About Me
Founder of Energentic AI · Seconded Researcher at the University of Birmingham · RAEng Global Talent Awardee
I am a Lecturer at the Centre of Excellence for Data Science, Artificial Intelligence, and Modelling (DAIM) at University of Hull.
I am leading the MSc AI for Engineering programme and developed its core module, AI for Optimal Control, integrating cutting-edge AI into engineering practices.
I lead the AI x Energy Systems research group and the commercial project Energentic AI, a platform pioneering modular Agent-as-a-Service solutions for forecasting, optimisation, and control in energy systems.
Experience
University of Hull
Lecturer (Assistant Professor) in AI and Data Science
Centre of Excellence for Data Science, Artificial Intelligence, and Modelling (DAIM). Developed the core module AI for Optimal Control for the MSc AI for Engineering variant programme.
Data Science, Artificial Intelligence and Modelling Centre
Member of DAIM Management Team
Responsible for overseeing daily operations and contributing to strategic decision-making. Ensures effective cross-departmental communication and supports the delivery of high-quality education and research.
Postgraduate Research Director for DAIM
Liaises with the Faculty PGR management and Doctoral College, oversees PGR applications, investigates student cases, and enhances the postgraduate research experience.
Founder & Entrepreneurial Lead
Energentic AI
AI-driven agentic energy management platform pioneering modular Agent-as-a-Service solutions for forecasting, optimisation, and control in energy systems. Innovate UK ICURe programme for commercialisation.
Seconded Researcher
University of Birmingham
Birmingham Energy Institute. Modelling hydrogen and electric demand at airports for UK decarbonised aviation.
Education
Brunel University of London
PhD in Electronics and Electrical Engineering
2019 – 2023
The University of Edinburgh
MRes in Energy Systems
2018 – 2019
Shandong University
BEng in Energy and Environmental System Engineering
2014 – 2018
Research Interests
Interested in collaborating? Visit my research topics and reach out: "Hi, I am interested in [topic], let's collaborate!"
Selected Publications
Beyond Rule-Based Workflows: An Information-Flow-Orchestrated Multi-Agents Paradigm via Agent-to-Agent Communication
arXiv:2601.09883, 2026
An information-flow-orchestrated paradigm with a dedicated orchestrator coordinating agents via A2A; on GAIA it achieves 63.64% pass@1 accuracy, outperforming OWL by 8.49 points.
Anemoi: A Semi-Centralized Multi-agent Systems Based on Agent-to-Agent Communication MCP server from Coral Protocol
arXiv:2508.17068, 2025
A semi-centralized multi-agent system enabling structured, real-time agent-to-agent collaboration; on GAIA it reaches 52.73% accuracy, surpassing OWL by +9.09%.
Funded Projects
Net-Zero Emissions Aviation: Developing Hydrogen Energy Infrastructure at Airports
Modelling Hydrogen and Electric Demand at Airports for UK Decarbonised Aviation
Energentic: AI-Driven Agentic Energy Management for Battery Storage Systems
Tech Talks & Blogs
Bridging Minds and Machines: Agents with Human-in-the-Loop
Agentic AI Energy Management: LLM-Enhanced Decision-Making in Battery Energy Systems
Teaching
MSc Data Science and Artificial Intelligence, postgraduate level:
Applied Artificial Intelligence (Module 771767)
Builds on foundational AI concepts to prepare students for dissertation-level research. Topics include classification revisited, deep learning, applications to real-world problems, cognitive bias, and implications for equality.
Research and Application in AI and Data Science (Module 771765)
A dual-theme module exploring how AI and Data Science apply to real-world contexts such as sustainability, healthcare, social responsibility, and the natural environment. Students develop their own research proposal to tackle a genuine research project, drawing from these experiences to identify questions and limitations.
AI and Data Science Research Project (Module 771764)
Students plan and work independently on a complex research-based problem, and report on the aims, methods, and outcomes of their scientific investigation.
MSc AI for Engineering variant programme (core module):
AI for Optimal Control (Module 772220) [GitHub]
Covers control methods, model predictive control, and deep reinforcement learning applications in engineering. Integrates cutting-edge AI technologies into engineering practices to solve real-world industrial challenges.