Research

My academic research falls into three main areas: 1) understanding nature-inspired AI agents and their evoluation/coordination in dealing with optimisation tasks, and developing new algorithms/tools to improve decision-making in complex systems via AI agents; 2) digital twin linking between computational simulations and physical systems; and 3) Computational biology with systems approaches to understand cell behaviors and design highly productive biosystems.

One strand of my research uses evolutionary computation (nature-inspired, subfield of AI) to solve dynamic multi-objective optimization problems, for which multiple optimisation objectives/constraints may vary over time.

In a new avenue of research, I leverage omics data to explore data-driven methods in conjunction with genome-scale metabolic models to eludicate microbial cell metabolism, gaining insights into plastic metabolic phenotypes.

Computational Biology

In silico ration design of metabolic networks for biochemical overproduction