This is a non-exhaustive list of the potential supervisors associated with the Centre for Doctoral Training in Foundational Artificial Intelligence with links to their webpages.
Daniel Alexander is in both the Department of Computer Science and the Centre for Medical Image Computing, Daniel's interests include: AI-for-health, medical imaging, pattern recognition, computational modelling
David Barber Director for the CDT and based in Computer Science, David's research interests include: Probabilistic Modelling and Reasoning with Uncertainty. Deep Learning, Gernative Models, Approximate Inference.
Peter Bentley is based in the Department of Computer Science as part of the Intelligent Systems Group, they have the following research interests: Bio-inspired computation including evolutionary computation, developmental systems, immune systems, spiking neural networks, agent-based modelling of natural systems.
Francois-Xavier Briol is based in the Department of Statistical Science.
His research interests are at the intersection of computational statistics and machine learning, and he is particularly interested in developing methodology for learning in the context of computationally expensive probabilistic models. Particular sub-areas of interest include learning/inference for unnormalised or generative models, algorithms for Bayesian computation (including sampling and variance reduction), transfer learning and kernel methods.
Neil Burgess is based in the Institute of Cognitive Neuroscience, and their research interests include: Computational modelling, analysis and experiments regarding the algorithms and neuronal representations for spatial localisation, episodic memory and model-based learning.
Matteo Carandini uses computational tools to study the activity of large populations of neurons in mice behaving in virtual reality. His work investigates how the brain integrates sensory signals from vision or hearing with internal signals related to navigation or value. He is a founding member of the International Brain Laboratory.
Benny Chain is interested in learning the guiding principles of the human immune system, and how it recognises potentially dangerous infections or cancer, and reacts against them to protect us from disease. The immune system makes complex decisions in response to tiny changes in the body, and achieves this using a highly dynamic distributed architecture. In partnership with Prof. John Shawe-Taylor and other computer scientists, they combine machine learning and stochastic stimulations to learn the design principles of this extraordinarily flexible intelligent system.
Youngjun Cho is based in Computer Science. He explores, builds and evaluates novel techniques and technologies for the next generation of artificial intelligence-enabled physiological computing that boosts disability technology innovation. Physiological computing is technology that helps us listen to our bodily functions and psychological needs (e.g. contact-less heart rate monitoring and automatic anxiety detection).
Ingemar Cox'a current research is in the application of AI, machine learning, and statistical natural language processing to large data sets of digital trails, e.g. web query logs, Twitter, to infer information about the health of individuals or populations. His research has included (i) estimating the prevalence of diseases in a population, (ii) monitoring mass gatherings for disease outbreaks, (iii) evaluating the effectiveness of vaccination programs, (iv) estimating the virulence of disease, (v) and estimating an individual’s risk of a disease. Recent work has focused on multitask and transfer learning, the latter facilitating training in regions where data is data is available and deploying in countries where training data is unavailable. Current work focuses on forecasting. This work is funded through an EPSRC £15M Interdisciplinary Research Collaboration (IRC) on Early Warning Sensing Systems for Infectious Disease, known as i-sense ( https://www.i-sense.org.uk ).
Benjamin Guedj’s research interests include, but are not limited to, machine learning, deep learning, statistical learning theory, computational statistics, PAC-Bayes theory, reinforcement learning, representation learning, kernel methods, semantic information pursuit, active learning, online learning, learning on graphs.
Thore Graepel is interested in the topics of deep learning, reinforcement learning and multi-agent learning. Ideally students should bring excellent maths and coding skills, and have experience in deep learning, reinforcement learning and game theory.
Arthur Gretton's research is focused on the two complementary areas: training generative models, and nonparametric hypothesis testing. Generative models are able to draw samples matching target reference samples, such as images: these include Generative Adversarial Networks, exponential family models with neural network parameters, and most recently, hybrids of the two. Hypothesis tests focus on evaluating generative models (e.g., comparing different architectures), or on discovering relations in data, such as statistical dependence
Kenneth Harris studies information processing in the brain. We can now record the activity of tens of thousands of living neurons simultaneously in real time. We use mathematical methods to characterize the structure of this high-dimensional data, to understand how the brain computes, which in turn may help the design of artificial learning systems.
Mark Herbster is based in the Department of Computer Science as part of the Intelligent Systems Group, they have the following research interests: Online Learning, Matrix Completion, and Semi-supervised Learning.
Robin Hirsch's reserach interests are: Logic, Algebra, Games (mathematical games) and Algebraic Logic
Anthony Hunter's research is in the area of machine reasoning, with a particular interest in computational models of argument (formalization of monological and dialogical argumentation using logic and probability theory; handling enthymemes through the use of common/commonsense knowledge; and applications in decision making and sense making) and computational persuasion (automated systems for engaging in a dialogue with a user, i.e. the persuadee, in order to persuade them through the use of convincing arguments and counterarguments). Further topics of interest include non-monotonic reasoning, commonsense reasoning, paraconsistent reasoning, methods for aggregating knowledge, and measures of inconsistency.
Franz Kiraly is based in the Department of Statistical Science and their research interests include: Model validation/comparison, predictive performance evaluation, automatic pipeline building, ML with time series and hierarchically structured data, machine learning toolboxes
Iasonas Kokkinos, based in the Department of Computer Science, their research interests include: Computer Vision, Multi-Task Learning, Unsupervised Learning, 3D Modelling
Matt Kusner is in the Department of Computer Science. His research interests include causal inference, algorithmic fairness, structure learning, and secure computation.
Niloy J. Mitra leads the Smart Geometry Processing group in the Department of Computer Science at University College London. He received his PhD degree from Stanford University under the guidance of Leonidas Guibas. His research interests include generative modeling, shape analysis, computational design and fabrication, and machine learning for geometry processing. Niloy received the ACM Siggraph Significant New Researcher Award in 2013, the BCS Roger Needham award in 2015, and the Eurographics Outstanding Technical Achievement Award 2019. More at http://geometry.cs.ucl.ac.uk/index.php. Keywords: geometry processing, generative modeling, neural modeling and rerendering, shape analysis, ML-assisted creative workflows.
Massimiliano Pontil's research interests are in the areas of machine learning, with a focus on statistical learning theory, kernel methods, multitask and transfer learning, online learning, and sparsity regularisation. Other recent interests include algorithmic fairness, meta-learning and hyper-parameter optimisation.
Geraint Rees uses high dimensional inference to study human cognition using structural and functional neuroimaging. His work also extends beyond neuroimaging to include using machine learning and multivariate inference to predict outcome in healthcare.
Sebastian Riedel's research interests include: Natural Language Processing, Machine Reading and Reasoning, Graphical Models, Question Answering, Fact Checking, Program Induction and Synthesis, Neuro-Symbolic methods.
Tim Rocktäschel is in the Department of Computer Science. His research interests include reinforcement learning, deep learning and natural language processing.
Maneesh Sahani is based in the Gatsby Computational Neuroscience Unit, seeking applicants researching the following areas: Probabilistic and Bayesian machine learning; unsupervised learning; approximate inference including variational methods and belief propagation; planning and control under uncertainty; theoretical and data-driven neuroscience
John Shawe-Taylor's research interests include: statistical learning theory, PAC-Bayes analysis, reinforcement learning, kernel methods, fairness in machine learning, artificial intelligence for online education, semantic information pursuit, multi-modal data analysis
Alexandra Silva is based in the Department of Computer Science as part of the Programming Principles and Logic Verification Group with the following research interests: verification of machine learning algorithms, semantics of probabilistic programming languages, active learning algorithms, logic, and formal methods.
Ricardo Silva is based in the Department of Statistical Science, primarily interested in working with students who want to focus in machine learning. His research interests include: causal inference, particularly on its intersection with graphical models, latent variable models, and flexible supervised and unsupervised learning algorithms; algorithmic fairness; relational and spatiotemporal learning; applications in complex systems.
Anthony Steed is interested in immersive user interfaces and production of 3D content. He is also interested in applications of AI in production (e.g. steering of semi-automatic content creation, detail expansion for model), also modelling of user behaviour (e.g. learning from demonstration, characterisation of collaborative behaviours) and for understanding of scenes for mixed-reality scenarios (e.g. adaptation of content, contextual keying).
Pontus Stenetorp is based in the Department of Computer Science and leads the Natural Language Processing group. His research interests lie primarily in the intersection between human language and machine learning, with applications such as machine reading comprehension and information extraction.
Jack Stilgoe works on the governance of new technologies, including AI. He is interested in AI and society, public engagement with AI, responsible AI and AI and inequality. He is a fellow of the Turing Institute.
Emine Yilmaz is a Turing Fellow and Professor at University College London (UCL), Department of Computer Science, as well as an Amazon Scholar at Amazon Cambridge. Between 2012 and 2019, she also worked as a research consultant for Microsoft Research Cambridge, where she used to work as a full time researcher prior to joining UCL. Her research until now has received several awards including a Bloomberg Data Science Research Award in 2018, the Karen Sparck Jones Award in 2015 and the Google Faculty Research Award in 2014. Emine's current research interests include information retrieval systems including search, conversational systems and intelligent assistants, data mining and applications of machine learning. She has published research papers extensively at major venues such as ACM SIGIR, CIKM and WSDM, gave several tutorials as part of top conferences, and organised various workshops. She has served in various roles including PC Chair for ECIR 2019, ACM SIGIR 2018 and ACM ICTIR 2017 Conferences, Practice and Experience Chair for ACM WSDM 2017, and as the Doctoral Consortium Chair for ECIR 2017. She is an elected member of the executive committee of ACM SIGIR. her research interests include:
• Information Retrieval and Natural Language Processing (Search, Conversational Systems and Intelligent Assistants)
• Web Science, User Modelling, Personalisation and Recommendation
• Machine Learning
Fabio Zanasi has research interests in the following areas: Logic, Category Theory, Programming Language Semantics, Concurrency Theory, Foundations of Bayesian Inference
Shi Zhou is interested in application of machine learning and AI methods for social media analysis and cybersecurity. He is in particular interested in the detection of bots and fraudulent activities on online social media networks and the prediction of information spreading in society and cyberspace. A collection of news reports on his recent works on the Star Wars botnet in Twitter and the hybrid epidemic spreading of the Internet worm Conficker and HIV in vivo infection are available at: https://twitter.com/SZ_UCL