My research group works on optimisation problems in software engineering, chemical engineering, machine learning, and related fields. See my supervisor Dr Miqing Li’s applied research page for examples.

Industry and Applications

My research focuses on multi-objective optimisation: methods for problems with several conflicting objectives. In vehicle design, for example, we may seek higher performance and lower energy use; in portfolio management, investors pursue returns while controlling risk. Multi-objective algorithms find a set of strong trade-off solutions so that decision-makers can compare alternatives transparently.

Optimising one objective is often inadequate for a modern complex system. Multi-objective optimisation has therefore been applied in AI, machine learning, environmental protection, healthcare, and many other areas. Examples include:

  1. Engineering design: Samuel Daulton and colleagues used multi-objective Bayesian optimisation for optical-display design, balancing display performance with energy consumption.1
  2. Machine learning: Suyun Liu and Luis Nunes Vicente demonstrated a stochastic multi-gradient method for supervised learning, balancing predictive performance and computational complexity.2
  3. Energy and environmental management: Noor A. Rashed and colleagues reviewed how objectives such as cost, emissions, and resource use can be optimised together to support more sustainable decisions.3

Research

Multi-objective problems, problem-characteristic analysis, and targeted algorithm design under limited resources.

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Application Scenarios

Multi-objective optimisation in engineering, design, machine learning, scheduling, and other complex systems.

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Doctorate Association

Academic events, knowledge transfer, and connections among researchers and organisations in China and abroad.

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References

  1. Daulton, S., Balandat, M., & Bakshy, E. (2021). Multi-Objective Bayesian Optimisation over High-Dimensional Search Spaces. Proceedings of the Neural Information Processing Systems. ↩

  2. Liu, S., & Vicente, L. N. (2019). The Stochastic Multi-Gradient Algorithm for Multi-Objective Optimisation and Its Application to Supervised Machine Learning. SIAM Journal on Optimisation. ↩

  3. Rashed, N. A., et al. (2024). Unraveling the Versatility and Impact of Multi-Objective Optimisation: Algorithms, Applications, and Trends for Solving Complex Real-World Problems. Scientific Reports. ↩