Researchers Database

Sustainable Supply chain, Decision Making with uncertainty, Mathematical modelling, Stochastic programming, Sustainable healthcare management

He has over 16 years’ experience of research and industrial consultancy and secured over £16M of research funding. His EMOTIVE research holds the world’s best f-measure for fine grained emotion detection and has the ability to identify mental health conditions. He has also spent over 15 years researching workplace email stress.

Methodology: metaheuristics, Bayesian optimisation, machine learning, clustering, explainable AI Problem features: multiple objectives, resourcing issues, dynamic constraints/objective functions/decision spaces, heterogeneous objective functions, safe vs unsafe search regions, applications: manufacturing, healthcare, drug development and delivery, personalized medicine, music, sports, forensics, feature-rich test problem generators.

My research focuses on how to improve individual and collective performance. My current research is at the intersection of issues such as human performance, technology and sustainable operations.

My research focuses on Modelling & Simulation (M&S) methodologies and its application in areas such as healthcare, supply chain management and the circular economy. A particular area of interest is in Hybrid Modelling and Simulation. The objective is to develop the best possible representation of the system under scrutiny by leveraging not only the strengths of individual M&S techniques (e.g., system dynamics, agent-based and discrete-event simulation) but also methods and techniques from wider disciplines such as Operational Research (OR), Applied Computing and Data Science. I am also interested in Bibliometric Analysis of knowledge domains and use methods like co-citation and meta-data analysis for this body of work.

Simulation modelling methodology (symbiotic simulation, hybrid modelling, agent-based simulation, discrete-event simulation, simulation optimisation) with applications in supply chain management, operations management and health and social care.

Sparse Regressions, Bayesian Analysis., Robust Regressions

Exact and heuristic algorithms for integer and mixed-integer optimisation problems and their applications to healthcare and logistics problems.

My work connects behavioural science with analytics: I employ experiments to understand lay and expert decision making under conditions of radical uncertainty; harvest this understanding to build models of how such decisions should be made; and test multiple models using machine learning methodologies. This approach is enjoying a rare success—our models are accurate and at the same time transparent. My group and I have developed tools for challenging problems such as identifying threats at security checkpoints in Afghanistan while minimising civilian casualties, regulating UK investment banks without curbing financial innovation, and predicting the incidence of influenza in the US more robustly than big data. Such psychologically inspired quantitative models help set high standards of transparency and effectiveness for various approaches, including big data algorithms. With the increasing use of artificial intelligence for decision support, understanding decision rationales is vital, especially in sensitive domains, in future epidemics, and other critical events.