Projects per year
Personal profile
Biography
I am a Lecturer (i.e., Assistant Professor) in the DSAI at Monash University, the AI and ML theme coordinator for Building 4.0 CRC, an associate investigator for OPTIMA ARC, and an academic supervisor for Monash DeepNeuron. I have earned my Ph.D. (2020) on the topic of optimal planning with deep neural networks, my MASc. (2016) on the topic of least-commitment partial-order planning 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.
Research interests
My research is in the intersection of sequential decision making, mathematical optimisation and machine learning. I am currently offering the following Ph.D. projects:
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Safe Neuro-symbolic Automated Decision Making with Mathematical Optimisation
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Blackbox Multi-Objective Optimization of Unknown Functions
If interested, please apply via the link provided here.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Education/Academic qualification
Optimal Planning with Learned Neural Network Transition Models, Ph.D., University of Toronto
1 Sept 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 Sept 2014 → 7 Nov 2016
Award Date: 15 Mar 2017
Industrial Engineering, B.A.Sc., University of Toronto
1 Sept 2010 → 18 Jun 2014
Award Date: 18 Jun 2014
Research area keywords
- Automated Planning
- Operations Research
- Neural Networks
Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Intelligent Robotics for Pharmaceutical Formulation Development
Kulic, D., Burke, M., Pham, H., Vong, C., Aleti, A. & Say, B.
15/09/24 → 14/09/27
Project: Research
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Human-in-the-loop Microgrid Project: Human-in-the-loop Microgrid Project
Wang, H., de Nijs, F. & Say, B.
15/11/23 → 14/05/27
Project: Research
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HARNESS: Hierarchical Abstractions and Reasoning for Neuro-Symbolic Systems
Rezatofighi, H., Garcia De La Banda Garcia, M., Li, Y., Stuckey, P., Gasevic, D., Gutierrez, J., Qu, L., Ignatiev , A., Swiecki, Z., Vered, M., Chen, G., Haffari, R. & Say, B.
20/06/23 → 20/02/27
Project: Research
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AI Biochemist - Software Infrastructure and Algorithm Development Phase 2
1/04/22 → 31/12/23
Project: Research
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Rapid identification of protein formulations with Bayesian Optimisation
Huynh, V., Say, B., Vogel, P., Cao, L., Webb, G. & Aleti, A., 2023, Proceedings - 2023 International Conference on Machine Learning and Applications, ICMLA 2023. Arif Wani, M., Boicu, M., Sayed-Mouchaweh, M., Henriques Abreu, P. & Gama, J. (eds.). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, p. 776-781 6 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
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Robust metric hybrid planning in stochastic nonlinear domains using mathematical optimization
Say, B., 2023, Proceedings of the International Conference on Automated Planning and Scheduling 2023. Koenig, S., Stern, R. & Vallati, M. (eds.). 1 ed. Palo Alto CA USA: Association for the Advancement of Artificial Intelligence (AAAI), Vol. 33. p. 375-383 9 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Open AccessFile1 Citation (Scopus) -
Training experimentally robust and interpretable binarized regression models using Mixed-Integer Programming
Tule, S., Le, N. L. & Say, B., 2022, Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence (SSCI-2022). Ishibuchi, H., Kwoh, C-K., Tan, A-H., Srinivasan, D., Miao, C., Trivedi, A. & Crockett, K. (eds.). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, p. 838-845 8 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
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A unified framework for planning with learned neural network transition models
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-review
Open AccessFile3 Citations (Scopus) -
Planning with learned binarized neural networks benchmarks for MaxSAT evaluation 2021
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