Academics and Staff

Director
 
Dr. Niki Trigoni

Professor Niki Trigoni

Localisation, communication and coordination protocols for networked sensors; applications in indoor localisation, animal tracking, road traffic monitoring, and sensing industrial processes.

Co-Director
 
Professor Michael Osborne

Professor Michael Osborne

Probabilistic machine learning; Bayesian statistics; active learning; Gaussian processes; Bayesian optimisation; Bayesian quadrature; changepoint, fault and anomaly detection; applications to sensor networks, astrostatistics and labour economics.

Academics
 
Professor Stephen Roberts

Professor Stephen Roberts

Machine learning approaches to data analysis; Bayesian statistics; signal analysis; information theory; applications in astronomy, mathematical biology, finance and sensor networks.

Dr. Alessandro Abate

Dr. Alessandro Abate

Correct-by-design symbolic controller synthesis; Formal and robust synthesis; Safety, liveness and reachability; Automated quantitative verification; Formal methods; Stochastic optimal control; Probabilistic and hybrid models; Abstractions and equivalences; Simulations and bisimulations; Applications in safety-critical systems (autonomy, energy, power), and in the life sciences (systems and synthetic biology).

Dr. Phil Blunsom

Dr. Phil Blunsom

Machine learning approaches to natural language processing; Bayesian non-parametrics and graphical models; deep learning; machine translation; parsing and grammar induction.

Professor Daniel Kroening

Professor Daniel Kroening

Automated verification, testing and certification of systems code; model-based design; hardware/software co-verification; program analysis; decision procedures.

Professor Marta Kwiatkowska

Professor Marta Kwiatkowska

Model checking, automated quantitative verification, correct-by-construction controller synthesis, probabilistic and real-time systems, safety and energy efficiency, applications to medical devices and automotive systems.

Dr. Ivan Martinovic

Dr. Ivan Martinovic

Security of mobile systems; wireless network security; applied cryptography; authentication protocols; physical-layer security; sensor network security.

Professor David Murray

Professor David Murray

Computational vision, particularly in the areas of detection and tracking of moving objects, and structure recovery from calibrated and partially calibrated imagery. Applications for surveillance, wearable and assistive computing, cognitive vision, augmented reality, human motion analysis, teleoperation, and navigation.

Professor Paul Newman

Professor Paul Newman

Robotics, navigation and mapping using machine vision and active sensing. Self driving cars, logistics, inspection, space robotics, off roading, autonomous inspection.

Dr. Antonis Papachristodoulou

Dr. Antonis Papachristodoulou

Modern control theory, robust stability analysis and design, nonlinear dynamical systems, Lyapunov stability, convex optimization, sum of squares programming, multi-agent systems, consensus, flocking, alignment, synchronization of oscillator networks, Internet congestion control, aerospace systems and flow control.

Dr. Ingmar Posner

Dr. Ingmar Posner

Machine learning approaches to robot perception and learning, robust planning and decision making, closing the action-perception loop, learning from demonstration; current application areas include transport (self-driving cars and beyond), infrastructure monitoring, space robotics, off-roading and intelligent energy management.

Professor Bill Roscoe

Professor Bill Roscoe

Verification and security. Verification is crucial in autonomous systems because so many aspects of them are safety critical. Similarly we need to prevent autonomous systems being hacked. Bill is interested in the verification of embedded software and the security of ad hoc networks.

Professor Andrea Vedaldi

Professor Andrea Vedaldi

Image understanding, object category detection and recognition, visual features, image matching and search; discriminative learning and kernel methods and large scale optimisation applied to computer vision problems.

Dr. Frank Wood

Dr. Frank Wood

Machine learning, probabilistic programming, Bayesian non-parametrics, unsupervised learning, artificial intelligence.

Professor Michael Wooldridge

Professor Michael Wooldridge

Autonomous agent programming languages & environments; cooperation and coordination in multi-agent systems; strategic reasoning in multi-agent systems; game theoretic analysis of multi-agent systems.

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Professor Andrew Zisserman

Computer vision and machine learning for visual recognition and search; applications in retrieval and analysis of large scale image and video collections, botany, zoology, art history.

Torr

Professor Phil Torr

Computer Vision, augmented reality, SLAM, scene understanding, energy models, image segmentation and recognition.

Professor Paul Goulart

Professor Paul Goulart

Optimization methods for embedded systems; robust and stochastic optimisation; distributional robustness; model predictive control; nonlinear systems; applications in fluid flows, flight control, energy systems, and finance.

Andrew-Markham

Dr. Andrew Markham

Sensor systems; adaptive and low power embedded systems; wireless sensor networks; localization. Applications in structural health monitoring; industrial process control and safety; wildlife monitoring.

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Professor Stephen Duncan

Applications of control: synchrotrons; electrical storage; battery management; manufacturing systems. Modelling and system identification. Process monitoring and fault detection: power grid; railway systems.

Whiteson

Professor Shimon Whiteson

Reinforcement learning, decision-theoretic planning, multi-agent coordination, multi-objective planning and learning, active perception, applications in robotics and information retrieval.

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Professor Alex Rogers

Artificial intelligence, coordination algorithms and embedded machine learning for sensor networks; applications in future energy systems and environmental monitoring.

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Professor Pawan Mudigonda

Discrete optimization, particularly energy minimization for graphical models in computer vision and related areas; machine learning, particularly estimation of graphical models from large-scale weakly supervised datasets; continuous optimization, particularly problems that arise in parameter estimation.

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Mihaela van der Schaar

Machine Learning for Medicine, Machine Learning for a Better Planet, Quantitative Finance, Data Science and Decisions, Game Theory

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Anthony Lin

My current research interests lie in the field of constraint solving, automated reasoning, program analysis, and formal verification, especially motivated by problems in (i) web security, (ii) web performance optimisation, (iii) analysis of concurrent programs with many processes, and (iv) graph databases.

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Maurice Fallon

Robotics. Specifically state estimation, localization and Mapping. Applications include visual navigation, legged robots, disaster response and manipulation.

Kostas

Dr Kostas Margellos

Control and optimization in complex systems: Distributed control in networked, multi-agent systems; Statistical learning theory and randomized optimization; Game theory; Energy management in power systems, e.g., energy efficient building control, optimal charging of electric vehicles; Shared mobility systems.

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Dr Ioannis Havoutis

Dynamic whole-body motion planning and control with machine learning, focusing on robots with arms and legs.

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Dr Jan-Peter Calliess

Jan-Peter Calliess is a Senior Research Fellow at the Oxford-Man Institute of Quantitative Finance. His interests include a wide range of topics pertaining to decision-making and computational learning. He has published in various communities related to artificial intelligence, including papers on machine learning, control, signal processing, optimisation and multi-agent systems. Apart from working on new learning algorithms and their theoretical understanding, he is also currently investigating applications of machine learning to finance, control and economic dynamical systems.

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Yarin Gal

My interests lie in the fields of linguistics, applied maths, and computer science. Most of my work is motivated by problems found in the intersections of these fields, with a major theme being understanding empirically developed machine learning techniques. At the moment I develop Bayesian techniques for deep learning. In the past I worked on Bayesian modelling, Gaussian processes and BNP, approximate inference, natural language processing, reinforcement learning, and much more.

Dong

Xiaowen Dong

My research focuses on two interwined aspects: theoretically, how emerging signal processing and machine learning techniques on graphs can help us decode the complexity of and structure behind data observations; and practically, how this can, together with the increasing amount of information being collected about ourselves, enable us to better understand the role physical environment and social network play in shaping human and social behavior as well as transforming modern cities and societies.

Administration
 
Ms Wendy Adams

Ms Wendy Adams

CDT Administrator