Students and Alumni

Théo Molfassis
Funded by G-Research

Zekun Wu
Funded by BNP Paribas

Xiaoyu Zhang
Funded by BNP Paribas

Ming Liang Ang
Ming Liang is currently pursuing his PhD in the algorithmic and theoretical foundations of machine learning. He is interested in understanding the conditions under which transfer learning effectively occurs and hopes to elucidate key design principles that can enable more effective transfer learning to take place, especially in foundation models. Prior to the Foundational AI CDT, he obtained a degree in mathematics from the National University of Singapore (NUS) and was awarded the Ho Family Prize for being the top applied mathematics student of his cohort.

Wiem Ben Rim
Wiem joined the CDT 

Kai Biegun
Kai is primarily interested in developing Reinforcement Learning (RL) algorithms in partially observable environments. He is currently exploring ways to integrate probabilistic latent variable modelling techniques into RL algorithms to learn world models in order to plan and act under uncertainty. Prior to his PhD, Kai obtained a MSc in Machine Learning at UCL, and a MSci in Mathematics and Physics at Durham University.

Sierra Bonilla
Sierra Bonilla is interested in neural rendering frameworks for surgical scene reconstruction. Prior to this, Sierra completed a Master's in Medical Physics & Biomedical Engineering at UCL and holds a Bachelor's in Bioengineering and Mathematics minor from the University of Washington. With four years of prior experience in academic and industrial healthcare research, Sierra is dedicated to pushing the boundaries of AI for healthcare problems and beyond.

Natalia Burton
Natalia is interested in neuro-inspired AI and AI safety. Her research focus in the development of bio-plausible and energy efficient architectures capable of generalising and succeeding in goal-oriented tasks whilst minimising power consumption. Natalia has a degree in Electronic Engineering and a Master's in Business Management and Engineering. Prior to joining the Foundational AI CDT, she held a number of positions in tech-driven companies, most recently as Solutions Architect helping financial institutions to leverage the power of graph analytics and contextual data in uncovering fraud and other financial risks.

Milo Carroll
Milo is researching generative models for human motion synthesis. His primary objective is to understand how motion data can serve as a useful foundation for controlling physically simulated characters and robots in a reinforcement learning context. Additionally, Milo is exploring ways to efficiently repurpose these generative policies for downstream tasks, such as incorporating vision and language. His research primarily focuses on the fields of Reinforcement Learning, Robotics, and Deep Generative Models.

Francesca Channon
Francesca is pursuing her PhD, focusing on the application of machine learning techniques to enhance energy efficiency in the built environment. Her research aims to address the limitations of traditional energy assessment methods by integrating supervised learning, probabilistic approaches, and physics-informed machine learning. Prior to her PhD, Francesca obtained a degree in Mathematics from Warwick University, and an MSc in Statistics, and MSc in Applied Biotechnology and Biosciences from Imperial College London. She has industry experience in sustainable investment due diligence and alternative investments at J.P. Morgan Private Bank, and consulted on ESG-related topics at Tellus Matrix. Francesca's research is supervised by Professor Tomaso Aste and Professor John Shawe-Taylor.

George Drayson
George researches the long-term behaviour and evolution of Large Language Models, with a focus on model collapse, continual learning, self-improvement mechanisms, and the role of synthetic data. He is also Co-founder and Chief AI Officer at Locai Labs, a foundational AI company from the UK. Prior to this, George studied Engineering Science at the University of Oxford and trained in software engineering at Makers Academy.

Rares Dolga
Rares is interested in probabilistic machine learning largely focusing on methods for efficient language generation.  Prior to his PhD, he was a quantitative developer and then an AI researcher at JP. Morgan where he worked on NLP problems in finance. Rares obtained an MEng in computer science from UCL in 2020. 

Ahmet Guzel
Ahmet is interested in developing generalist AI agents that improve their capabilities through synthetic data and exhibit creativity in open-ended environments. Before pursuing this, he completed a year-long deep learning research internship at HUAWEI R&D in Cambridge, UK, and holds an MSc in Artificial Intelligence from Leeds University. He also has extensive experience in motorsport engineering

Nathan Herr
Nathan is passionate about using AI to create positive societal impacts. His research focuses on enabling effective, natural cooperation and coordination between AI agents and humans in various tasks. Nathan's research interests include enhancing complex sequential decision-making processes, especially through improved exploration in large language models (LLMs), to help AI agents navigate and solve more complex problems

Hongyu Lin
Hongyu is supervised by Professor Tomaso Aste and Professor John Shawe-Taylor. His research interests lie in statistical learning theory and causality. Prior to the PhD, he obtained a BSc in Mathematics and Physics from UCL, an MAST in Applied Mathematics from the University of Cambridge, and worked as a data scientist in the renewable energy industry

Varsha Ramineni
Varsha is a PhD student and DeepMind scholar in Artificial Intelligence (AI) at UCL, supervised by Prof. Emine Yilmaz and Prof. David Barber. She is passionate about bridging the technical and societal aspects of AI systems through interdisciplinary research, with particular interest in AI bias and fairness, and synthetic data. Varsha holds an MSc in Statistical Science from Oxford University and a BSc in Mathematics from Warwick University. Before joining the PhD program, she worked in mental health research, where she applied Bayesian adaptive design techniques to evaluate a digital intervention for healthcare staff experiencing intrusive memories of traumatic events.

Leonie Ritcher
Leonie is interested in reward models, deception and reinforcement learning from human feedback, specifically in the context of LLMs. She would like to contribute to the problem of aligning powerful artificial models with human values. Prior to starting the PhD, Leonie completed an internship at the Center for Human-Compatible Artificial Intelligence in Berkeley and worked as a Machine Learning Engineer. Before that, she completed an MSc in Artificial Intelligence at Imperial College London

Romy Williamson
Romy works in the intersection between classical geometry processing (differential geometry, and mesh algorithms) and modern techniques (neural networks). Prior to starting the PhD, Romy earnt a BA in Mathematics from the University of Oxford and an MSc in Computer Graphics, Vision and Imaging from UCL. During the PhD Tomy is focused mainly on the representation and processing of smooth surfaces. In another project, Romy has worked on simulation, PDEs and the Finite Element Method. Romy works in the UCL Smart Geometry Processing Group, under the supervision or Prof. Niloy Mitra.

William Bankes
William is broadly interested in studying probabilistic machine learning and how uncertainty quantification can enable machine learning models to make more intelligent decisions. Prior to his PhD, he spent two years working as a Machine Learning Engineer in Canary Wharf before completing an MSc in Computational Statistics and Machine Learning at UCL.

David Chanin
David studies mechanistic interpretability in LLMs. He is particularly interested in knowledge representation in LLM hidden activations and unsupervised interpretability techniques like Sparse Autoencoders (SAEs). Prior to starting his PhD, David worked as a software engineer, and holds degrees from Stanford University and Georgia Tech

Zonghao (Hudson) Chen
Hudson is supervised by François-Xavier Briol and Arthur Gretton. His research interests center around Wasserstein gradient flows, numerical integration and causal inference. Prior to his PhD, he obtained his bachelor's degree from the department of Electronic Engineering, Tsinghua University. Hudson also organizes the JumpTrading/ELLIS CSML seminar series on Computational Statistics and Machine Learning for the UCL ELLIS Unit.

Gbetondji Donovan
Gbetondji is broadly interested in deep learning, causality and graph theory, with the goal of designing more reliable and interpretable supervised learning systems that explicitly build on domain knowledge. He is a student at UCL and part of the ELLIS PhD program under the supervision of Matt Kusner and Michael Bronstein.

Jianwei Liu
Jianwei's current research direction involves utilising Generative ML models (e.g. diffusion models) and Neuromorphic learning for planning for legged robots. He's also interested in exploring topics such as continual learning and open-endness. Prior to starting the PhD, Jianwei obtained a MEng in Biomedical Engineering from Imperial College London, a MSc in Robotics and Computation from UCL, and worked in industry as a robotics software engineer on off-shore NDT inspection robots and construction robots.

Hossein A (Saeed) Rahmani
Hossein (aka Saeed) is a Ph.D. student in Foundational Artificial Intelligence at UCL AI Centre working on Fairness and Bias in Conversational Systems. Prior to starting his Ph.D., he received his MSc from the University of Zanjan, where he was working on context-aware recommender systems. His research interests lie in Information Retrieval, Recommender Systems, and Responsible AI.

Stuart Shanks
Stuart is interested in the intersection of optimal control and machine learning for robotics. He is supervised by Dr Dimitrios Kanoulas and Dr Carlo Ciliberto, and is working in the Robot Perception and Learning (RPL) Lab. He is particularly interested in continual learning algorithms for use on legged robots.

Hengyi Wang
Hengyi's research topic is "Neural Surface Reconstruction from Video". He is particularly interested in designing learning-based algorithms for real-time surface reconstructions from visual signals. Prior to starting his PhD,  he obtained a BSc in Telecommunications Engineering with Management from the Queen Mary University of London and an MSc in Computer Graphics, Vision, and Imaging from University College London

Jiayi Wang
Jiayi is working on natural language processing and adversarial machine learning.

Lorenz Wolf
Lorenz is generally interested in Multi Agent Reinforcement Learning, Generative Modelling, and Unsupervised Learning, and he is supervised by Mirco Musolesi. Prior to his PhD, Lorenz obtained his MSc in Statistics and BSc in Mathematics from Imperial College London

Rokas Bendikas
Rokas is working in the field of Surgical Robot Control and Robot Learning. His research project is focused on ‘Learning priors of high-level and low-level motion representations for autonomous robot control’. Therefore, I am interested in answering questions: ‘what perception modalities provide the optimum medium for an agent to learn’ and ‘how to learn complex behaviours that allow tackling long-horizon, multi-staged objectives in surgical setting’. During his Master’s studies, he completed his thesis at Dyson Robotics Lab, supervised by Prof. Andrew Davison. His project introduced imagination-augmented DQN, which allowed to learn optimal behavioural policy in dynamically complex environments. 

Harry (Jake) Cunningham
Jake is interested in understanding and improving uncertainty quantification in machine learning methods, focusing largely on Gaussian processes and kernel methods. He also has strong interests in applying statistical learning methods to problems in climate science. Prior to his PhD, he obtained an MEng in Engineering from the University of Oxford and MSc in Computing (Machine learning and AI) from Imperial College London.

Emilio McAllister Fognini
Emilio is interested in using AI and machine learning to aid in the solving the equations that govern physical systems. His primary area of focus is efficiently and quickly solving general Helmholtz problems, with research interests in Inverse problems and applications of AI to scientific computing and physical systems. He obtained a Master of Mathematics from UCL.

Zak Morgan
Zak is working under the supervision of Youngjun Cho and Sriram Subramanian in the UCL Interaction Center (UCLIC) primarily on the fields of computer vision and human-computer interaction.

Daniel Augusta de Souza
Daniel is broadly interested in the intersection of Bayesian machine learning models and the needs of their practitioners in the real world and how to potentially correct this mistmatch. This includes how theoretical benefits of Bayesian modelling may translate or not into practical benefits, including supposed interpretability benefits. Before joining UCL, he obtained his MSc and BSc degrees in Computer Science at the Federal University of Ceará in Brazil 

Alex Hawkins-Hooker
Alex is broadly interested in the development of machine learning methods for the study of biological molecules. His PhD research focusses on the use of deep generative models for protein design and is supervised by David Jones and Brooks Paige

Yao Lu
Yao is interested in everything, especially human language model interaction (HLMI). The ultimate goal of his research is to create a unified communication interface between human and machine through human language

Mirgahney Mohamed
Mirgahney H. Mohamed is a PhD student at University College London working on 3D computer vision and uncertainty estimation. He obtained his master's degree in Machine Intelligence from AMMI - African Master in Machine Intelligence at AIMS, and undergrad in Statistics and Computer Science from University of Khartoum Faculty of Mathematical Science

Yuchen Zhu
Yuchen is interested in making machine learning robust through modelling causal invariances within data. She develops both theory and algorithms for unbiased causal effect inference. In her investigation, she often uses kernel methods and methods inspired by the econometrics literature. Previously, she earned her Master's degree in Machine Learning from UCL, and a Bachelor's degree in Mathematics from Cambridge. She is advised by Professor Ricardo Silva and Dr Matt Kusner

Fernando Acero (Cohort 2)
Fernando submitted his thesis titled 'Embodied Artificial Intelligence: Advanced Deep Reinforcement Learning for Robot Sensorimotor Control' in July 2024 then started a research role in industry as part of the AI Research team at J.P. Morgan, led by Professor Manuela Veloso. The team works on exploring and advancing research in various fields of AI that are impactful to the firm’s clients and its business. Specifically, Fernando works on Optimization, Reinforcement Learning, Decision Making, and Control problems, leveraging the skills he developed during his PhD.
Fernando reports that having undertaken a research internship with the same team as part of his PhD was a positive contribution to being able to take an informed decision on the direction he wanted to follow upon completing. He adds that he is thankful to the CDT for supporting students to undertake internships in industry and remains thankful for all the resources available to CDT students, the community, and the exposure to industrial partners. Finally Fernando adds that he is extremely thankful to his supervisor for having provided a fruitful and supportive environment during his PhD research, and to all the CDT members and staff for their support throughout his journey

Reuben Adams (Cohort 2)
Reuben submitted his thesis titled  "Neural Network Generalisation in the Overparameterised Regime" in June 2025 and was awarded a PhD in November the same year.

Felix Biggs (Cohort 1)
Felix was awarded a PhD in June 2024 with his thesis 'Exploring Generalisation Performance through PAC-Bayes' and is now a senior machine learning researcher at SecondMind (www.secondmind.ai), working on active learning.

Samuel Cohen (Cohort 1)
Samuel  submitted his thesis titled 'Optimal transport on structured spaces: theory and applications' in January 2024 and is preparing for his viva.

Yue Feng (Cohort 2)
Yue submitted her thesis titled 'Knowledge Enhanced Task-oriented Dialogue Systems' and was awarded a PhD in January 2025 before joining the University of Birmingham as an assistant professor.

Denis Hadjivelichkov (Cohort 2) 
Denis submitted his thesis titled 'Semantic Correspondence in Robot Perception' which looks at how establishing visual semantic correspondences, such as similar parts and points of different objects, can be used to transfer robot manipulation skills across different object. Denis was awarded a PhD in July 2025.

Jean Kaddour (Cohort 2)  
Jean submitted his thesis "Steering by Step Size: Time, Geometry, and Structure in Neural Network Optimization" in October 2025 and was awarded a PhD in March 2026

Oscar Key (Cohort 2) 
Oscar submitted his thesis titled "Scalable Deep Learning & Data Assimilation" and is preparing for his viva

Akbir Khan (Cohort 3)
Akbir submitted his thesis titled 'Aligning Super Intelligent Systems via Multi-Agent Curricula' in April 2025 and ws awarded a PhD in June 2025

Robert Kirk (Cohort 2) 
Robert was awarded his PhD in March 2025 with his thesis titled 'Understanding and Evaluating Generalisation for Superhuman AI Systems'

Linqing Liu (Cohort 2)Linqing was awarded her PhD in May 2024 for her thesis titled 'Towards Generalized Open Domain Question Answering Systems'.

Yicheng Lou (Cohort 2)
Yicheng was awarded a PhD for his thesis titled "Reinforcement Learning from Imperfect Data" in May 2025

Aengus Lynch (Cohort 3)
Aengus submitted his thesis "The Persistent Vulnerability of Aligned AI Systems" in November 2025 and is preparing for his viva

Augustine Mavor-Parker (Cohort 1)
Augustine submitted his thesis titled 'Investigating Sample Efficient Deep Reinforcement Learning' and was awarded a PhD in July 2025

Luca Morreale (Cohort 1)
Luca submitted his thesis 'Neural Surface Representations' in May 2024 and was awarded a PhD in October 2024

Mariia (Masha) Naslidnyk (Cohort 3)
Masha submitted her thesis titled "Scalable Kernel-Based Distances for Statistical Inference and Integration" in October 2025 and was awarded a PhD in February 2026

Laura Ruis (Cohort 3)
Laura was awarded a PhD in January 2026 for her thesis titled "Reasoning in the Time of Scaling"

Antonin Schrab (Cohort 2)
Antonin submitted his thesis titled 'Optimal Kernel Hypothesis Testing' in September 2024 and received his PhD in March 2025

Jas Semrl (Cohort 1)
Jas was awarded a PhD with his thesis titled 'Finite Representations in Relation Algebra' in December 2023. 

Oliver Slumbers (Cohort 2)
Oliver was awarded a PhD for his thesis titled "Population Dynamics in Multi-Agent Systems" in November 2025

Jingwen Wang (Cohort 1)
Jingwen was awarded his PhD in June 2024 with his thesis titled Exploiting Neural Priors in Visual SLAM.

Changmin Yu (Cohort 1) 
Changmin was awarded a PhD in June 2024 with his thesis titled 'Hippocampus-Inspired Representation Learning for Artificial Agents'.

Jakob Zeitler (Cohort 1)
Jakob received his PhD for his thesis titled 'Elements of Partial Identification: Theory and Applications in July 2025    

Sicelukwanda Zwane (Cohort 2) 
Sicelukwanda submitted his thesis titled "Safety-aware learning in real-world robotics with Gaussian processes" and is preparing for his viva