Rate My Professor Buser Say

Dr. Buser Say is a Senior Lecturer in the Department of Data Science & AI within the Faculty of Information Technology at Monash University. He serves as the Director of Student Experience in the Faculty of Information Technology (FIT) and as Director of Student Engagement Optimisation in the department. Say is also an associate investigator for the ARC Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA). His academic background includes a PhD awarded in 2020 from the University of Toronto for his dissertation titled 'Optimal Planning with Learned Neural Network Transition Models,' a Master of Applied Science (MASc) in 2016 on 'Mixed-Integer Linear Programming Models for Least-Commitment Partial-Order Planning,' and a Bachelor of Applied Science (BASc) in Industrial Engineering in 2014, all obtained from the University of Toronto. Before joining Monash, Say was the AI and Machine Learning theme coordinator for the Building 4.0 Cooperative Research Centre (CRC), a Postgraduate Affiliate with the Vector Institute for Artificial Intelligence, and a visiting researcher at the Australian National University.

Say's research specializes in the intersection of sequential decision making, mathematical optimisation, and machine learning, with key areas including automated planning, operations research, and neural networks. He supervises honours, masters, and PhD projects on topics such as continuous-time automated decision making with mathematical optimisation and probabilistic active goal recognition, and is currently accepting PhD students. His recent publications include 'A Metric Hybrid Planning Approach to Solving Pandemic Planning Problems with Simple SIR Models' (2026, with Ari Gestetner), 'A Bayesian Optimisation with Segmentation Approach to Optimising Liquid Handling Parameters' (2025, Journal of Process Control), 'Human-in-the-Loop AI for HVAC Management: Enhancing Comfort and Energy Efficiency' (2025, ACM e-Energy), 'Probabilistic Active Goal Recognition' (2025, Principles of Knowledge Representation and Reasoning), and 'Rapid Identification of Protein Formulations with Bayesian Optimisation' (2023, ICMLA). Through these contributions, Say advances applications in optimisation and AI planning.