I am a Ph.D. candidate at University of Toronto under the supervision of Professor Scott Sanner, and a member of the Data-Driven Decision Making Lab (D3M). Previously, I have completed my BASc. in Industrial Engineering from University of Toronto (2014) with emphasis on Operations Research, and earned my MASc. from University of Toronto (2016) as a member of the Toronto Intelligent Decision Engineering Laboratory (TIDEL) on the topic of Mixed-Integer Linear Programming Models for Least-Commitment Partial-Order Planning under the supervision of Professor J. Christopher Beck and Professor Andre Augusto Cire. My main research focus is in the application of Operations Research techniques and Deep Neural Networks to our Data-Driven Automated Hybrid Planning framework.
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains
Ga Wu, Buser Say, Scott Sanner. (accepted) Proceedings of the Thirty-First Annual Conference on Advances in Neural Information Processing Systems (NIPS-17).
Nonlinear Hybrid Planning with Deep Net Learned Transition Models and Mixed-Integer Linear Programming
Buser Say, Ga Wu, Yu Qing Zhou, Scott Sanner. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17), 750-756.
Mathematical Programming Models for Optimizing Partial-Order Plan Flexibility.
Buser Say, Andre Augusto Cire, J. Christopher Beck. Proceedings of the Twenty-Second European Conference on Artificial Intelligence (ECAI-16), 1044-1052.
Deriving Pandemic Disease Mitigation Strategies by Mining Social Contact Networks
Mario Ventresca, Alexandra Szatan, Buser Say, Dionne Aleman. Optimization, Control, and Applications in the Information Age, Springer Proceedings in Mathematics & Statistics. 2015, 359-381.
Deriving Pandemic Public Policies from Contact Networks
Mario Ventresca, Alexandra Szatan, Buser Say, Dionne Aleman. Proceedings of the INFORMS Workshop on Data Mining and Health Informatics (INFORMS DM-HI-13), 2013.
Data-Driven Automated Planning