MINDS researchers have gone on to postdoctoral positions, industry roles and enterprise activity across the AI and electronics sectors. Their profiles below give a picture of the range of research carried out through the CDT and the paths it has opened up.
Meet the researchers
Current doctoral researchers
The MINDS Centre for Doctoral Training is no longer recruiting. Its doctoral researchers are completing their research, and this site shares their work and the programme’s wider activity.
Epifanios Baikas
Developing privacy-preserving AI that can learn directly on memory-constrained Internet of Things devices, reducing reliance on cloud processing.
Tyler Clark
Tyler Clark is demonstrating that high-performance deep reinforcement learning can run on standard desktop computing, reducing reliance on expensive large-scale infrastructure.
Xiyue Fan
Training transformer models on mathematical expressions to investigate whether AI can approximate solutions to differential equations in a symbolic form that may be easier to inspect than numerical output alone.
Oliver Grainge
Oliver Grainge is developing compact visual AI systems that help robots and drones recognise where they are when GPS is unavailable, using hardware with limited memory and computing power.
Balint Gucsi
Developing social robots that adapt their decisions and behaviour to individual users, with the aim of making human-robot interactions more useful and less frustrating.
Jack Hilton-Jones
Jack Hilton-Jones is developing predictive-control methods for flexible magnetic robots, aiming to help them navigate blood vessels more safely and precisely during minimally invasive procedures.
Sam Hilton-Jones
Using mathematical analysis to investigate how transformer models focus and reason across long sequences, helping to open the black box of modern large language models.
William Hunt
Developing interfaces and language-based control methods that help a single human operator understand, communicate with and oversee swarms of autonomous robots.
Xander Lowe
Combining laboratory electrophysiology and computational brain modelling to investigate short-term memory, with potential relevance to neurological conditions and low-energy, brain-inspired computing.
Kieran Maguire
Investigating how patterns across networks shape the link-prediction models behind many online recommendations, with the aim of making recommendations more relevant, transparent and fair.
Rudra Mutalik
Helping large language models learn new information without forgetting existing knowledge, with the aim of keeping AI systems up to date without extensive, costly retraining.
Yaseen M. Osman
Investigating how large language models can run more efficiently on lower-compute devices, with potential benefits for privacy, sustainability and wider access to AI.
Keniel Peart
Developing AI that interprets sign-language movement from video and radar using 3D human modelling, with potential applications in assistive communication and privacy-aware sensing.
Michail Rontionov
Michail Rontionov is developing compiler tools that translate neuromorphic algorithms into digital FPGA hardware, aiming to bridge the gap between brain-inspired computing research and physical devices.
Nehal Yasin
Investigating shortcut learning, where machine learning models rely on the easiest patterns in their data, and how models could be helped to learn more diverse and robust patterns.
Lianghong Ye
Lianghong Ye is investigating whether tiny mechanical resonators can process time-dependent data, with the aim of supporting lower-power physical computing for sensors and edge AI.
MINDS alumni
These researchers have completed their doctoral research with MINDS. Their profiles describe that research and, where they have shared it, what they do now.
Jack Dymond
Developed resource-aware deep-learning systems that adjust their computational effort to each situation. Now a Data Scientist at The Alan Turing Institute.
Charles Hutchins
Investigated how drone networks can detect and respond to hidden cyberattacks while telling them apart from ordinary signal loss. Now a Knowledge Exchange and Enterprise Fellow at the University of Southampton.
Ilias Kazantzidis
Leveraging human feedback to build safer autonomous agents
Olaf Lipinski
Olaf Lipinski is a MINDS alumnus whose doctoral research investigated how autonomous AI agents develop languages from scratch, including how they communicate about events across time. Now a Research Fellow at Nanyang Technological University.
Matthew Pugh
Matthew Pugh is a MINDS alumnus whose research explores the mathematical foundations of machine learning and how theoretically grounded methods can support practical problems in sensing and anomaly detection.
