Abstract: The modern world is powered by data. However, as the capabilities of data-intensive systems grow, so does their complexity, making them hard to understand and troubleshoot. I will discuss my lab's efforts towards understanding models and the data they learn from, including local and global model explanations as well as model diagnostics for fairness and bias avoidance.

Bio: Lukasz Golab is a Professor and Canada Research Chair at the University of Waterloo. From 2006 to 2011, he was a Senior Member of Research Staff at AT&T Labs. He obtained a BSc in Computer Science from the University of Toronto (with High Distinction) and a PhD in Computer Science from the University of Waterloo (with Alumni Gold Medal). His long-term research agenda of Data for Good calls for building data-intensive systems with societal impact. His recent projects focus on systems for managing high-speed data events such as data stream engines and blockchains, understanding complex models and the data they learn from, and applications including online safety, education, and sustainability.