Christian Cabrera Jojoa — Projects to Supervise

Projects on Agentic System Implementation (theme)

Supervisors: Christian Cabrera Jojoa, Neil D. Lawrence

Prerequisite: L172 Information, Energy and Intelligence (IEI), or equivalent preparation in information theory, maximum entropy, and information geometry.

The research group is interested in how we engineer multi-agent systems for reliability and transparancy. We have developed the DOAgent framework for delivering such multi-agent systems. This list of Part III / MPhil projects apply information-topographic ideas to engineered multi-agent and organisational systems using DOAgent. The projects listed serve as ideas, we will run a maximum of two or three projects in this space.

Projects on Data-Oriented Multi-Agent Systems (theme)

Supervisors: Christian Cabrera Jojoa, Neil D. Lawrence

Data Oriented Architecture (DOA) is a software architecture pattern that creates data-driven, loosely coupled, decentralised, and open systems. DOA achieves these goals by exposing systems’ data as a first-class citizen to distributed, stateless, and asynchronous components. These design decisions enable DOA-based systems to achieve data availability, reusability, and monitoring, as well as adaptability, scalability, and autonomy. DOAgent is a Python library for building multi-agent systems where shared data is the primary interface between decentralised agents that cooperate in open environments. It follows three principles: data as a first-class citizen, decentralisation, and openness. These Part II, Part III, and MPhil projects use that library. Related group programme: Data-Oriented Architectures for AI-based Systems.

Decision Support for Cities Management

Supervisors: Christian Cabrera Jojoa, Neil D. Lawrence

Smart cities are urban environments where computing devices generate considerable amounts of heterogeneous data. Cities’ authorities need sophisticated platforms to manage and analyse such data before using it. In this project you will provide a flexible, scalable, and real time decision-support tool for city managers. This tool will manage data from heterogeneous sources and provide meaningful insights to city managers to support their decisions.

Edge Service Placement based on Large Language Models (LLMs)

Supervisors: Christian Cabrera Jojoa, Neil D. Lawrence

Edge computing architectures propose placing software services closer to end users. This distributed placement can enable super-low latency, data-intensive applications that can benefit domains as diverse as virtual reality, gaming, and healthcare. The decision of what services to deploy in which edge is an optimisation problem called service placement. Solutions to the service placement problem must consider latency requirements and resource constraints while assigning services to edge servers in an automatic fashion. Exact, approximation, heuristics, and meta-heuristic algorithms are traditional approaches to solving such an optimisation problem. This project proposes to explore the capabilities of Large Language Models (LLMs) to make the placement decisions. The main idea is to replace current algorithms with a LLM-based agent.

Interpretable Machine Learning for Intensive Care Decision Support

Supervisors: Christian Cabrera Jojoa, Neil D. Lawrence

Intensive care units (ICUs) generate vast volumes of patient data, yet clinicians often lack tools to translate this data into timely, trustworthy decisions. The aICU project, which aims to support safe, interpretable, and clinically meaningful decision-making by establishing a standardised pipeline for developing, evaluating, and deploying AI in critical care. This project proposes to reproduce existing machine learning models that address specific ICU problems (e.g., mortality prediction, sepsis detection, ventilator weaning, or length-of-stay estimation) and then investigate interpretability methods to make the model predictions understandable to clinicians.

Optimisation Benchmarks for Online RL

Supervisors: Pierre Thodoroff, Christian Cabrera Jojoa, Neil D. Lawrence

Reinforcement Learning algorithms have been applied in different domains. Now there is a growing interest in applying RL algorithms to optimisation problems. Such algorithms are a better alternative to produce near-optimal solutions in dynamic environments, compared against exact or approximation algorithms. Benchmarks are a key element in the development and evaluation of novel algorithms as they enable a standardised comparison of these algorithms’ performance. In this project you will provide a set of optimisation benchmarks to evaluate online RL algorithms.

Self-Adaptive Systems and Large Language Models (LLMs)

Supervisors: Christian Cabrera Jojoa, Neil D. Lawrence

Software systems are increasingly complex and include different actors and components interacting in dynamic environments. Maintaining such systems is a difficult task where human intervention is not feasible. Autonomous computing has explored approaches to optimise systems’ performance by changing their structure, behaviour, or environment variables. These approaches rely on feedback loops that accumulate knowledge from the system interactions to inform autonomous decision-making. However, this knowledge is often limited, constraining the systems’ interpretability and adaptability. This project proposes to explore the capabilities of Large Language Models (LLMs) for self-adaptive systems. The main idea is to replace current autonomous RL-agents with LLM-based agents to make self-adaptive decisions.