Pseudo-transient models for multiscale, multiresolution simulation and optimization of intensified reaction/separation/recycle processes: Framework and a dimethyl ether production case study. - Richard C. Pattison; Calvin Tsay; Michael Baldea (Comput. Chem. Eng., 2017)

A superstructure-based design of experiments framework for simultaneous domain-restricted model identification and parameter estimation. - Calvin Tsay; Richard C. Pattison; Michael Baldea; Ben G. Weinstein; Steven J. Hodson; Robert D. Johnson (Comput. Chem. Eng., 2017)

A simulation-based optimization framework for integrating scheduling and model predictive control, and its application to air separation units. - Lisia S. Dias; Richard C. Pattison; Calvin Tsay; Michael Baldea; Marianthi G. Ierapetritou (Comput. Chem. Eng., 2018)

A survey of optimal process design capabilities and practices in the chemical and petrochemical industries. - Calvin Tsay; Richard C. Pattison; Michael R. Piana; Michael Baldea (Comput. Chem. Eng., 2018)

Automating Visual Inspection of Lyophilized Drug Products With Multi-Input Deep Neural Networks. - Calvin Tsay; Zheng Li (CASE, 2019)

Optimal demand response scheduling of an industrial air separation unit using data-driven dynamic models. - Calvin Tsay; Ankur Kumar; Jesus Flores-Cerrillo; Michael Baldea (Comput. Chem. Eng., 2019)

Learning latent variable dynamic models for integrated production scheduling and control. - Calvin Tsay; Michael Baldea (CoRR, 2019)

Identification and online updating of dynamic models for demand response of an industrial air separation unit. - Calvin Tsay; Yanan Cao 003; Yajun Wang; Jesus Flores-Cerrillo; Michael Baldea (CoRR, 2020)

Between Steps: Intermediate Relaxations Between Big-M and Convex Hull Formulations. - Jan Kronqvist; Ruth Misener; Calvin Tsay (CPAIOR, 2021)

Partition-Based Formulations for Mixed-Integer Optimization of Trained ReLU Neural Networks. - Calvin Tsay; Jan Kronqvist; Alexander Thebelt; Ruth Misener (NeurIPS, 2021)

Sobolev trained neural network surrogate models for optimization. - Calvin Tsay (Comput. Chem. Eng., 2021)

Multi-Objective Constrained Optimization for Energy Applications via Tree Ensembles. - Alexander Thebelt; Calvin Tsay; Robert M. Lee; Nathan Sudermann-Merx; David Walz; Tom Tranter; Ruth Misener (CoRR, 2021)

SnAKe: Bayesian Optimization with Pathwise Exploration. - Jose Pablo Folch; Shiqiang Zhang; Robert M. Lee; Behrang Shafei; David Walz; Calvin Tsay; Mark van der Wilk; Ruth Misener (NeurIPS, 2022)

Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces. - Alexander Thebelt; Calvin Tsay; Robert M. Lee; Nathan Sudermann-Merx; David Walz; Behrang Shafei; Ruth Misener (NeurIPS, 2022)

Maximizing information from chemical engineering data sets: Applications to machine learning. - Alexander Thebelt; Johannes Wiebe; Jan Kronqvist; Calvin Tsay; Ruth Misener (CoRR, 2022)

P-split formulations: A class of intermediate formulations between big-M and convex hull for disjunctive constraints. - Jan Kronqvist; Ruth Misener; Calvin Tsay (CoRR, 2022)

OMLT: Optimization & Machine Learning Toolkit. - Francesco Ceccon; Jordan Jalving; Joshua Haddad; Alexander Thebelt; Calvin Tsay; Carl D. Laird; Ruth Misener (J. Mach. Learn. Res., 2022)

Model-Based Feature Selection for Neural Networks: A Mixed-Integer Programming Approach. - Shudian Zhao; Calvin Tsay; Jan Kronqvist (LION, 2023)

Optimizing over trained GNNs via symmetry breaking. - Shiqiang Zhang; Juan S. Campos; Christian Feldmann; David Walz; Frederik Sandfort; Miriam Mathea; Calvin Tsay; Ruth Misener (NeurIPS, 2023)

Distributional constrained reinforcement learning for supply chain optimization. - Jaime Sabal Bermúdez; Antonio del Rio-Chanona; Calvin Tsay (CoRR, 2023)

When Deep Learning Meets Polyhedral Theory: A Survey. - Joey Huchette; Gonzalo Muñoz 001; Thiago Serra; Calvin Tsay (CoRR, 2023)

Practical Path-based Bayesian Optimization. - Jose Pablo Folch; James Odgers; Shiqiang Zhang; Robert M. Lee; Behrang Shafei; David Walz; Calvin Tsay; Mark van der Wilk; Ruth Misener (CoRR, 2023)

Combining multi-fidelity modelling and asynchronous batch Bayesian Optimization. - Jose Pablo Folch; Robert M. Lee; Behrang Shafei; David Walz; Calvin Tsay; Mark van der Wilk; Ruth Misener (Comput. Chem. Eng., 2023)

Scaling Mixed-Integer Programming for Certification of Neural Network Controllers Using Bounds Tightening. - Philip Sosnin; Calvin Tsay (CDC, 2024)

Transition Constrained Bayesian Optimization via Markov Decision Processes. - Jose Pablo Folch; Calvin Tsay; Robert M. Lee; Behrang Shafei; Weronika Ormaniec; Andreas Krause 001; Mark van der Wilk; Ruth Misener; Mojmir Mutny (NeurIPS, 2024)

Mixed-integer optimisation of graph neural networks for computer-aided molecular design. - Tom McDonald; Calvin Tsay; Artur M. Schweidtmann; Neil Yorke-Smith (Comput. Chem. Eng., 2024)

Bayesian optimization as a flexible and efficient design framework for sustainable process systems. - Joel A. Paulson; Calvin Tsay (CoRR, 2024)

System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization. - Jixiang Qing; Becky D. Langdon; Robert M. Lee; Behrang Shafei; Mark van der Wilk; Calvin Tsay; Ruth Misener (CoRR, 2024)

Certified Robustness to Data Poisoning in Gradient-Based Training. - Philip Sosnin; Mark Niklas Müller; Maximilian Baader; Calvin Tsay; Matthew Wicker (CoRR, 2024)

Certificates of Differential Privacy and Unlearning for Gradient-Based Training. - Matthew Wicker; Philip Sosnin; Adrianna Janik; Mark N. Müller; Adrian Weller; Calvin Tsay (CoRR, 2024)

Control-Informed Reinforcement Learning for Chemical Processes. - Maximilian Bloor; Akhil Ahmed; Niki Kotecha; Mehmet Mercangöz; Calvin Tsay; Ehecatl Antonio del Rio-Chanona (CoRR, 2024)

PC-Gym: Benchmark Environments For Process Control Problems. - Maximilian Bloor; José Torraca; Ilya Orson Sandoval; Akhil Ahmed; Martha White; Mehmet Mercangöz; Calvin Tsay; Ehecatl Antonio del Rio-Chanona; Max Mowbray (CoRR, 2024)

EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization. - Mujin Cheon; Jay H. Lee; Dong-Yeun Koh; Calvin Tsay (CoRR, 2024)

Constrained continuous-action reinforcement learning for supply chain inventory management. - Radu Burtea; Calvin Tsay (Comput. Chem. Eng., 2024)

Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation. - Yilin Xie; Shiqiang Zhang; Joel A. Paulson; Calvin Tsay (AISTATS, 2025)

A quantile neural network framework for two-stage stochastic optimization. - Antonio Alcántara; Carlos Ruiz; Calvin Tsay (Expert Syst. Appl., 2025)

System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization. - Jixiang Qing; Rebecca D. Langdon; Robert M. Lee; Behrang Shafei; Mark van der Wilk; Calvin Tsay; Ruth Misener (Trans. Mach. Learn. Res., 2025)

Hierarchical RL-MPC for Demand Response Scheduling. - Maximilian Bloor; Ehecatl Antonio del Rio-Chanona; Calvin Tsay (CoRR, 2025)

Training Neural ODEs Using Fully Discretized Simultaneous Optimization. - Mariia Shapovalova; Calvin Tsay (CoRR, 2025)

Global optimization of graph acquisition functions for neural architecture search. - Yilin Xie; Shiqiang Zhang; Jixiang Qing; Ruth Misener; Calvin Tsay (CoRR, 2025)

The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning. - Toby Boyne; Juan S. Campos; Becky D. Langdon; Jixiang Qing; Yilin Xie; Shiqiang Zhang; Calvin Tsay; Ruth Misener; Daniel W. Davies; Kim E. Jelfs; Sarah Boyall; Thomas M. Dixon; et al. (CoRR, 2025)