Calvin Tsay
Associate Professor in Computing
Develops optimisation and learning methods for sequential decision making under uncertainty, focusing on mixed-integer programming and optimisation over trained models.
Calvin Tsay's lab at Imperial works on reinforcement learning and probabilistic & causal inference. 23 papers since 2023, mostly Expert Systems with Applications. 8 PhD students. Applications close .
At a glance
- papers, 2023–2026
- 6/ year
- at T1 venues
- 0%
- PhD students
- 8
- MS students
- 0
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 11 papers2025 · T1 0
- 5 papers2024 · T1 0
- 7 papers2023 · T1 0
T1 venuesOtherVenues: Expert Systems with Applications
Research focuspaper titles that use each topic's words
- Reinforcement learning7 papers
- Probabilistic & causal inference7 papers
- optimisation1 paper
- trained1 paper
- sequential decision0 papers
Paper list
From the lab homepage.
202511 papers
- Current Opinion in Green and Sustainable Chemistry, 51:100983Bayesian optimization as a flexible and efficient design framework for sustainable process systems
- Accepted to Mathematical ProgrammingBetween steps: Intermediate relaxations between big-M and convex hull formulations
- International Conference on Machine Learning (ICML), Vancouver, CA, JulyCertification for differentially private prediction in gradient-based training
- Industrial & Engineering Chemistry Research, 64(9):4966-4987Control-informed reinforcement learning for chemical processes
- International Conference on Machine Learning (ICML), Vancouver, CA, JulyEARL-BO: Reinforcement learning for multi-step lookahead, high-dimensional Bayesian optimization
- International Conference on Artificial Intelligence and Statistics (AISTATS), Mai Khao, Thailand, MayGlobal optimization of Gaussian process acquisition functions using a piecewise-linear kernel approximation
- ICLR WorkshopMolecular design using graph Bayesian optimization with shortest-path kernels
- Expert Systems with ApplicationsQuantile neural networks for two-stage stochastic programming
- IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems (DYCOPS), Bratislava, SK, JuneReinforcement learning for economic scheduling
- Transactions on Machine Learning Research (TMLR)System-aware neural ODE processes for few-shot Bayesian optimization
- IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems (DYCOPS), Bratislava, SK, JuneTraining neural ODEs using fully discretized simultaneous optimization
20245 papers
- Computers & Chemical Engineering, 181:108518Constrained continuous-action reinforcement learning for supply chain inventory management
- Computer Aided Chemical Engineering, 53:1639-1644Hierarchical reinforcement learning for plantwide control
- Computers & Chemical Engineering, 185:108660Mixed-integer optimisation of graph neural networks for computer-aided molecular design
- IEEE Conference on Decision and Control (CDC) , Milan, IT, DecScaling mixed-integer programming for verification of neural network controllers using bounds tightening
- International Conference on Neural Information Processing Systems (NeurIPS), Vancouver, BC, DecTransition constrained Bayesian optimization via Markov decision processes
20237 papers
- Computers & Chemical Engineering, 172:108194Combining multi-fidelity modelling and asynchronous batch Bayesian optimization
- Computer Aided Chemical Engineering, 52:1649-1654Distributional constrained reinforcement learning for supply chain optimization
- Lecture Notes in Computer ScienceModel-based feature selection for neural networks: A mixed-integer programming approach
- AIChE JournalOptimal design and demand response operation of green hydrogen: bridging process and power grid models
- International Conference on Neural Information Processing Systems (NeurIPS), New Orleans, LA, DecOptimizing over trained GNNs via symmetry breaking
- NeurIPS WorkshopPractical path-based Bayesian optimization
- Computer Aided Chemical Engineering, 52:1643-1648Safe deployment of reinforcement learning using deterministic optimization over neural networks
Where alumni went
Alumni are not listed on the lab homepage.
Apply to Imperial2027 (Apr/Oct)
Computing PhD
- Round
- Round 2 (1 Dec 2026)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- none
Confirmed on the program page.
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