Projects per year
I am a Lecturer (i.e., Assistant Professor) in the Department of Data Science and Artificial Intelligence (DSAI) at Monash University, the Artificial Intelligence and Machine Learning theme coordinator for Building 4.0 Cooperative Research Centre (CRC), and an academic supervisor for Monash DeepNeuron. I have earned my Ph.D. (2020) under the supervision of Professor Scott Sanner on the topic of optimal planning with deep neural networks, my MASc. (2016) on the topic of least-commitment partial-order planning under the supervision of Professor J. Christopher Beck and Professor Andre Augusto Cire and my BASc. in Industrial Engineering (2014) from University of Toronto. I was also a Postgraduate Affiliate with the Vector Institute and a visiting researcher at the Australian National University.
Optimal Planning with Learned Neural Network Transition Models, Ph.D., University of Toronto
1 Sep 2016 → 14 Apr 2020
Award Date: 18 Jun 2020
Mixed-Integer Linear Programming Models for Least-Commitment Partial-Order Planning, M.A.Sc., University of Toronto
1 Sep 2014 → 7 Nov 2016
Award Date: 15 Mar 2017
Industrial Engineering, B.A.Sc., University of Toronto
1 Sep 2010 → 18 Jun 2014
Award Date: 18 Jun 2014
Research area keywords
- Automated Planning
- Operations Research
- Neural Networks
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1/04/22 → 31/12/23
Say, B., 2021, Proceedings of the AAAI Conference on Artificial Intelligence, AAAI-21. Leyton-Brown, K. & M. (eds.). 6 ed. Palo Alto CA USA: Association for the Advancement of Artificial Intelligence (AAAI), Vol. 35. p. 5016-5024 9 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-reviewOpen AccessFile
Say, B., Sanner, S., Devriendt, J., Nordström, J. & Stuckey, P., 2021, MaxSAT Evaluation 2021: Solver and Benchmark Descriptions. Bacchus, F., Berg, J., Järvisalo, M. & Martins, R. (eds.). Helsinki Finland: University of Helsinki, p. 32-36 5 p.
Research output: Chapter in Book/Report/Conference proceeding › Chapter (Report) › Research
Compact and efficient encodings for planning in factored state and action spaces with learned Binarized Neural Network transition modelsSay, B. & Sanner, S., Aug 2020, In: Artificial Intelligence. 285, 21 p., 103291.
Research output: Contribution to journal › Article › Research › peer-review
Wu, G., Say, B. & Sanner, S., 20 Jul 2020, In: Journal of Artificial Intelligence Research. 68, p. 571 606 p.
Research output: Contribution to journal › Article › Research › peer-reviewOpen AccessFile
Theoretical and experimental results for planning with learned binarized neural network transition modelsSay, B., Devriendt, J., Nordström, J. & Stuckey, P. J., 2020, Principles and Practice of Constraint Programming : 26th International Conference, CP 2020 Louvain-la-Neuve, Belgium, September 7–11, 2020 Proceedings. Simonis, H. (ed.). Cham Switzerland: Springer, p. 917-934 18 p. (Lecture Notes in Computer Science ; vol. 12333 ).
Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review