Dr. Siyang Ma

Dr. Siyang Ma is a Computational Modelling Researcher and a Research Associate at the Research Centre for Carbon Solutions (RCCS), Heriot-Watt University, where he is currently contributing to the AI4Capture project. He specializes in the design and implementation of mechanistic and hybrid (Physics-ML) models for complex dynamic systems. His expertise lies in integrating first-principles with data-driven surrogates to accelerate multi-scale simulations, ensuring both physical accuracy and computational efficiency.

He earned his Ph.D. in Chemical Engineering from the University of Manchester in 2025. His doctoral research focused on sustainable process design, specifically the optimal design of Pressure Swing Adsorption (PSA) processes for carbon capture. During his studies, he developed rigorous, first-principles dynamic models that accurately capture complex kinetics and mass transfer phenomena. To address the severe computational bottlenecks typically found in multiscale simulations, Dr. Ma engineered a robust hybrid optimization framework. By bridging mechanistic models with data-driven surrogates—such as Artificial Neural Networks and Gaussian Processes—he successfully reduced simulation computational costs by 20-fold. This significant acceleration enables efficient operational decision-making under strict constraints, a factor critical for real-time process control.

Prior to his doctoral work, he completed his M.Sc. in Chemical Engineering at Tianjin University in 2020. His master’s research integrated machine learning with industrial crystallization, where he developed high-precision artificial neural network models to predict metastable zone widths and optimize reactive crystallization processes.

Research Associate

Sorbent-based Carbon Capture and PSA Processes, hybrid Modelling, multi-objective Process Optimisation, black-box optimisation.