Cohort 3 (2026 Entry)

Research: Exploring Accuracy and Performance Tradeoffs for Compiler Optimisations of AI Model Deployment

Supervisors: Ajitha Rajan, Adriana Sejfia, Héctor Menéndez

My research interests lie in reliable and efficient machine learning systems, particularly the testing and evaluation of the software and hardware infrastructure underlying modern ML workloads. I am interested in how we can improve the reliability of ML systems across compilers, runtimes, and heterogeneous hardware, including GPUs and other accelerators. Previously, I worked on systems verification, compiler optimisation, fuzzing, and robust machine learning. Including developing testing approaches for system simulators, energy-efficient software testing, and working on hardware-aware ML optimisation and quantisation infrastructure. I completed my undergraduate and master's studies in Computer Science at King's College London before joining the University of Edinburgh.

Photo: Chris Scott

Research: Understanding vulnerability exploitability towards improving vulnerability detection

Supervisors: Adriana Sejfia, Ajitha Rajan

Fascinated by what happens when good code goes bad and how we can fix it. My research interests are software engineering, software vulnerabilities and software testing. I build automated tools that improve software engineering workflows.

Research: Compressing LLM Reasoning while Preserving Legibility

Supervisors: Shay Cohen, Ivan Titov

Ed's research will explore efficient and interpretable LLM reasoning. His current project proposes to explore methods that allow translation between compressed or latent reasoning processes and full-form natural language such that reasoning quality, token budget, and legibility are maintained.

Research: Multi-agent simulation and constrained, multi-objective reinforcement learning technologies for install scheduling optimisation

Supervisors: Fengxiang He, Chetna Arora

My research project can be separated into two connected strands. The first focuses on digital twins in the context of field service management. The second uses these digital twins as a test bed to design and prototype machine learning systems for operational research problems. I will also be exploring applications of my research to distributed machine learning. My interests include multi-agent systems, reinforcement learning, parallel computing, and operational research.

I graduated from the University of Manchester with first-class honours in Mathematics (BSc) before completing an MSc in Artificial Intelligence at Edinburgh. My MSc thesis proposed a decentralised machine learning framework and a family of decentralised SGD algorithms. Previously, I collaborated on a project developing an SMPC protocol for secure neural network inference.

Research: AI Assistants for Video Games

Supervisors: Kartic Subr, Amos Storkey

My research explores how AI systems can understand, reason about, and interact with complex virtual environments, with a particular focus on intelligent game assistants. I am interested in combining causal modelling, program synthesis, and learning to build representations that support planning, explanation, and decision-making. For example, such assistants could help players understand game mechanics and anticipate the consequences of their actions. My background is in Electronics and Computer Science, and my broader interests include computer graphics and efficient optimisation. Alongside this, I am exploring inverse rendering methods for recovering underlying scene properties and supporting realistic relighting and interactive scene editing.

Research: Efficient and Reliable Solutions to large-scale graph optimisation problems based on Local Sub-graph Joining

Supervisors: Liang Zhao, Tianqi Hou

Hi, I am a robotics guy who enjoys a good robotics challenge. My previous work used reinforcement learning for energy efficient microrobot navigation in dynamic environments, quadruped navigation over uneven 3D terrain, and joint optimisation of robot morphology and control. I also used RL to control a robotic arm that helps a person carry water steadily. My current research focuses on SLAM and graph optimisation for more accurate and reliable mapping. I am always interested in how robots can learn to make better decisions and move reliably in complex environments. I enjoy exchanging ideas with others and exploring new perspectives across robotics and learning model to see how far we can push what robots can do.

Research: AI-Enabled Verification of Semantic Equivalent Between Binaries

Supervisors: Elizabeth Polgreen, Michael O'Boyle

I first came to the University of Edinburgh for my bachelor's in computer science - where I received a Class Medal upon completion. During my undergraduate studies, I grew interests in Natural Language Processing, Programming Languages and eventually some Systems tasks. During my undergraduate thesis, under Elizabeth Polgreen's supervision, I devised a method to obtain post-hoc formal proofs of equivalence for tensor programs. Subsequently, I joined Skyscanner, completing rotations as a frontend engineer and a product manager for the data platform. I'm now interested how we can use formal methods to gain confidence in the functionality of our code. My initial efforts will target equivalence verification between binary programs. In my personal life, I like to spend time cooking and trying out new hobbies and hoping some of them stick - currently cycling and fermentation.

Photo: Chris Scott

Research: AI Methods to Identify Trends in Simulation and Characterisation of Wafer Chip Design

Supervisors: Ajitha Rajan, Isobel Nicholson, John McPhillimy

I am a PhD student with the CDT in Machine Learning Systems, and STMicroelectronics. My research focuses on developing methods for advancing simulation and charaterisation in the electronics industry. I have a background in Physics with previous work in computational physics developing simulation techniques, and using machine learning for characterisation. The CDT exists in an interesting interdisciplinary space which enables and encourages my research. I am particularly interested in bridging physical insight and data-driven approaches to improve the accuracy, efficiency, and interpretability of all types of models and simulations, supporting more informed materials and technology development.

Research: Towards Coding Agents that Reliably Observe, Verify and Optimize Compute Systems

Supervisors: Luo Mai, Wenda Li

I want to build the next generation of "operating systems" for AI, and eventually, have AI build them for us. Before coming to Edinburgh, I completed my master's degree at the University of Science and Technology of China, where my research focused on improving the efficiency of machine learning training and inference by orchestrating and co-optimizing compute, communication, and memory resources. During my PhD, I plan to continue improving the efficiency and scalability of ML workloads through systems research. At the same time, I am interested in exploring how AI agents can autonomously and rigorously design and implement efficient, scalable, and reliable systems with minimal human supervision.

Research: LLM-Based AI Companion for Multi-Agent Collaboration

Supervisors: Amos Storkey, Henry Gouk

I'm a PhD student working on machine learning and multi-agent systems. I'm broadly interested in reliable and interpretable machine learning systems and am currently working on methodological developments in reinforcement learning. Outside of my area of study, I enjoy learning about things related to ecology, earth sciences and languages.

Research: Optimization and learning theory, Bayesian inference and probabilistic modelling, Statistical learning theory, Differential privacy and distributed learning

Supervisors: Henry Gouk, Amos Storkey

My research interests lie at the intersection of optimization, statistics, and machine learning, with a particular emphasis on the theoretical foundations of learning algorithms. I am interested in understanding the interplay between optimization and probabilistic inference, as well as establishing rigorous theoretical guarantees for modern learning methods. My previous research has covered non-convex optimization, convergence analysis, differential privacy, reasoning in LLMs and distributed learning. I hold a bachelor's degree in computer engineering from Sharif University of Technology.

Photo: Chris Scott

Research: Multi-Modal Memory models for vision, language and action

Supervisors: Laura Sevilla-Lara, Alessandro Suglia

By the end of my bachelor's degree in computer science and AI at Loughborough University, I developed a strong conviction that studying the human mind can inspire new and efficient algorithms in AI. After a year as a software developer, I attended UCL's MSc in Cognitive and Decision Sciences, where I engaged with many topics such as embodied cognition, language and memory.

It is these three topics that form the basis of my research in the ML Systems CDT. By developing long and short-term memory mechanisms for Vision-Language-Action models, I aim to enable these systems to continually learn and adapt from physical interactions for long time horizons. Currently, I am creating an efficient method to address the stability-plasticity dilemma in neural networks by identifying and sparsely updating the model parameters.

Research: Deep Learning based Solutions to large-scale graph optimisation problems

Supervisors: Liang Zhao, Tianqi Hou

My background is in statistics and data science. I hold a BSc in Statistics, Economics and Finance from University College London (UCL) and an MPhil in Data Intensive Science from the University of Cambridge. Before joining Edinburgh, I worked on variational inference and Bayesian neural networks. My PhD focuses on developing efficient robotic algorithms using machine learning methods, with a particular interest in Simultaneous Localisation and Mapping (SLAM). I am currently working on Gaussian process-based mapping, under the supervision of Dr Liang Zhao. I am interested in probabilistic approaches to robotics, where robots keep track of their own uncertainty. This helps with noisy sensors, changing environments and limited data.

Photo: Chris Scott

Research: Cool Cores: thermal management for next-generation computing systems through a cross-stack approach

Supervisors: Boris Grot

I completed my undergraduate degree in Electronic Engineering at the University of Southampton. During my time at Southampton, I became interested in VLSI, digital design, and computer architecture, and worked in chip design for two years following my graduation. My research interests lie broadly in computer architecture and in understanding how novel computer systems should be designed for the challenges of current and future workloads, such as machine learning. I am also interested in how advanced process technologies enable new computer architectures which are more performant and energy efficient. In my free time I enjoy spending time outdoors hiking, running, and cycling.

Photo: Chris Scott

Research:Computer architecture, 3D-stacked and memory-centric system designs

Supervisors: Boris Grot

My research focuses on computer architecture, with a particular emphasis on 3D-stacked and memory-centric system designs for machine learning workloads. Currently, I am exploring memory-stacked GPU architectures for large language model (LLM) inference, investigating how emerging packaging technologies and memory hierarchies can address the performance, bandwidth, and energy efficiency challenges of modern AI systems. I am interested in designing next-generation computing platforms that bridge the gap between rapidly growing model demands and hardware capabilities. Before moving to Edinburgh, I earned my bachelor's degrees in Physics and Electrical & Computer Engineering, as well as a master's degree in Electrical Engineering, from the University of Rochester. I also interned at Intel and Ampere Computing, working on system architecture, performance optimization, and hardware-software co-design.