Sandra-Zilles

Sandra Zilles
Department of Computer Science
University of Regina

 

Dr. Sandra Zilles works in the area of computational learning theory, where she has made numerous important contributions. She is a Canada Research Chair (Tier 2) at the University of  Regina with significant NSERC and CFI funding. One of her recent papers won the Best Paper award at the German AI conference in 2012.  She is a regular contributor to the top conferences in her field (ALT and COLT) and she has been named a co-chair of the conference steering committee for ALT. She is a rare person who can bridge the gap between theory and practice. She has repeatedly demonstrated an enviable combination of breadth and depth, combining expertise in several areas to illuminate fundamental phenomena. Collaborations with other researchers, established and junior, are a prominent feature of Dr. Zilles’ publication record. Her colleagues come from Japan, Singapore, the United States, Germany, as well as from other Canadian universities. She has many publications in the top journals of her field. For example, she has three publications in Artificial Intelligence, which is often considered to be the leading journal in that area.  Many of her conference publications have been rated highly enough to be amongst the select few invited to appear in an associated journal’s special issue – after being expanded and further reviewed. She also has further important publications currently under review. She is renowned as a communicator par excellence, able to make difficult concepts accessible with clarity and simplicity: one of many reasons why colleagues and students hold her in high regard. Within her excellent publication record, her important research advances include: the surprising and insightful  integration of two seemingly unrelated learning models that had been studied independently from each other for almost 20 years; substantial improvements upon previous research through new models of cooperative teaching and learning; and the significant improvement of a solution for a classical search problem using an intuitively clear and easy to understand idea based on techniques from machine learning. Her research career demonstrates creativity, productivity, independence and leadership ability.