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Centre members


Leader(s)

Dr Anne-Sophie Kaloghiros Dr Anne-Sophie Kaloghiros
Email Dr Anne-Sophie Kaloghiros Reader in Maths /Ops
Co-director, Centre for Mathematical and Statistical modelling Algebraic geometry, Birational geometry
Dr Dmitry Savin Dr Dmitry Savin
Email Dr Dmitry Savin Senior Lecturer
Dmitry is a senior lecturer in the Department of Mathematics. His research interests span applied mathematics and mathematical physics, focusing in particular on applications of random matrix theory to scattering and transport in complex quantum or wave systems. He uses statistical methods and analytical techniques to quantify chaotic behaviour in nature. Dmitry also enjoys teaching both applied and abstract subjects, reflecting on their existing and emerging connections to real world examples. Random matrix theory and its applications. Chaotic resonance scattering - quantum / wave chaos in open systems. Interference effects and collective excitations in complex systems. Applied mathematics and mathematical physics; RMT.

Members

Professor George Ghinea Professor George Ghinea
Email Professor George Ghinea Professor - Mulsemedia Computing
I am a Professor in the Department of Computer Science at Ã÷ÐÇ°ËØÔ. I obtained my BSc. Degree with Computer Science and Mathematics majors from the University of the Witwatersrand, South Africa. I later went on to obtain BSc. (Hons.) and MSc. Degrees, both in Computer Science, from the same university. I was awarded my PhD – Quality of Perception: An Essential Facet of Multimedia Communications - from the University of Reading, UK, in 2000. In it, I proposed the Quality of Perception metric, a precursor of the Quality of Experience (QoE) concept now widely known. However, whilst QoE is still a concept, QoP is a concrete metric. Thus, recognising the infotainment duality of multimedia, QoP not only characterises the subjective enjoyment associated with experiencing multimedia presentations, but also how such presentations aid a person\'s ability to assimilate informational content. My research activities lie at the confluence of Computer Science, Media and Psychology. In particular, my work focuses on the area of perceptual multimedia quality and how one builds end-to-end communication systems incorporating user perceptual requirements. I have applied my expertise in areas such as eye-tracking, telemedicine, multi-modal interaction, and ubiquitous and mobile computing. I am particularly interested in building human-centred e-systems, particularly integrating human perceptual requirements. My work has been funded by both national and international funding bodies – all of it being collaborative work with other teams and stakeholders I have been privileged to be involved with. I have also been honoured to supervise 33 PhD students to completion and to have published over 350 high-quality research articles with them and other research collaborators. Currently, my research pursuits are centered on extending the notion of multimedia with that of mulsemedia – a term which I have put forward to denote multiple sensorial media, ie. media applications that go beyond engaging the by now traditional auditory and visual senses, engaging three of our other human in a realistic manner akin to our experiences of everyday life. • Multimedia and multimodal interactive environments• Mulsemedia applications and environments• Adaptive, cross-layer communication systems• Human-centred e-systems• Mobile and pervasive computing• Communications security • Multimedia and multimodal interactive environments • Mulsemedia applications and environments • Adaptive, cross-layer communication systems • Human-centred e-systems • Mobile and pervasive computing • Communications security I currently lead the level 7 postgraduate module Research Project Management.
Professor Stephen Langdon Professor Stephen Langdon
Email Professor Stephen Langdon Associate PVC - Academic Planning & Strategic Projects / Professor - Mathematics
I joined Ã÷ÐÇ°ËØÔ in October 2019, having previously worked at the University of Reading for over fifteen years, the last five as Head of the Department of Mathematics and Statistics. I served as Head of the Department of Mathematics from October 2019 to October 2022, and then as Interim Executive Dean of the College of Engineering, Design and Physical Sciences from November 2022 until May 2024. My research is in the area of Numerical Analysis, particularly the development, analysis and implementation of numerical methods for the solution of partial differential equations, and the application of such schemes to the solution of mathematical models arising from physical or biological processes such as acoustic or electromagnetic scattering, fluid flow, or tumour growth. MA2690 - Professional Development and Project Work
Professor Jane Lawrie Professor Jane Lawrie
Email Professor Jane Lawrie Professor & Deputy Dean - Academic Affairs
Structural acoustics, diffraction theory
Dr Cormac Lucas Dr Cormac Lucas
Email Dr Cormac Lucas Senior Lecturer
Cormac has extensive knowledge of Mathematical Optimisation and Software Tools for (algebraic) Optimisation Modelling. He is also a subject expert in the domains of Stochastic Optimisation, Asset and Liability Management (ALM) and Risk Analytics. He has executed many industrial projects; these include US Coast Guard Cutter Scheduling, an ALM project for Insight Investment, Natural Oil Buying (trading) policy for Unilever amongst others. He has many journal publications and has held an academic position at CARISMA, Ã÷ÐÇ°ËØÔ University, London.
Dr Matthias Maischak Dr Matthias Maischak Elliptic boundary value and Transmission problems. Signorini problems/variational inequalities. Boundary Element and Finite Element Methods. Fast Solvers and Preconditioners. Error estimators and adaptive algorithms. High Performance and Scientific Computing. Software development
Professor Sergey Mikhailov Professor Sergey Mikhailov
Email Professor Sergey Mikhailov Professor - Applied Mathematics and Analysis
Joined Ã÷ÐÇ°ËØÔ in 2006 Applied Analysis, Solid Mechanics, and Computational Mathematics, including: Analysis of Stokes, Oseen, and Navier-Stokes PDEs, especially of existence, uniqueness and regularity of solution of evolution (non-stationary) problems in Sobolev spaces. Boundary-domain integral and integro-differential equations. Theoretical fatigue, damage, durability, and fracture mechanics. Nonlinear partial integro-differential Volterra equations of crack propagation in damaged media. MA2632, Algebra and Analysis MA1608, Elements of Applied Mathgematics
Dr Ben Parker Dr Ben Parker
Email Dr Ben Parker Senior Lecturer in Statistics
Academic with research interests in Design and Analysis of Experiments; Statistics of Networks and related areas. Design of Experiments, particularly optimal design; statistics of networks, specialising in data communications networks and social networks; statistical inference of queues; computer simulation. Computational statistics, particularly algorithms for design. Biostatistics.
Dr Diana Roman Dr Diana Roman
Email Dr Diana Roman Senior Lecturer
Dr Diana Roman is a lecturer in the Department of Mathematics. Her research is in the area of decision making under uncertainty and risk, tackled through the paradigm of stochastic optimisation. This means that the parameters involved in optimisation are not known with certainty, but described by statistical distributions and approximated by discrete distributions, given by possible realisations, called “scenarios”. Optimisation and simulation techniques can be applied to a variety of fields. A major application is financial portfolio optimisation. Key research sub-areas are: risk modelling and minimisation, modelling randomness in asset prices, hedging against downside risk and extreme loss, cash flow matching of assev values and liabilities, finding computational solutions for the resulting optimisation models. Stochastic Programming, Decision Making under Uncertainty and Risk, Financial Optimisation and Simulation, Financial Risk Measures, Asset and Liability Management MA2668 Elements of Investment Mathematics; MA2786 Operations Research; MA1895 Mathematical and Computational Skills; Final year projects
Professor Simon Shaw Professor Simon Shaw
Email Professor Simon Shaw Head of Department- Mathematics /Professor
Simon Shaw is a professor in the Department of Mathematics in the College of Engineering, Design and Physical Sciences, and belongs to the Applied and Numerical Analysis Research Group. He is also a member of the Structural Integrity theme of our Institute of Materials and Manufacturing, and of the Centre for Assessment of Structures and Materials under Extreme Conditions, and of the Centre for Mathematical and Statistical Modelling. Shaw was initially a craft mechanical engineering apprentice but (due to redundancy) left this to study for a mechanical engineering degree. After graduation he became an engineering designer of desktop dental X Ray processing machines, but later returned to higher education to re-train in computational mathematics. His research interests include computational simulation methods for partial differential Volterra equations and, in this and related fields, he has published over thirty research papers. He is currently involved in an interdisciplinary project that is researching the potential for using computational mathematics and machine learning as a noninvasive means of screening for coronary artery disease. Personal home page: Computational Science, Engineering and Mathematics: finite element and related methods. Dispersive media (viscoelasticity and lossy dielectrics); deep neural nets and machine learning. Finite element, and related, methods in space and time for partial differential equations arising in continuum mechanics. Particularly interested in dispersive materials such as polymers and lossy dielectrics for which the constitutive laws exhibit memory effects. Currently interested in using real or (from forward solves) virtual training data to solve inverse problems using machine learning, with a particular focus on deep neural networks. The motivating application for this inverse problem work is in screening for coronary artery disease.
Dr Shash Virmani Dr Shash Virmani
Email Dr Shash Virmani Senior Lecturer
I am a theoretical physicist working in the theory of quantum information and computation. My research to date has covered aspects of entanglement theory, architectures for quantum computing, quantum channel capacities, classical simulation of quantum systems, and the construction of local hidden variable models. For more details on my research, please see my research tab. My research concerns quantum information theory and all things related to the complexity of quantum systems. Preprints to all my publications can be found on the quant-ph arXiv (here) or on Ã÷ÐÇ°ËØÔ's research archives. Here are short summaries of my research work, loosely organised into various themes: Entanglement theory and entanglement measures I started out my scientific career as a graduate student at Imperial College under the supervision of Martin Plenio (now at Ulm) and Peter Knight, working on entanglement theory. Results include statements about the relative ordering of entanglement measures, bounds on the relative entropy of entanglement, and computation of an asymptotic entanglement measure (a paper for which most credit goes to coauthor Koenraad Audenaert for his quite heroic contribution). Since my PhD I have often revisited the topic of entanglement measures with the fortunate assistance of many insightful coauthors, e.g. here and here. Quantum Computation with Triplet/Singlet measurements In a collaboration with Terry Rudolph that seems to become active with approximately the same period as a typical species of cicada, I established the "STPBQP" conjecture of Michael Freedman, Matt Hastings, and Modjtaba Shokrian-Zini. We did this by building upon the insights of their paper which proposed and evidenced the conjecture, and our own other work on a related question from many years ago. The published proof of the conjecture is available at this link. Loosely speaking, the work demonstrates that using only measurements of two qubit total angular momentum, one can perform quantum computation given almost any initial state that is not completely symmetric. This brings natural robustness to a certain form of error, and has interesting fundamental connections to the study of quantum reference frames. It is also perhaps surprising that quantum computation is possible with a single combined dynamical/measurement operation of such a simple and physically natural form, in a way that is almost completely agnostic about the initialisation of the qubits. LOCC discrimination of quantum states I had an early interest in the LOCC discrimination of quantum states (loosely speaking - how to distinguish quantum states of many quantum subsystems when you can only measure the subsystems in a distributed way). In collaboration with various colleagues I showed that two pure states can be optimally discriminated even in the LOCC setting, and obtained bounds on when discrimination is possible given more states, and obtained optimal LOCC protocols in some settings with high symmetry. Correlated error quantum information I was introduced to this topic while I was a postdoc with Chiara Macchiavello at Pavia. We investigated the effect of correlations on the information carrying capacity of two correlated quantum channels. Motivated by some intriguing non-analyticity in that example, together with Martin Plenio I developed connections between correlated error quantum channel capacities and many-body physics, see here and here for details. Classical simulation of quantum systems Motivated by the ever increasing buzz concerning quantum computing, I became interested in how well classical computers can efficiently simulate quantum systems. Together with various coauthors I've developed bounds (e.g. here and here) on the noise that quantum computers can tolerate before losing their advantage over classical computers. In more recent work have shown how ideas from the foundations of physics can be used to develop efficient simulations of some complex quantum systems, even without noise. Perhaps the most surprising example of this arises in certain pure entangled modifications of cluster state quantum computing, which we have shown can be efficiently simulated classically (see also here for more explicit examples). Some of this work was supported by an EPSRC "Bright Ideas" grant and an EPSRC DTP.
Dr Matthias Winter Dr Matthias Winter
Email Dr Matthias Winter Senior Lecturer
PhD Stuttgart 1993, Habilitation Stuttgart 2003; Postdoctoral Research Fellow, Institute for Advanced Ã÷ÐÇ°ËØÔ, Princeton, 1993-94; Postdoctoral Research Fellow, Heriot-Watt University, Edinburgh, 1994-96; Wissenschaftlicher Mitarbeiter/Wissenschaftlicher Assistent, Stuttgart, 1996-2005; Lecturer/Senior Lecturer, Ã÷ÐÇ°ËØÔ, 2005- Mathematical Biology, Pattern Formation, Infectious Diseases. Phase Transitions, Micromagnetics, Microstructure. Nonlinear Partial Differential Equations. Nonlinear Functional Analysis. Calculus of Variations. Dynamical Systems.
Professor Keming Yu Professor Keming Yu Keming Yu – Chair in Statistics Impact champion of REF – in UOA (Mathematical Sciences) Keming joined Ã÷ÐÇ°ËØÔ in 2005. Before that he held posts at various institutions, including University of Plymouth, Lancaster University and the Open University. Keming got his first degree in Mathematics and MSc in Statistics from universities in China and got his PhD in Statistics from The Open University, Milton Keynes. Based on mathematical theory and data analysis methods, my research aims to explore statistical methods, models and optimal algorithms to deal with challenges in: New regression models and methods, including quantile regression, for Financial Econometrics and Business. Robust algorithms for Machine Learnign and Deep Learning. Statistical analysis and Machine learning for modelling loneliness and social isolation in Gerontology. New distributional/regression methods for the analysis of Wellbeing, health and biomedical scoences, such as obesity. Statistics/Machine learning methods for risk assessment in engineering, such as rail truck failure, cable fault, pipeline corrosion and wind turbine. Statistical theory, method, including Bayesian analysis, for the analysis of big data and small data. I teach Level 3 UG Statistics and MSc Statistics Courses. And I supervise final year UG student projects and MSc dissertation. MA3670: Statistics III. MA5632MA5673: Computer Intensive Statistical Methods. MA5629MA5676: Time Series Modelling.
Dr Jiawei Lim Dr Jiawei Lim Lecturer in Financial Mathematics Parisian option pricing, excursion theory, simulation of Levy processes Financial mathematics, applied probability, operations research

Doctoral Researchers

Ms Gargi Roy Ms Gargi Roy
Email Ms Gargi Roy PhD Student
I am Gargi Roy, currently pursuing Ph.D in the department of Mathematics. I have joined the department on January, 2021. I am currently on leave from my job where I was working as a researcher in Tata Consultancy Services (TCS) Ltd. Research and Innovation (India) since Oct, 2014 in the area of Text Mining involving statistical analysis and machine learning techniques. Prior to joining the TCS, I have done MS (by research) from Indian Institute of Technology (IIT) Kharagpur, India in computer science specializing in developing novel digital logic simulation algorithm involving graph theoretic analysis and novel formal methods for e-learning. Before MS, I have done 4 years of Bachelors to Technology (B-Tech) in Information Technology. The developed tool in MS work have been used to conduct under-graduate and post-graduate level laboratory course Computer Organization at IIT Kharagpur since 2012. My research group affiliation is "Statistics & data Science" within the departmet of Mathematics at Ã÷ÐÇ°ËØÔ.
Mr Kam Jipreze Mr Kam Jipreze I am a doctoral researcher in mathematics with two masters degrees in financial mathematics, and business and financial economics. My PhD research focuses on the pricing and hedging of exotic options in a Lévy framework and possibly under stochastic volatility. My research interests include Lévy processes, Stochastic optimal control problems, Information based asset pricing, Portfolio theory and interest rate modeling. My research areas include option pricing, stochastic analysis, volatility modelling, Lévy processes and decisions under uncertainty.
Mr Mykolas Grublys Mr Mykolas Grublys PhD Student at Ã÷ÐÇ°ËØÔ. Research area is in the applications of machine learning to chaotic dynamical systems. Other interests include fractals and computer simulations. Chaos Theory Computer Simulations Dynamical Systems Machine Learning Mathematical Modelling Applied mathematics. Chaotic dynamical systems and machine learning.