Keynote Speakers

Always eager to meet new challenges and opportunities, Jian Pei is currently a Canada Research Chair (Tier 1) in Big Data Science, a Professor in the School of Computing Science at Simon Fraser University, and an associate member of the Department of Statistics and Actuarial Science. Recognized as an ACM Fellow and an IEEE Fellow, he is a pioneering researcher in data science, big data, data mining, and database systems. He is also renowned for his active and productive professional leadership. He has over 200 technical publications, which have been cited by 75000+ times, 38000+ in the last 5 years. His research has generated remarkable impact substantially beyond academia, such as his algorithms being adopted by industry in production, by open source software suites in vogue likes Spark MLlib and WEKA, and by classical data mining textbooks. He is the recipient of the ACM SIGKDD 2017 Innovation Award and the IEEE ICDM 2014 Research Contributions Award, the highest awards for technical excellence in data science and data mining, and the ACM SIGKDD 2015 Service Award.  He is the ACM SIGKDD Chair.

How is big data science changing retail industry?

Data and data science are essential to the development of innovative retailers and retail services.  In this talk, I will argue that data science plays a central role in retail industry and applied AI is a most wanted tool in improving retail services.  With real examples I will demonstrate how applied AI techniques can transform stores as well as retail business models.

Amanda Stent is a NLP architect at Bloomberg LP. Previously, she was a director of research and principal research scientist at Yahoo Labs, a principal member of technical staff at AT&T Labs - Research, and an associate professor in the Computer Science Department at Stony Brook University. Her research interests center on natural language processing and its applications, in particular topics related to text analytics, discourse, dialog and natural language generation. She holds a PhD in computer science from the University of Rochester. She is co-editor of the book Natural Language Generation in Interactive Systems (Cambridge University Press), has authored over 90 papers on natural language processing and is co-inventor on over twenty patents and patent applications. She is president emeritus of the ACL/ISCA Special Interest Group on Discourse and Dialog, treasurer of the ACL Special Interest Group on Natural Language Generation and one of the rotating editors of the journal Dialogue & Discourse. She is also a board member of CRA-W, where she co-edits the newsletter.

AI for Unstructured Data in Finance

The finance industry increasingly seeks insight from unstructured data, including text, tables and charts. AI presents opportunities to address these needs by enriching unstructured data and mapping it to structured data sources - and finance offers a wealth of structured data for "grounding" unstructured data. In this talk, I will describe AI problems common to finance, and outline several NLP and ML projects from Bloomberg that show how AI can address finance analytics needs, including: text enrichment; table and chart understanding; and market representation. I will conclude with some challenges to the research community that arise naturally from examining finance data.

Peter van Beek is a Professor in the Cheriton School of Computer Science at the University of Waterloo. He received his PhD in 1990 from the University of Waterloo and at that time joined the faculty at the University of Alberta. Ten years later he returned to Waterloo. His research interests span the field of Artificial Intelligence with a current focus on probabilistic graphical models, constraint programming, and applied machine learning. Peter has co-authored seven research papers which have won awards. From 2005-2009, he served as Editor-in-Chief of the journal Constraints, a forum for research in constraint programming. In 2008, he was named a Fellow of the Association for Artificial Intelligence.

Machine Learning of Bayesian Networks

Bayesian networks are a widely used probabilistic graphical model with diverse applications in knowledge discovery, classification, and decision making. A Bayesian network can either be constructed by a human domain expert or machine learned from data. In this talk, I will review Bayesian networks and their advantages. I will then focus on our recent work on learning the structure of a Bayesian network from discrete data, which has led to improved exact and approximate learning algorithms. Finally, I will briefly present some of our current work on incorporating expert domain knowledge into learning a Bayesian network, and on generating all of the best networks rather than selecting a single network. The talk will be aimed at a general audience.