Meet and Eat with Professor Andre Cire, University of Toronto

The University of Toronto Operations Research Group (UTORG) is hosting a Meet & Eat with Professor Andre Cire, of the University of Toronto, Scarborough, following his talk titled “Network-based Approximate Linear Programming”. Lunch and coffee will be served. We hope to see you there!



When: Thursday Oct 18 @ 1:00pm – 2:00 pm

Where: BA 5281

Bio-sketch: Andre Cire is an Assistant Professor at the Department of Management at the University of Toronto Scarborough, cross-appointed with the Operations Management area at Rotman School of Management. His main research interests include discrete optimization, mathematical programming, constraint programming, and practical applications of scheduling and routing. Andre’s recent work focuses on hybrid methods that exploit the interface between operations management and computer science for the purpose of developing computationally efficient methods for hard and large-scale optimization problems.

Meet & Eat with Professor James Luedtke, University of Wisconsin-Madison

The University of Toronto Operations Research Group (UTORG) is hosting a Meet & Eat with Professor James Luedtke, of the University of Wisconsin-Madison, following his talk titled “Optimizing Truck Dispatching Decisions in Open-pit Mining using Integer Programming”. Lunch and coffee will be served. We hope to see you there!


When: Tuesday Sept 25 @ 1:15pm – 2:15 pm

Where: GB 167

Please note that the Meet & Eat will follow immediately after the talk which will be held on Sep 25th, at 12pm-1pm in GB202. For more information regarding the talk, please visit

Bio-sketch: James Luedtke is an Associate Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison. Luedtke earned his Ph.D. at Georgia Tech and did postdoctoral work at the IBM T.J. Watson Research Center. Luedtke’s research is focused on methods for solving stochastic and mixed-integer optimization problems, as well as applications of such models. Luedtke is a recipient of an NSF CAREER award, was a finalist in the INFORMS JFIG Best Paper competition, and was awarded the INFORMS Optimization Society Prize for Young Researchers. Luedtke serves on the editorial boards of the journals SIAM Journal on Optimization, INFORMS Journal on Computing, and Mathematical Programming Computation. Luedtke is the current secretary of the SIAM Activity Group in Optimization, serves on the Committee on Stochastic Programming, and is a former secretary/treasurer of the INFORMS Optimization Society.

Lunch Talk: Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach

The University of Toronto Operations Research Group (UTORG) is hosting a lunch talk by Michael Gimelfarb. The talk is entitled “Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach”.  Lunch and coffee will be provided.  Hope to see you there!

Who: Michael Gimelfarb, Ph.D. candidate, University of Toronto


When: Thursday, September 20th @ 12:00pm – 1:00pm

Where: MB101


Bio-sketch: Michael Gimelfarb is a full-time PhD student in MIE since September 2017, supervised jointly by Professor Chi-Guhn Lee and Professor Scott Sanner. He received his BBA in Finance from the Schulich School of Business in 2014, and his MASc from MIE in 2016, where his thesis focused on the theoretical analysis of the Thompson sampling algorithm applied to queuing control problems. His current research focuses on the application of Bayesian methods and deep learning techniques to reinforcement learning problems. Some of his current and recent work includes reward shaping, decision tree classification using bandits, and automated curriculum learning.

Abstract:  Potential based reward shaping is a powerful technique for accelerating convergence of reinforcement learning algorithms. Typically, such information includes an estimate of the optimal value function and is often provided by a human expert or other sources of domain knowledge. However, this information is often biased or inaccurate and can mislead many reinforcement learning algorithms. In this paper, we apply Bayesian Model Combination with multiple experts in a way which learns to trust the best combination of experts as training progresses. This approach is both computationally efficient and general, and is shown numerically to improve convergence of various reinforcement learning algorithms across many domains.

Meet and Greet with Phebe Vayanos, University of Southern California

The University of Toronto Operations Research Group (UTORG) is hosting a meet and greet with Phebe Vayanos, from the University of Southern California, following her talk titled ‘Data-Driven Integer and Robust Optimization for Scarce Resource Allocation’. Refreshments will be served. We hope to see you there!



When: Thursday Sept 13 @ 1:30pm – 2:00 pm

Where: BA8256


Bio-sketch: Phebe Vayanos is an Assistant Professor of Industrial & Systems Engineering and Computer Science at the University of Southern California, and Associate Director of the CAIS Center for Artificial Intelligence in Society. Prior to joining USC, she was a lecturer in the Operations Research and Statistics Group at the MIT Sloan School of Management and a postdoctoral research associate in the Operations Research Center at MIT. She holds a Ph.D. degree in Operations Research and a MEng degree in Electrical & Electronic Engineering, both from Imperial College London.

Her research aims to address fundamental questions arising in data-driven integer and robust optimization, and game theory. Her work is motivated by decision-making and resource allocation problems that are important for social good, such as those arising in public health, public safety and security, biodiversity preservation, education, and energy. Her aim is to advance research in Operations Research and Artificial Intelligence in a manner that will benefit society and in particular low resource communities and others that have not benefited from these recent developments.


2019 Syngenta Crop Challenge in Analytics

Hi everyone,

Please see below for the information about the 2019 Syngenta Crop Challenge in Analytics:

The population is growing, but our resources are not, leaving us with limited land to produce crops. How will we grow enough to meet the global food demand?

Syngenta and the Analytics Society of INFORMS are asking data analytics and math experts to find innovative ways to tackle this issue in the 2019 Syngenta Crop Challenge in Analytics. Finalists have a chance at cash awards totaling $8,500.

The contest opens this fall and concludes in Jan. 2019.

Learn more and sign up for the challenge here: