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Optimisation DAIM

Research areas

Artificial intelligence-driven optimisation is transforming industries by enabling computers to make decisions that rival human intelligence. Advanced algorithms allow AI systems to find optimal solutions to complex problems more efficiently than traditional methods. In autonomous driving, AI processes sensor data to navigate vehicles safely. Unmanned aerial vehicles (UAVs) use AI for precise flight operations. Power systems benefit from AI by balancing supply and demand, optimising energy distribution, and predicting maintenance needs.

Manufacturing sees improved production processes, reduced waste, and enhanced product quality through AI optimisation. IoT and Sensors collect data from physical assets, providing real-time insights into their condition and performance. AI and Machine Learning algorithms analyse this data, enabling predictive maintenance, anomaly detection, and process optimisation. Deep reinforcement learning (DRL) allows AI to learn optimal strategies through trial and error. Multi-agent reinforcement learning (MARL) enables coordination in complex environments, such as traffic management and warehouse robotics. Model predictive control (MPC) uses real-time data for dynamic adjustments, ensuring optimal performance.

Dr. Zekun Guo is leading the Large Language Model (LLM) Agent research team within DAIM to explore innovative applications of LLM agents across diverse fields, including biology, healthcare, engineering, and business. This initiative focuses on advancing the understanding and capabilities of multi-agent systems driven by LLMs to solve complex, real-world problems.

Overall, AI-driven optimisation enhances decision-making, efficiency, and innovation across various applications.

 

  • Academic staff

    Dr Zekun Guo, Z.Guo2@hull.ac.uk

    Dr Matishalin Patel, Matishalin.Patel@hull.ac.uk

    Dr Bhupesh Mishra, Bhupesh.Mishra@hull.ac.uk

    Dr Julius Mboli, j.mboli@hull.ac.uk

    Dr Kenneth Wertheim, k.y.wertheim@hull.ac.uk

    Dr Temitayo Matthew Fagbola, temitayo-matthew.fagbola@hull.ac.uk

    Eva Sousa, e.sousa@hull.ac.uk

  • PhDs/GTAs/MSc by thesis

    Waqas Ahmed, AI-based Autonomous Transportation for Emergency Healthcare Services,

    Supervisors: Dr Muhammad Khalid, Prof. Adil Khan

    Email: W.AHMED-2021@hull.ac.uk

     

    Neil Chaabouni-Yaiche, Financial Analysis and Data Extraction through Natural Language Processing with Asset allocation/Price Prediction

    Supervisors: Dr John Fry, Dr Bhupesh Mishra

    Email: N.Chaabouni-2014@hull.ac.uk

  • Recent projects and grants

    Evolution of AI SystemsFocused on the development of network topologies under incomplete information scenarios and computational complexity of formal grammars. Utilising NEAT (Neural Evolution of Augmented Topologies) and hyper-NEAT algorithms, the project aims to create an open-source Julia package to facilitate future research.

    This work combines evolutionary theory, AI coordination, and reinforcement learning to better understand the behaviour of AI systems developed using evolutionary algorithms. Special interest in pathologies of such approaches and alignment issues with developing AI systems in this way.

    Contact Dr Matishalin Patel, Patel@hull.ac.uk for more details.

     

    Net-Zero Emissions Aviation: Developing Hydrogen Energy Infrastructure at Airports

    Decarbonising aviation is essential for the UK's net zero emissions target by 2050, with plans for all airport operations and domestic flights in England to reach net zero by 2040. Hydrogen is a promising aviation fuel, but significant advancements in airport infrastructure are needed. While some airports, like Bristol, are exploring hydrogen infrastructure, technical knowledge gaps persist. Current research lacks detailed designs to integrate hydrogen with existing energy systems and meet net zero flight requirements. This project will evaluate, design, and optimise hydrogen-based airport infrastructure, covering production, storage, and transportation to support domestic hydrogen aircraft. Using a bottom-up approach, we will model hydrogen demand based on flight operations and assess devices' efficiency and risks. Case studies with a major UK airport and a hydrogen aircraft prototype will validate findings. Our research aims to fill critical gaps, providing actionable insights for sustainable aviation infrastructure and supporting the UK's 2040 targets.

    For more information: Net-Zero Emissions Aviation: Developing Hydrogen Energy Infrastructure at Airports.

     

    Multi-agent Large Language Model

    Dr. Zekun Guo is leading a focused research group within DAIM to explore cutting-edge applications of Large Language Models (LLMs), particularly in Retrieval-Augmented Generation (RAG), agent systems, and multi-agent frameworks. The team includes Eva Sousa, Dr. Julius Mboli, Dr. Kenneth Wertheim, Dr. Matishalin Patel, and Dr. Temitayo Matthew Fagbola, bringing together a wealth of expertise and diverse perspectives.

    The group aims to develop and evaluate multi-agent systems capable of addressing real-world challenges, such as decision-making in hospitals, factories, or power systems. These systems will leverage LLMs to generate and coordinate decisions, providing a robust platform to test their efficacy in complex environments. By combining the team’s innovative ideas and technical skills, this initiative seeks to establish a structured pathway for impactful research.

    Dr. Guo is currently exploring frameworks like Camel-AI and other multi-agent LLM systems, and the team is committed to sharing insights and collaborating to accelerate progress. The group envisions producing significant outcomes, including co-authored publications, reusable tools, and metrics for wider applications. This initiative aims to set a foundation for transformative research in LLM multi-agent systems, with tools and findings applicable across various domains.

  • Selected recent publications
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