Knowledge-Based Planning: Prediction, Optimization, and Open-Access Advances

The Joint CARTE (University of Toronto) and University of Seoul Applied AI seminar series welcomes Professor Timothy Chan.

Registration: Please register through here.

Abstract: In this talk, I will present recent work our group has done in knowledge-based planning, including the development of dose prediction and optimization models. I will also discuss the organization and results of the Open Knowledge-Based Planning Challenge (OpenKBP), an AAPM-sponsored international competition to compare dose prediction models on a large open-access dataset of head-and-neck cancer patients. Finally, I will present follow-up work from an international collaboration that extends OpenKBP to include plan optimization.

Bio: Timothy Chan is the Associate Vice-President and Vice-Provost, Strategic Initiatives of the University of Toronto, the Canada Research Chair in Novel Optimization and Analytics in Health, a Professor in the department of Mechanical and Industrial Engineering, and a Senior Fellow of Massey College. His primary research interests are in operations research, optimization, and applied machine learning, with applications in healthcare, medicine, sustainability, and sports. He received his B.Sc. in Applied Mathematics from the University of British Columbia (2002), and his Ph.D. in Operations Research from the Massachusetts Institute of Technology (2007). Before coming to Toronto, he was an Associate in the Chicago office of McKinsey and Company (2007-2009), a global management consulting firm. During that time, he advised leading companies in the fields of medical device technology, travel and hospitality, telecommunications, and energy on issues of strategy, organization, technology and operations.