About

Tianyi Ma is a quantitative researcher with a PhD in Systems Modelling and Optimisation from University College London. His work focuses on optimisation under uncertainty, infrastructure systems, energy economics, and quantitative decision modelling.

The modelling chain runs from component-level physical representation to system behaviour, then to uncertainty propagation, and finally to lifecycle economics and decision metrics. The intent is that each layer stays traceable to the one beneath it, so that a cost or resilience conclusion can be attributed to the physical and stochastic assumptions that produced it.

Tianyi Ma (formerly Haotian Ma) is based in London.

Research focus

Quantitative systems & uncertainty

Optimisation, Monte Carlo simulation, scenario analysis, sensitivity analysis, stochastic decision models.

AI & data-centre infrastructure

Infrastructure sizing, energy demand, storage, resilience, techno-economics, resource allocation.

Electrochemical & energy systems

PEM electrolysis, degradation, lifecycle economics, hybrid energy storage.

Education

PhD, Systems Modelling and Optimisation

University College London2024–2026Awarded 28 July 2026

Thesis: Multiscale Modelling and Optimisation of the PEM Electrolyser–Battery Hybrid Storage System for Data Centre Reliability. Supervised by Catalina Spataru and Georgios Nikiforidis, Department of Chemistry.

MScR, Systems and Control — Distinction

University of Warwick2021–2023

MSc, Sustainable Energy Engineering — Distinction

University of Nottingham2019–2021

BEng, Energy and Power Engineering

Southeast University2015–2019

Selected experience

Huawei European Research Institute

MunichMay 2023 – Dec 2024

Techno-economic and system modelling of energy infrastructure.

Global Energy Interconnection Research Institute

Berlin / BirminghamOct 2021 – Apr 2023

Modelling and techno-economic assessment of PEM electrolysis systems.

Methods and tools

Optimisation: Mixed-integer and convex programming for sizing, dispatch and capital-allocation problems; constraint modelling; robustness ranking of candidate designs.
Uncertainty: Monte Carlo simulation, structured scenario design, global sensitivity analysis, P10/P50/P90 distributions, tail-risk and stress testing.
Physical modelling: Multiscale PEM electrolyser models with experimental validation and loss decomposition; degradation and lifetime modelling; energy-flow representation across supply, conversion, storage and demand.
Economics: Multi-scenario DCF, levelised cost decomposition, NPV driver attribution, break-even corridor solving, lifecycle and replacement-cycle costing.
Software: Python (NumPy, Pandas, SciPy), C++, SQL, Linux, Git; reproducible research workflows.

Selected publications

Degradation-aware assessment of dominant factors in performance, durability, and cost of proton exchange membrane water electrolysers

T Ma, G Qiao, X Zhang, D Hou, C Spataru, G Nikiforidis, S Du

International Journal of Hydrogen Energy 264, 156855 (2026)

System modelling and sizing optimization of PEM-integrated hybrid energy storage for data centre resilience

H MA, G Nikiforidis, C Spataru

IET Conference Proceedings CP963, 2025 (44), 149–154

Technical and economic performance assessment of blue hydrogen production using new configuration through modelling and simulation

Y Li, J Ren, H Ma, AN Campbell

International Journal of Greenhouse Gas Control 134, 104112 (2024)

All publications →