray Hill, New Jersey. Dr. Hu has conducted and led extensive research in machine learning, data mining, statistical pattern recognition, and signal processing, with applications to healthcare analytics and medical informatics, business analytics, and multimedia content analysis. Her recent focus has been on leading research efforts to develop advanced computational methods for deriving datadriven insights from real world healthcare data to facilitate learning health systems. Current Research Interests Healthcare Analytics and Biomedical Informatic Patient Similarity Analysis, Predictive Modeling, Temporal Modeling, Disease Modeling, Care Pathway Analtyics, Population Risk and Utilization Analysis, Drug Indication Expansion and Safety.