ABSTRACT:

Science faces complex integrative problems of increasing societal importance that are orders of magnitude more challenging every decade.  Computing has had a prime role in handling that complexity, but has focused mostly on managing large calculations over data.  There are many unexplored opportunities for Artificial Intelligence (AI) to break new barriers as assistants in research tasks that involve handling complex information spaces and searching systematically for plausible hypotheses.  Unlike AI innovations that have been very successful in the commercial arena, these AI research assistants for science have a crucial requirement to capture all forms of scientific knowledge in order to accept guidance from humans and to place new findings in the context of what is known.  In this talk, I will describe our ongoing research on intelligent workflow systems that capture scientific knowledge about data and analytic processes to assist scientists in analyzing data systematically and efficiently while providing customized explanations of their findings.  I will also describe a new research project to develop an AI research assistant capable of hypothesis-driven discovery by capturing experimental design strategies that determine what data and analysis methods are relevant for a given hypothesis.  I will discuss how AI research motivated by science challenges will significantly augment our ability to tackle fundamental problems in big data analytics that have been a barrier for progress in many areas.

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, with a focus on artificial intelligence.  Her research is on intelligent interfaces for knowledge capture, which she investigates in a variety of projects concerning knowledge-based planning and problem solving, information analysis and assessment of trust, semantic annotation and metadata, and community-wide development of knowledge bases.  In recent years, Dr. Gil has collaborated with scientists in different domains on semantic workflows, metadata capture, social knowledge collection, and computer-mediated collaboration.  She is a Fellow of the Association for Computing Machinery (ACM), and Past Chair of its Special Interest Group in Artificial Intelligence (SIGAI).  She is also Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), and was elected as its 24th President in 2016.