Dr Hongyu Zhang (张宏宇）

I am a Lecturer (tenured Assistant Professor) in optimisation for machine learning and AI at University of Southampton. I work in the operational research group in the School of Mathematical Sciences.

I hold a PhD in operational research. I am interested in the development of optimisation models, solution algorithms and machine learning and AI tools aimed at addressing the complexities of large-scale energy system planning problems.

My passion is to advance the field of energy system modelling, contributing to the energy transition by providing valuable insights that inform energy and climate policies and facilitate decision-making processes.

Call for papers

Special issue "Interfaces between AI and decision-making under uncertainty" in Computational Management Science. Submission deadline: 15 December 2026.  Co-editing with Dr Alan King, Professor Enza Messina, and Dr Vincent Leclère.

Call for abstracts

If you are interested in presenting in the stream "AI and quantum computing for stochastic optimisation" in the ECSO-CMS 2027 conference, please email me. The conferce will take place in Lancaster 20-23 June 2027.

PhD opportunities

I am looking for PhD students with a strong background in optimisation, machine learning and AI, and quantum computing and an ambition to develop novel methodologies and tools to aid the energy transition. 

I have the following PhD positions that welcome applications. If you are interested, please contact me via email with a CV included. 

1. Explainable stochastic decision intelligence for future energy market design, funded by the SustAI CDT. (Application oepn soon)

2. Techno-economic modelling of sustainable data centre integration in the European energy system, funded by the SustAI CDT. (Application oepn soon)

3. Machine learning-enhanced stochastic programming for long-term energy storage management under extreme weather and climate risk, funded by the MFC CDT.  (Deadlines: first round 8 November 2026, second round 10 January 2027, final round 7 March 2027)

4. Hybrid quantum–classical stochastic optimisation for future energy systems under uncertainty, funded by the Quantum technology CDT. (Application oepn soon)

5. Machine learning and stochastic optimisation for maritime anomaly behaviour identification, funded by the CISDnS. (Application oepn soon, British students only)

Please check the information sheet for more details about these positions.