Available Projects

Design of plant models with Helios and Bayesian optimisation

Climate change increases the need for crops that can withstand heat and drought. Crop breeding helps develop resilient plants, but evaluating large numbers of plants is challenging. Current methods like high-throughput phenotyping collect images, but extracting detailed plant traits is complex and requires advanced processing. An alternative approach is using procedural 3D plant models, like Helios, which simulate plants based on architectural parameters. However, Helios requires many parameters to generate accurate 3D models, making the model fitting process complex. This project focuses on using Bayesian optimization, a popular machine learning technique for optimising/calibrating expensive black-box processes, to improve the alignment of Helios’ 3D plant models with real crop images. By optimizing Helios’ parameters based on images from a 2022 UC Davis bean trial, we aim to generate fully annotated 3D crop models, which can be used for breeding selection to enhance climate resilience. In this project we will work closely with Henry Moss and Ioannis Droutsas (Wageningen University).

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.

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.

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.

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.