Yolanda Gil, PhD

Information Sciences Institute and Department of Computer Science

University of Southern California

ABSTRACT: Artificial Intelligence has had great impact in recent years in the commercial sector, through speech-based personal assistants, product recommender systems, self-driving cars, and knowledge-driven search.  There are many unexplored opportunities for artificial intelligence to make scientific processes more efficient and break new barriers in the complexity of the problems that can be tackled.  In this talk, I will describe our current research on intelligent workflow systems that capture scientific knowledge about data and analytic processes to help scientists create new workflows correctly and efficiently.  I will present a collaborative data analysis framework called “organic data science” that captures collaborative workflows and their associated requirements, data, models, skills, and findings.  I will also describe a new research project to develop intelligent systems capable of hypothesis-driven discovery.  The development of intelligent frameworks for scientific data analysis will enable scientists to use more cost-effective approaches and tackle increasingly more challenging integrative problems.

SPEAKER BIO: Dr. Yolanda Gil is Director of Knowledge Technologies and Associate Division Director at the Information Sciences Institute of the University of Southern California, and Research Professor in the Computer Science Department. She received her M.S. and Ph. D. degrees in Computer Science from Carnegie Mellon University. Dr. Gil leads a group that conducts research on various aspects of Interactive Knowledge Capture. Her research interests include intelligent user interfaces, knowledge-rich problem solving, and the semantic web. An area of recent interest is collaborative scientific data analysis through semantic workflows.  She initiated and chaired the W3C Provenance Group that led to a community standard in this area.  Dr. Gil has served in the Advisory Committee of the Computer Science and Engineering Directorate of the National Science Foundation. She is Chair of ACM SIGAI, the Association for Computing Machinery’s Special Interest Group on Artificial Intelligence.  She was elected Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) in 2012.