Chakrabarty
my d.phil (from st. cross college, oxford) was in theoretical astrophysics, and i was examined by prof. james binney. my doctoral thesis was dedicated to the development of a novel bayesian learning method to learn the gravitational mass of the black hole in the centre of the milky way, (along with the galactic phase space density), and to the computational modelling of non-linear dynamical phenomena in galaxies. thereafter, i continued to develop probabilistic learning methods, and undertake bayesian inference, within astronomical contexts, till 2009, when i moved to warwick statistics, and started developing bayesian methodologies, to apply to diverse areas. after warwick, i was a lecturer in statistics, in leicester maths, and then in loughborough maths. i moved to brunel, department of mathematics, at the beginning of 2020. my current interest is strongly focused on the development of bayesian learning methodologies, given different challenging data situations, such as data that is shaped as a hyper-cuboid; with components that are diversely correlated; absent training data; data that is discontinuously distributed and/or changing with time. i am equally keen on learning graphical models and networks of multivariate datasets, as random geometric graphs, with the ulterior aim of computing distance between a pair of learnt graphs. i am also interested in the development of bayesian tests of hypotheses that are useful when the alternative model is difficult/impossible to perform computation within, and recently, have initiated a method of optimising the mis-specified parameters of a parametric model, while learning the desired model parameters. my current applications include areas such as healthcare, vino-chemistry, astronomy, test theory, material science, etc. my research focuses upon the development of methodologies within computational & mathematical statistics, and i undertake inference primarily using markov chain monte carlo techniques. my reserach interests include: bayesian learning methods — given different data situations, such as high-dimensional data; temporally-evolving; and/or discontinuously distributed data; absent training data; large in size, or under-abundant. this has resulted in --supervised learning methodologies given hypercuboidally-shaped, discontinuous and/or non-stationary data, using compounding of gaussian processes; --pursuit of graphical models & networks of multivariate data, as random graphs, followed by computing distance between learnt graphical models, to inform on the inter-data correlation. --a novel method that allows for variable prediction at test data, (when bearing of functional relation between variables is not possible), by embedding the sought variable vector within support of the state space density. --sometimes, in pursuit of this latterly mentioned prediction without learning, intractability is encountered, and i am interested in developing new tests of hypotheses in which we seek the probability of a simplifying model, conditioned on the data, where said simplification is undertaken to counter the intractability. --have recently developed a 5-step method that optimises the mis-specification parameter vector, in a parametric model, while bayesianly learning the sought model parameters. in addition, i have worked on developing a novel classification technique in lieu of training data, and on another occasion, trained the model for the causal relationship between the observable and covariates, using hierarchical regression. applications of these methods are in astronomy, materials science, chemistry, petrophysics, testing theory, etc. bayesian supervised learning of high-dimensional functions, given high-dimensional, discontinuously distributed data, using the a dual-layered covariance kernel parametrisation-based learning strategy. random geometric graphical models and networks of multivariate datasets, and distance between learnt pair of graphical models. novel bayesian prediction, given test data on the dependent variable, when training data is absent, and distribution of neither variable is known. tests of hypotheses given intractable alternatives. novel optimisation of mis-specification parameters given parametric models, (while learning sought model parameters). supervised learning using hierarchical regression & supervised classification. applications to astrophysics, materials science, chemistry, healthcare, petrophysics, testing theory, etc.
Dr Dalia Chakrabarty
My D.Phil (from St. Cross College, Oxford) was in Theoretical Astrophysics, and I was examined by Prof. James Binney. My doctoral thesis was dedicated to the development of a novel Bayesian learning method to learn the gravitational mass of the black hole in the centre of the Milky Way, (along with the Galactic phase space density), and to the computational modelling of non-linear dynamical phenomena in galaxies. Thereafter, I continued to develop probabilistic learning methods, and undertake Bayesian inference, within astronomical contexts, till 2009, when I moved to Warwick Statistics, and started developing Bayesian methodologies, to apply to diverse areas. After Warwick, I was a Lecturer in Statistics, in Leicester Maths, and then in Loughborough Maths. I moved to Brunel, Department of Mathematics, at the beginning of 2020. My current interest is strongly focused on the development of Bayesian learning methodologies, given different challenging data situations, such as data that is shaped as a hyper-cuboid; with components that are diversely correlated; absent training data; data that is discontinuously distributed and/or changing with time. I am equally keen on learning graphical models and networks of multivariate datasets, as random geometric graphs, with the ulterior aim of computing distance between a pair of learnt graphs. I am also interested in the development of Bayesian tests of hypotheses that are useful when the alternative model is difficult/impossible to perform computation within, and recently, have initiated a method of optimising the mis-specified parameters of a parametric model, while learning the desired model parameters. My current applications include areas such as healthcare, vino-chemistry, astronomy, test theory, material science, etc. My research focuses upon the development of methodologies within Computational & Mathematical Statistics, and I undertake inference primarily using Markov Chain Monte Carlo techniques. My reserach interests include: Bayesian learning methods — given different data situations, such as high-dimensional data; temporally-evolving; and/or discontinuously distributed data; absent training data; large in size, or under-abundant. This has resulted in --supervised learning methodologies given hypercuboidally-shaped, discontinuous and/or non-stationary data, using compounding of Gaussian Processes; --pursuit of graphical models & networks of multivariate data, as random graphs, followed by computing distance between learnt graphical models, to inform on the inter-data correlation. --a novel method that allows for variable prediction at test data, (when bearing of functional relation between variables is not possible), by embedding the sought variable vector within support of the state space density. --Sometimes, in pursuit of this latterly mentioned prediction without learning, intractability is encountered, and I am interested in developing new tests of hypotheses in which we seek the probability of a simplifying model, conditioned on the data, where said simplification is undertaken to counter the intractability. --Have recently developed a 5-step method that optimises the mis-specification parameter vector, in a parametric model, while Bayesianly learning the sought model parameters. In addition, I have worked on developing a novel classification technique in lieu of training data, and on another occasion, trained the model for the causal relationship between the observable and covariates, using hierarchical regression. Applications of these methods are in Astronomy, Materials Science, Chemistry, Petrophysics, Testing theory, etc. Bayesian Supervised learning of high-dimensional functions, given high-dimensional, discontinuously distributed data, using the a dual-layered covariance kernel parametrisation-based learning strategy. Random Geometric Graphical models and networks of multivariate datasets, and distance between learnt pair of graphical models. Novel Bayesian prediction, given test data on the dependent variable, when training data is absent, and distribution of neither variable is known. Tests of hypotheses given intractable alternatives. Novel optimisation of mis-specification parameters given parametric models, (while learning sought model parameters). Supervised learning using hierarchical regression & supervised classification. Applications to Astrophysics, Materials Science, Chemistry, Healthcare, Petrophysics, Testing Theory, etc.
Ghinea
i am a professor in the department of computer science at brunel university london. 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 George Ghinea
I am a Professor in the Department of Computer Science at Brunel University London. 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.
Langdon
i joined brunel university london 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. 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 Stephen Langdon
I joined Brunel University London 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. 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
Lawrie
structural acoustics, diffraction theory
Lucas
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, brunel university, london.
Dr Cormac Lucas
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, Brunel University, London.
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
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
Mikhailov
joined brunel university london 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
Professor Sergey Mikhailov
Joined Brunel University London 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
Parker
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 Ben Parker
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.
Roman
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
Dr Diana Roman
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
Shaw
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.
Professor Simon Shaw
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.
Virmani
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. *******funded phd studentship available to start in 2024! application deadline at the beginning of april. see my personal research tab above for details******** *** funded phd studentship available in 2024 to work on topics in quantum information/computation theory. deadline beginning of april 2024. international applicants are eligible, although the process is more competitive. to apply you must do two things: (i) follow the university's general two-stage application process here, (ii) before april email me a cv and transcript (you may be separately asked to do this by the university as part of (i), but even if that happens you *must* send them to me too). *** 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 brunel'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 Shash Virmani
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. *******Funded PhD studentship available to start in 2024! Application deadline at the beginning of April. See my personal research tab above for details******** *** Funded PhD Studentship available in 2024 to work on topics in quantum information/computation theory. Deadline beginning of April 2024. International applicants are eligible, although the process is more competitive. To apply you must do two things: (i) follow the university's general two-stage application process here, (ii) before April email me a CV and transcript (you may be separately asked to do this by the university as part of (i), but even if that happens you *must* send them to me too). *** 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 Brunel'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.
Winter
phd stuttgart 1993, habilitation stuttgart 2003; postdoctoral research fellow, institute for advanced study, princeton, 1993-94; postdoctoral research fellow, heriot-watt university, edinburgh, 1994-96; wissenschaftlicher mitarbeiter/wissenschaftlicher assistent, stuttgart, 1996-2005; lecturer/senior lecturer, brunel, 2005- mathematical biology, pattern formation, infectious diseases. phase transitions, micromagnetics, microstructure. nonlinear partial differential equations. nonlinear functional analysis. calculus of variations. dynamical systems.
Dr Matthias Winter
PhD Stuttgart 1993, Habilitation Stuttgart 2003; Postdoctoral Research Fellow, Institute for Advanced Study, Princeton, 1993-94; Postdoctoral Research Fellow, Heriot-Watt University, Edinburgh, 1994-96; Wissenschaftlicher Mitarbeiter/Wissenschaftlicher Assistent, Stuttgart, 1996-2005; Lecturer/Senior Lecturer, Brunel, 2005- Mathematical Biology, Pattern Formation, Infectious Diseases. Phase Transitions, Micromagnetics, Microstructure. Nonlinear Partial Differential Equations. Nonlinear Functional Analysis. Calculus of Variations. Dynamical Systems.
Yu
keming yu – chair in statistics research director (impact) – in mathematical sciences keming joined brunel university london 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.
Professor Keming Yu
Keming Yu – Chair in Statistics Research Director (Impact) – in Mathematical Sciences Keming joined Brunel University London 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.
Lim
lecturer in financial mathematics parisian option pricing, excursion theory, simulation of levy processes financial mathematics, applied probability, operations research
Dr Jiawei Lim
Lecturer in Financial Mathematics Parisian option pricing, excursion theory, simulation of Levy processes Financial mathematics, applied probability, operations research