Supervisors

In addition to the supervisors listed here, you could contact any member of the Department of Mathematics whose research area interests you.

Barnett, Dr Ryan

Keywords: 
Theoretical Physics, Quantum Superfluids, Topological Insulators

Research Area: 
Condensed Matter Theory, Quantum Mechanics

Battey, Dr Heather

Keywords:
Statistical theory and applied probability motivated by the medical and physical sciences. Particularly: conditional and high-dimensional inference; inferential separation and evasion of nuisance parameters; comparisons and contradictions between alternative modes of inference; probabilistic behaviour of eigenvectors and eigenvalues of structured random matrices.

Research Area:
Much of my research is about calibrated inference for key quantities of interest, like the effect of a drug or treatment, in the presence of a large number of nuisance parameters. The latter are aspects of no direct subject-matter concern, but that are needed to complete the idealised representation of the physical, biological or sociological system. Large numbers of them arise naturally when one wishes to limit the strength of modelling assumptions in the equations describing the data generating process. 

Bertrand, Dr Thibault

Keywords: 
Non-equilibrium statistical mechanics, stochastic dynamics in life sciences, biomathematics, theoretical soft matter physics, fluid dynamics

Research Area: 
Our research group is interested in understanding collective and emergent behaviours in out-of-equilibrium and disordered systems. Employing analytical and computational tools from non-equilibrium statistical mechanics, soft condensed matter physics and stochastic processes, our work finds applications in a variety of settings in physical, life and social sciences. Examples include statistical mechanics of active matter, collective dynamics in tissues, jamming in disordered systems, random walks in complex environments, stochastic models of opinion formation, swelling and drying dynamics of gels. Combining theory and modelling, we try to work closely with experimentalists.

Bodenham, Dr Dean

Keywords:
Changepoint detection; two-sample testing; missing data; outlier detection

Research Area:
My research is primarily focused on the areas of two-sample testing and changepoint detection. I am interested in developing methods that are both nonparametric and computationally efficient, and so can be deployed on large-scale datasets or in a streaming data scenario. A recent interest is how missing data affects these procedures and other statistical analyses. A related technique is outlier detection, sometimes called anomaly detection, which involves identifying any anomalous values within a dataset. Several real-world applications for these topics include finance, cybersecurity, meteorology and biology, but the main motivation is to develop methods that can be deployed for medical applications.  

Buzzard, Prof. Kevin

Keywords: 
Formal proof verification, Lean theorem prover

Research Area: 
Me and a small but growing team of mathematicians and computer scientists from across the world are developing a mathematical database of computer-checked theorems using a computer program called the Lean Formal Proof Verification System.
To get a feeling for what it's like try the natural number game. I am particularly interested in formalising algebra, algebraic geometry and number theory. We are gamifying mathematics. 

Cheraghi, Dr Davoud

Keywords:
Complex Analysis, Holomorphic Dynamics, Mandelbrot and Julia sets, Renormalisation

Research Area:
I am interested in the interface of analysis, geometry, and combinatorics. Specifically, I work in the area of the dynamics of holomorphic maps, related to the Mandelbrot, Julia and Fatou sets. In general one aims to explain the orbits of points generate by successively applying a holomorphic map. Explaining come in many forms, such as topological, geometric, or probabilistic features of orbits. There is a wide range of interesting problems, and techniques, such as renormalisation methods employed in the area.

Cohen, Dr Ed

Keywords: 
Statistical signal and image processing, event data, point processes, bio-imaging, network monitoring

Research Area: 
I am interested in developing new statistical methods for analysing signals and images, with a particular interest in event data (temporal, spatial or spatio-temporal). I am motivated by questions arising in engineering and the natural sciences, for example, detecting if a pair of neurons are firing coherently, looking for anomalous correlations in computer networks, or determining the distribution of molecules on 3D cell membranes.

Cotter, Prof. Colin

Keywords: 
Numerical analysis, scientific computing, numerical weather prediction, finite element methods, time parallel algorithms, data assimilation.

Research Area: 
I like to run research projects on: a) numerical methods for partial differential equations, especially finite element and discontinuous Galerkin methods, with diverse applications in fluids, energy, weather and climate, manufacturing, engineering etc. I’m particularly interested in the development of solution algorithms for these methods that can make full use of parallel computers. b) optimisation, data assimilation and inverse problems. c) Model reduction – finding ways to make computational models cheaper so that we can use them efficiently to make decisions. In all of these projects I like to build implementations using Firedrake (firedrakeproject.org) which is a system that allows us to work at the level of the maths and algorithm instead of getting too bogged down in implementation details, so we can work on more interesting problems.

Craster, Prof. Richard 

Keywords: 
Metamaterials, Waves, Acoustics, Scientific Computation

Research Area: 
We aim to model, design and understand the next generation of designer materials and surfaces. In particular we study how to send light, or sound, to places of our choosing using small devices. Our research uses pieces of pure mathematics (group theory and topology), applied mathematics and scientific computation (modelling, efficient numerical simulation) to generate fast and accurate results.  There are many real-world applications to cloaking, vibration control, energy harvesting, soundproofing and antennas.  

Ham, Prof. David

Keywords: 
Finite Element, Code Generation, Differentiable Programming, Scientific Software.

Research Area:
I work on composable, differentiable systems for numerical simulation of physical systems using the finite element method. The key idea is the creation of software that combines symbolic reasoning with high performance computing so that users can combine any equation with any finite element discretisation and any linear or nonlinear solver. Applications include ocean and ice simulation, convection in the Earth’s mantle, weather forecasting and many others.

Heard, Prof. Nick

Keywords: 
Dynamic networks, Bayesian changepoint analysis,  Bayesian cluster analysis, meta-analysis, statistical cyber-security.

Research Area:
My main research interests are in finding structure in large, dynamic graphs such as enterprise computer networks, typically through latent factor models.  Inference tasks include identifying clusters of similar nodes and performing outlier/anomaly detection. These ideas have clear applications in cyber-security contexts but can also be applied in a wide variety of other domains. I’m also interested in finding temporal changes in network data streams, using Bayesian changepoint models or anomaly detection techniques. I also have interests in meta-analysis, combining evidence from typically weak signals from a large number of data streams to assess overall significance levels; this is also of direct importance in cyber-security problems.

Holm, Prof. Darryl

Keywords: 
Geometric Mechanics, Applied Mathematics, Geophysical Fluid Dynamics, Shape Analysis, Integrable Systems
 
Research Area: 
Stochastic PDEs for GFD and Nonlinear waves

Jacquier, Prof. Jack  

Keywords: 
Data modelling, stochastic analysis, quantum computing, deep learning

Research Area: 
I am interested in developing algorithms and models to capture the random (some would say erratic) behaviour of financial markets. The tools that I am using range from stochastic analysis to machine learning and quantum computing. Many exciting projects can be developed either theoretically or numerically, depending on students’ preferences.

Joergensen, Dr Andreas

Keywords:

Monte Carlo methods, particle filters, Optimisation and Control

Research Area: 
I work on a variety of problems and numerical methods for challenging inference or optimisation. Some recent work is on parameter estimation in large scale agent-based models, estimation and control for Hidden Markov models, control and inference of interacting stochastic differential equations.

Karin, Dr Omer

Keywords: Systems Biology; Control and computation in biological systems; Emergent behaviors in biological networks and their contribution to adaptive function; Mathematical medicine/physiology; Epigenetics, stem cells, and cell fate dynamics.

Research interests: I develop mathematical and computational approaches to understand biological regulation, with a specific emphasis on the dynamics of cells and cell fate behavior. This includes applying modelling approaches to understand how biological mechanisms work, and developing mathematical and computational approaches to understand how these mechanisms contribute to adaptive function.

Keaveny, Dr Eric

Keywords: 
Biofluid dynamics, microhydrodynamics, mathematical biology, computational dynamical systems and simulation

Research Area: 
Our current research direction has been focused on understanding the mechanical underpinnings of elastic biofilament-motor protein complexes that cells utilise to drive fluid flows, both internally and externally.  We have developed a bespoke set of codes to perform simulations of these systems and assess bifurcations that occur in their models.  The project will involve using these codes to explore different regions of parameter space, as well as employ techniques from computational dynamical systems to find different time periodic solutions and assess their stability through Floquet analysis.

Kestner, Dr Charlotte

Keywords: 
Logic, Model theory, Geometric stability theory

Research Area: 
Geometric stability theory aims to classify mathematical structures, according to logical properties, to create a “geography of mathematical structures." The idea is to show that whole groups of seemingly different mathematical structures share certain basic properties; usually combinatorial properties of sets defined by formulas in the chosen first order language. The real strength of modern model theory is in using what is essentially a combinatorial geography to reach geometric conclusions about areas of the geography (for example the existence of dimension). Research summer students would get the chance to learn about a specific area of this classification. The ultimate aim of a project would be to prove something new about either a specific area or show where a specific structure fits into the geography.

Lawn, Dr Marie-Amelie

Keywords: 
Differential geometry, Spin geometry, Submanifold theory, Geometric flows

Research Area: 
My working group is particularly interested in the study of homogeneous spaces, special geometries and G-structures. 

Longo, Dr Iacopo

Keywords: 
Nonautonomous dynamical systems, bifurcation theory, critical transitions

Research Area: 
The terms "tipping points" and "critical transitions" have become alarmingly prevalent in recent years, reflecting the rapid and unprecedented changes our world is undergoing. However, current mathematical methodologies are primarily grounded in the assumption of a slowly changing environment, as is the case in autonomous dynamical systems theory and statistical mechanics, and do not allow us to fully understand these critical phenomena and their implications. My research focuses on the mathematical underpinning to critical transitions via nonautonomous dynamical systems and bifurcation theory, with the objective of providing a rigorous characterisation, classification and prediction of such phenomena. 

Luati, Prof. Alessandra

Keywords: 
Time series; theoretical statistics; score-driven models.

Research Area: 
My research interests are mainly focused on time series models. In particular, I am working on non-linear models for time-varying parameters of conditional densities. According to the parameter or quantity of interest (e.g. mean, scale, quantiles), to the specific research problem addressed (e.g. signal extraction, volatility forecasting, conditional density estimation) and to the data at hand (univariate, multivariate, high-dimensional, spatio-temporal data) different problems arise. The focus of the project may thus be concerned with: optimal filtering of non-Gaussian models; robust filtering; asymptotic theory under non-standard assumptions; stochastic properties of dynamic models; quasi score-driven models.

Monod, Dr Anthea

Keywords: 
Topological data analysis; Algebraic statistics; Biomathematics; Applied algebraic geometry; Tropical Geometry

Research Area: 
I am a mathematical data scientist; I study random algebraic structures and randomness in algebraic settings.  I leverage theory from algebraic topology and algebraic geometry to develop methodology to handle complex data structures.  I have applied my methods in real biological settings.

Olver, Dr Sheehan

Keywords: 
Spectral methods, orthogonal polynomials, differential equations, singular integral equations, Riemann–Hilbert problems, random matrix theory, applied complex analysis

Research Area: 
I work on spectral methods for solving differential equations, singular integral equations, and problems in applied complex analysis. These methods use orthogonal polynomials to construct sparse discretisations that are efficiently solvable even when there are millions of degrees of freedom. Lately, I’m particularly interested in constructing new families of orthogonal polynomials on algebraic curves and surfaces in 2D and 3D for solving partial differential equations on complicated geometries, and in equations involving fractional Laplacians. 

Papageorgiou, Prof. Demetrios 

Keywords: 
Fluid dynamics, free-boundary problems, nonlinear waves, nonlinear PDEs, scientific computing

Research Area: 
I am interested in the study of partial differential equations (PDEs) arising from physical problems and in particular multi fluid flows that support waves at liquid-air and liquid-liquid interfaces. Such mathematical problems known as free-boundary problems since the wave shape evolves spatiotemporally and must be determined as part of the complete solution (whether analytically, computationally or a combination of the two). I study such problems “holistically”, i.e. starting from their physical origin using mathematical modelling to extract reduced-dimension systems, to the analytical and computational study of such systems, and where possible use mathematical structures to compare with experiments. There is a wide spectrum of research problems that emerge with applications to many modern technologies such as superhydrophobic surfaces and propulsion and mixing on the micro-scale.
Mathematically issues that emerge include, for example, spatiotemporal chaos and its control, nonlinear waves and coherent structures, and singularity formation and blow-up in PDEs; these are addressed using tools from asymptotic analysis, applied dynamical systems, numerical analysis and scientific computation.

Papatsouma, Dr Ioanna

Keywords:
Clustering, mixed-type data, robust clustering, clustering validation

Research Area:
My research interests lie broadly in the area of clustering. Many statistical applications are carried out on mixed-type data, that is, data consisting of both continuous and categorical variables. In medical research, for instance, data sets typically include gender, and smoking status (usually categorical variables) together with other measures, such as blood pressure and blood glucose (usually continuous variables). Such heterogeneity demands ways to guide researchers and practitioners in choosing appropriate clustering approaches that will identify distinct groups of individuals and generate hypotheses. Common problems in clustering mixed-type data concern the choice of validation indices for estimating the quality of partitions produced by clustering algorithms and for determining the number of clusters, the use of appropriate distances for mixed-type data, and complexities introduced by high dimensionality, noise, outliers and missing values.

Passeggeri, Dr Riccardo

Keywords:
probability, mathematical statistics.

Research Area:
My research spans various topics in probability and mathematical statistics, including random measures, Bayesian analysis, extreme value theory, robust statistics, and stochastic analysis. While my work is primarily centred on theoretical questions, I also have an interest in applications, particularly in economics. That said, I am open to supervising students who bring their own ideas, provided they align with areas such as probability, statistics, analysis, or machine learning.

Pavliotis, Prof. Grigorios

Keywords: 
Stochastic differential equations, statistical mechanics, Markov Chain Monte Carlo, agent based modelling, optimal control for PDEs

Research Area: 
Mathematical statistical mechanics, computational statistical mechanics, analysis of algorithms for sampling and for optimisation, analysis of dynamical models exhibiting phase transitions, mathematical modelling in the social sciences, in particular models for opinion formation, optimal control for linear and nonlinear Fokker-Planck equations, numerical methods for linear and nonlinear Fokker-Planck equations.

Pike-Burke, Dr Ciara

Keywords: 
Statistical machine learning, sequential decision making, reinforcement learning, multi-armed bandits

Research Area: 
My research is in the field of statistical machine learning with a focus on sequential decision-making problems and online learning. I am interested in problems where we receive data sequentially and use this data to learn to make better decisions. Some examples of these problems are multi-armed bandit and reinforcement learning problems which arise naturally in many settings such as web-advertising, product recommendation or healthcare.

Pruessner, Dr Gunnar

Keywords: 
Active matter, field theory, entropy production, biological physics

Research Area:
Active matter is a collection of entities that transform some form of fuel from the environment into mechanical action. Typically this action is self-propulsion, but it can be more subtle. The resulting phenomena are often strikingly different to what we are used to from equilibrium. In our group we use field theoretic methods to characterise such active matter. While somewhat cumbersome to get started with, once a phenomenon has been cast in the language of field theory, it can be investigated perturbatively in a systematic way, using diagrams to help our intuition and our calculations. We often use numerics to guide our research. Applications range from fundamental technical questions to morphogenesis and flocking and swarming.

Rasmussen, Prof. Martin

Key words: 
Nonautonomous and random dynamical systems, bifurcation theory
 
Research Area:  
In contrast to classical dynamical systems, the time evolution of a nonautonomous or random dynamical system is influenced by an independent process (which reflects either changes in the rules governing the system or randomness available in the system). The importance of nonautonomous and random dynamical systems is illustrated by the fact that a significant number of real-world applications, ranging from climate, ecology to finance, are governed by time-dependent inputs or subjected to random perturbations, and the traditional mathematical theory fails to address dynamical changes in these contexts. My research group develops tools to understand qualitative properties of these systems, with main focus on bifurcation theory, in order to understand and characterise changes in the behaviour of the system when external parameters are varied.

Ratmann, Dr Oliver & Blenkinsop, Dr Alexandra

Keywords:
Applied Bayesian statistics, machine learning, public health, phylogenetics, infectious diseases

Research area:
The work in my group focuses on applied Bayesian modelling for Public Good. We develop flexible, robust, and computationally scalable models and methods, and apply these tools in applied fields of phylogenetics, infectious disease dynamics, and social science. I am particularly interested in novel approaches that harness information in viral deep sequence data, mobile phone mobility data, and time-resolved patient data to characterise the spread of infectious diseases, and to guide public health interventions. 

Rodriguez, Dr Pierre-Francois

Keywords:
Probability, Analysis, Critical Phenomena, Renormalization

Research Area:
I work on various problems involving randomness, often concerning models (for instance: percolation) that display phase transitions. For many of these systems, physicists predict a rich phenomenology near the critical point, involving random fractal geometric objects that exhibit a universal scaling behavior.

Salvi, Dr Cristopher

Keywords:
Rough Analysis, Deep Learning, Kernel Methods, Signal Processing.

Research Area:
My research interests are in the areas of rough path theory, deep learning, kernel methods and signal processing. Generally speaking, I develop machine learning algorithms for processing irregular, high-dimensional time series in continuous time. I am also interested in the interplay between neural networks and differential equations.

Sanna Passino, Dr Francesco

Keywords:
dynamic networks, clustering, statistical cyber-security

Research Area: 
My main research interests are broadly based on statistical analysis of dynamic networks. In my work, I enjoy exploring an array of different statistical techniques, adapted and extended to dynamic network modelling, such as latent variable models and model-based clustering. In recent years, I have also developed an interest for statistical analysis of event-time data, topic modelling, Bayesian non-parametric methods, and recommender systems. My research has been mainly applied to statistical cyber-security problems, but also to social networks, music streaming services, and bike sharing systems.

Schedler, Prof. Travis

Keywords: 
Interface of algebra, geometry, physics, Poisson and symplectic geometry, moduli spaces, homology, deformation theory.

Research Area: 
I work in various subjects in the interface of algebra, geometry, and physics, specifically: representations of groups and algebras, Poisson and symplectic geometry, moduli spaces, homology and deformation theory.

Schnitzer, Dr Ory

Keywords: 
Asymptotic Analysis, Singular Perturbation Theory, Low-Reynolds Number Hydrodynamics, Transport Phenomena, Wave Motion.

Research Area: 
I use mathematical modelling and asymptotic methods to illuminate fundamental problems in fluid dynamics, transport phenomena and wave motion. A current focus is problems where the coupling between inertialess (small-scale) flows and other physics gives rise to instabilities and spontaneous symmetry-breaking dynamics. In particular, I'm currently studying the spontaneous mobility of levitated Leidenfrost drops, the memory-mediated spontaneous dynamics of chemically active drops and the electrohydrodynamic Quincke rotation of droplets in strong electric fields.

Sivek, Dr Steven

Keywords: 
Low-dimensional topology, knots, 3-manifolds, gauge theory, Floer homology, Khovanov homology.

Research Area: 
I study low-dimensional topology, especially knots and 3-manifolds, from the perspective of gauge theory, Floer homology, and related invariants.

Taylor, Dr Martin

Keywords: 
General relativity, black holes, kinetic theory, geometric analysis, partial differential equations.

Research Area: 
I am interested in understanding the asymptotic behaviour of solutions of certain geometric hyperbolic and kinetic equations arising in mathematical physics, such as the Einstein equations of general relativity and the Boltzmann and Vlasov--Maxwell equations of kinetic theory.  One current topic of very active research is the famous black hole stability problem in general relativity.  A common theme in such problems is to exploit a quantitative understanding of geometric properties of the equations in order to prove analytic results.  Research students will have the chance gain experience with the cutting-edge techniques currently being employed to address these types of questions.

Thomas, Prof. Richard

Keywords: 
Algebraic geometry, moduli problems, derived categories, mirror symmetry

Research Area: 
I work in bits of algebraic geometry related to theoretical physics, such as enumerative algebraic geometry, mirror symmetry, and categories of branes.

Thottungal Valapu, Dr Raziman

(Prof. Richard Craster co-supervising)

Keywords: 
Space-time crystals; synthetic motion; time-varying phenomena, electromagnetism, metamaterials

Research Area: 
Electromagnetic waves propagating through media whose properties change with time give rise to unusual physical phenomena. Effects such as breakdown of energy conservation and reciprocity arise immediately. When the waves are modulated in space and time, synthetic motion can arise, which is unconstrained by relativity. This permits the analog simulation of inaccessible and impossible phenomena such as Hawking radiation and interaction with faster-than-light particles. I study how these effects can occur in realistic materials.

Veneziale, Dr Sara

(Dr Anthea Monod co-supervising)

Keywords: 
machine learning, artificial intelligence, algebraic geometry, combinatorics

Research Area: 

My research is focused on the interplay between machine learning and pure mathematics. My research so far has focused on using machine learning techniques to study objects from algebraic geometry and combinatorics to guide the discovery of new results. I am interested in both using machine learning to advance pure mathematics and in using ideas from geometry to understand the mathematical foundations of AI. 

Zatorska, Dr Ewelina

Keywords: 
Applied Analysis, Partial Differential Equations, Collective Behaviour, Fluid Mechanics 

Research Area: 
I am interested in understanding how emergence of swarms, traffic jams, consensuses, or opinions can be described using macroscopic models. This is done through analysis of systems of partial differential equations describing interacting groups of individuals macroscopically. These system are similar in their structure to classical equations of Mathematical Fluid Mechanics (compressible Euler and Navier-Stokes equations) but they also include terms corresponding to specific interactions between the agents like avoidance of collisions, or ability to choose the optimal route to reach a target.

Benning, Prof. Martin

Keywords: 
Inverse problems, Regularisation theory, Machine learning, Image processing, Imaging, Optimisation.

Research Area: 
My research interests revolve around the theoretical and computational aspects of inverse and ill-posed problems. These problems involve deducing unknown quantities—like imaging the interior of a human body—from indirect observations, which is achieved by inverting a mathematical operator. This inversion process is typically unstable, especially when faced with measurement errors. To counter this, the inverses can be approximated through a series of continuous operators, known as regularisation operators. My research primarily focuses on exploring and implementing both model-based and data-driven regularisation methods. This involves a profound understanding of nonlinear regularisation theory coupled with machine learning. My interdisciplinary approach integrates various fields such as numerical analysis, optimisation, deep learning, functional analysis, compressed sensing, and data analytics, to address these complex problems. My application areas of interest include but are not limited to Magnetic Resonance Imaging, Positron Emission Tomography, Transmission Electron Microscopy, Image & Video Processing, Computer Vision and Signal Processing.

Betcke, Prof. Timo

Keywords: 
Scientific computing, partial differential equations, research software.

Research Area: 
My research interest is at the intersection of computational mathematics, research software, and applications. In my group we are developing numerical methods and software implementations for application areas including computational electromagnetics, high-frequency acoustics, and electrostatic modelling. A particular emphasis is on the design and implementation of well maintained open-source software tools in these areas.

Guillas, Prof. Serge

Keywords: 
Environmental statistics, uncertainty quantification, data sciences.

Research Area: 
Prof. Serge Guillas is investigating Environmental Statistics, and Uncertainty Quantification of complex computer models. Applications to tsunami and climate are carried out with his PhD students and postdocs. As Chair of the UCL-Met Office Academic Partnership, he supports multiple collaborations with researchers at the Met Office, and is currently interested in particular in the extension of weather and climate models using Data Sciences and Machine Learning: Uncertainty Quantification, Bayesian Calibration, Data Assimilation, with e.g. applications to upper atmosphere and cloud modelling. S. Guillas also collaborates with the UK Atomic Energy Authority (UKAEA) on modelling nuclear fusion, the clean energy of the future. He founded and leads the Uncertainty Quantification interest group of the Alan Turing Institute.

Jensen, Dr Max

Keywords: 
Optimal Control, Finite Element Methods, Numerical Analysis, Fully Nonlinear PDEs, Numerical Solution of High-Dimensional PDEs, Dynamic Programming, Stochastic Numerical Methods.

Research Area: 
My research centres on developing and rigorously analysing numerical methods for nonlinear differential equations and optimal control systems. Optimal control concerns optimising dynamical systems, which are often formulated through stochastic differential equations to account for noise. Results find direct application in engineering, science, and mathematical finance. Challenges arise from the fully nonlinear structure of the associated PDEs, the low regularity of solutions, and their often high-dimensional nature.

Marra, Prof. Giampiero

Keywords: 
Endogeneity, non-random sample selection, MNAR missing data, unobserved confounding, penalised regression spline, copula, generalized regression, joint models, computational statistics, gamlss, survival data.

Research Area: 
Penalized likelihood based inference in semiparametric simultaneous joint equation models, copula regression, generalized additive modelling, distributional regression, generalised additive models for location, scale and shape, flexible survival modelling.

Ni, Prof. Hao

Keywords:
Stochastic analysis, machine learning, scientific computing, quantitative finance, computer vision

Research Area: 
My research interests include stochastic analysis, machine learning and their applications in diverse fields, such as scientific computing, quantitative finance and computer vision. I am interested in innovating machine learning algorithms for analysing sequential data by leveraging rough path theory. One of my current interests lies in the development of novel physics-informed neural networks aimed to solve (stochastic)-PDEs accurately and efficiently.

Rubio Alvarez, Prof. Javier

Keywords: 
Bayesian inference, Biostatistics, Computational statistics, Model selection.

Research Area: 
My research resides at the intersection of statistical methodology, computational statistics, and applications. Key highlights include: (i) the analysis of time-to-event data (survival analysis), with particular interest in the development of flexible and interpretable models; (ii) Bayesian variable and model selection using different types of priors; and (iii) Applying statistical methodologies in medical contexts, such as cancer epidemiology, to answer genuine questions of interest. I am keen on supervising projects within these areas or related topics. Specifically, I am interested in supervising a candidate focusing on the use of Ordinary Differential Equations (ODEs) for survival modeling.