Available Projects

Automatic Discovery of Trade-off Between Utility and Energy Comsumption of ML models

The use of machine learning models both in academia and industry is on the rise. And so is the environmental impact of ML models. While deploying a model to production, it is important to be able to estimate its carbon footprint and to understand the costs involved in running it. Balancing performance and carbon efficiency of ML models becomes critical to ensure that the benefits of ML are maximized while minimizing its environmental costs. This project proposes to develop a procedure that automatically discovers and quantifies this trade-off.

Improving Probabilistic Models for Machine Learning in Science

Each of the six following projects involves understanding and extending an existing probabilistic model commonly used in a scientific context to improve usability and model understanding. Please email me (ar847@cam.ac.uk) if interested.

Machine Learning (Bayesian Methodology, Inference and Applications)

Students interested to work with me should come up with a grain of an idea before reaching out. If there is a match I would be happy to discuss to flesh out the details and create a project out of it. I always believed that part of doing a project is coming up with ideas and angles ripe for exploration.

I am broadly interested in probabilistic machine learning and applications in climate science.

Machine Learning for Modelling Formula One Races

The machine learning group is working with one of the leading forumla one teams in analysis of data generated in Formula One races with the aim of improving strategy. With this aim we are running one or more projects this year focussed on Formula One data. Formula one is a data intensive sport, information about the location of each team’s car during the race is provided to the teams. Optimization of pit stop strategy can make the difference between winning and losing the race. There are commercial confidentiality issues over which areas will be studied, but interested students can discuss these areas directly with the supervisors.

Self-Adaptive Systems and Large Language Models (LLMs)

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.

Unconventional AI and explainable AI

These twelve projects in unconvential and explainable AI would be supervised by Soumya Banerjee.

Edge Service Placement based on Large Language Models (LLMs)

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.

Machine Learning (Bayesian Methodology, Inference and Applications)

Students interested to work with me should come up with a grain of an idea before reaching out. If there is a match I would be happy to discuss to flesh out the details and create a project out of it. I always believed that part of doing a project is coming up with ideas and angles ripe for exploration.

I am broadly interested in probabilistic machine learning and applications in climate science.

Machine Learning for Modelling Formula One Races

The machine learning group is working with one of the leading forumla one teams in analysis of data generated in Formula One races with the aim of improving strategy. With this aim we are running one or more projects this year focussed on Formula One data. Formula one is a data intensive sport, information about the location of each team’s car during the race is provided to the teams. Optimization of pit stop strategy can make the difference between winning and losing the race. There are commercial confidentiality issues over which areas will be studied, but interested students can discuss these areas directly with the supervisors.

Self-Adaptive Systems and Large Language Models (LLMs)

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.