Find Academic Supervisors

Jo KennedyView profile

Associate Professor

• Financial Engineering
• Quantitative Finance
• Mathematical Finance
• +3 more

Dr. Jo Kennedy is an Associate Professor in the Department of Statistics at the University of Warwick, where she has been since 1998. Previously, she held positions at the University of Oxford and the University of Bristol. Her research focuses on financial derivatives, particularly interest rate derivatives and modeling requirements for market practitioners. She co-authored the influential book Financial Derivatives in Theory and Practice (2nd ed., 2004) with Phil Hunt. Kennedy holds a PhD in probability theory from the University of Cambridge, following her undergraduate and MSc degrees from the University of Sydney. Her work emphasizes advanced stochastic models, including Markov-functional models, stochastic volatility, and the SABR framework. Recent publications explore cross-currency models, non-Gaussian drivers, and the implications of model choice for hedging strategies. Her research bridges theoretical finance with practical applications in derivatives pricing and risk management. Despite her prolific output, no scientific awards are explicitly mentioned in the provided text. She collaborates actively with colleagues like J. Gogala, D. Pham, and L. Kaisajuntti, contributing to computational finance and applied mathematical finance journals. Her office is located in Room 3.06 of the MSB building at the University of Warwick.

Douglas NollView profile

Professor

• Biomedical Engineering
• Radiology
• Magnetic Resonance Imaging
• +9 more

Douglas Noll is the Ann and Robert H. Lurie Professor in the Department of Biomedical Engineering at the University of Michigan , with additional appointments as Professor of Radiology and affiliate of multiple research institutes including the Michigan Alzheimer’s Disease Research Center, Michigan Neuroscience Institute, and Michigan Institute for Imaging Technology and Translation (MIITT). He co-directs the Functional MRI Laboratory and leads the NeuroImaging Core at the Michigan Alzheimer’s Disease Research Center. Prof. Noll’s research focuses on MRI technology development , particularly for functional MRI (fMRI) . His group specializes in high-speed MRI acquisition , signal processing , and image reconstruction to map brain function. Key areas include artifact elimination , physiological modeling , and quantitative imaging for neurological and psychiatric disorders. Recent collaborations apply MRI to histotripsy therapy monitoring. The 15 most recent articles reflect his work in dynamic MRI reconstruction , artifact correction , and cross-vendor protocol standardization . Topics span B0 shimming , histotripsy targeting , and non-Cartesian sampling , emphasizing spatiotemporal modeling and machine learning integration for accelerated imaging. Scientific Awards : Ann and Robert H. Lurie Professor. Research Collaborations : Functional MRI Laboratory, Michigan Alzheimer’s Disease Research Center, Michigan Institute for Imaging Technology and Translation (MIITT).

Arie LevantView profile

Professor

• Nonlinear Control Theory
• Sliding Mode Control
• Homogeneous Discontinuous Control
• +2 more

Prof. Arie Levant is a Professor in the Department of Applied Mathematics at Tel Aviv University's School of Mathematical Sciences, actively teaching Spring 2025 courses with Monday reception hours via Zoom (17:10-18:00). His foundational work established High-Order and Homogeneous Sliding Mode Control theories alongside Robust Exact Differentiation. His educational background includes: Ph.D. (1987) from USSR Academy of Sciences, Moscow: Thesis "Higher-order sliding modes and their application in control of uncertain processes" supervised by Prof. S.V. Emelyanov Postgraduate studies (1983-1987) in mathematical control theory at same institute B.Sc./M.Sc. (1980) from Moscow State University under Prof. V.I. Arnold in Theory of Differential Equations Levant's research centers on Nonlinear Control Theory with specialization in Sliding Mode Control , Homogeneous Discontinuous Control , and Robust Exact High-Order Differentiation . His methodologies enable finite-time-exact tracking in uncertain systems and real-time noise-robust signal differentiation, addressing fundamental challenges in control system resilience. Analysis of his 15 most recent publications (2016-2023) reveals concentrated advancements in chattering reduction, discretization for digital implementation, and noise filtering within sliding mode frameworks. These works demonstrate consistent focus on bridging theoretical control principles with practical engineering applications, particularly in real-time signal processing and robust controller design.

Günther GrünView profile

Professor

• Stochastic Partial Differential Equ (SPDE)
• Free Boundary Problems
• Continuum Mechanics
• +5 more

Prof. Dr. Günther Grün is a faculty member at the University of Erlangen-Nuremberg , specializing in Applied Mathematics . His work bridges rigorous mathematical analysis, numerical methods, and applications in continuum mechanics , fluid dynamics , and free boundary problems . He serves as Spokesperson for the DFG Research Training Group IntComSin (2022–2027), focusing on interfaces, complex structures, and singular limits in mathematical modeling and simulation. Education Diploma in Mathematics (1991), University of Bonn PhD in Mathematics (1994), University of Bonn Habilitation in Mathematics (2001), University of Bonn Research Interests center on degenerate parabolic equations (e.g., porous-medium and thin-film equations), stochastic SPDEs with multiplicative noise, diffuse interface models for two-phase flow with surfactants, and dimension-reduced models via singular limits. His work combines calculus of variations , homogenization , and Gamma-convergence for multi-scale analysis, alongside finite-element/finite-volume schemes for efficient simulations. Scientific Awards Marie Curie Research Fellowship (1996), Universita di Tor Vergata, Rome Advising Legacy includes mentoring doctoral candidates such as Dr. Fabian Klingbeil (Accenture), Dr. Jürgen Becker (Fraunhofer Institute), and Prof. Dr. Julian Fischer (IST Austria), who have pursued careers in academia, industry, and research institutions.

• Mathematical Analysis
• Applied Mathematics
• Geometry

Professor Alexander Kushpel is affiliated with Çankaya University, Faculty of Arts and Sciences, Department of Mathematics, Turkey, since 2018. He has held academic roles at the University of Leicester (2011-2016), State University of Campinas (1997-2001, 2006-2010), and Ryerson University (2001-2006). PhD in Mathematics, University of Leicester (2015) PhD in Mathematics, Universidade Estadual de Campinas (2009) PhD in Mathematics, Institute of Mathematics of the National Academy of Sciences of Ukraine (1992) His research focuses on Mathematical Analysis , Applied Mathematics , and Geometry , with emphasis on approximation theory, Fourier analysis, and operator entropy. Key article trends include n-widths, Sobolev classes on manifolds, and financial mathematics applications. He has advised Regis Leonardo Braguim Stabile (MSc thesis: "N-width of set of smooth functions on the sphere SD").

Andrea BarthView profile

Professor

• Stochastic Partial Differential Equations
• Uncertainty Quantification
• Monte Carlo Methods
• +4 more

Andrea Barth is a W3-Professor of Computational Methods for Uncertainty Quantification at the University of Stuttgart, leading the Research Group for Computational Methods for Uncertainty Quantification within the Excellence Cluster for Simulation Technology. She holds a Ph.D. from the University of Oslo (2009) and has held positions at ETH Zürich and the University of Stuttgart. Her work focuses on stochastic partial differential equations, numerical methods for uncertainty quantification, and applications in engineering and natural sciences. Education: Ph.D. in Mathematics, University of Oslo (2006–2009) Lecturer/Postdoc at ETH Zürich (2010–2013) Junior Professor at University of Stuttgart (2013–2017) Research Interests: Stochastic PDEs, uncertainty quantification, Monte Carlo methods, Bayesian inverse problems, and numerical analysis of random fields. Her work bridges stochastic analysis and numerical simulations, addressing challenges in modeling and simulating complex systems with uncertainties. Grants & Funding: Principal Investigator in projects like 'Data-Integrated Simulation Science' (ExC 2075) and 'Quantitative Methods for Visual Computing' (SFB/TRR 161). Her research also explores applications in porous media, carbon dioxide storage, and optical flow analysis. Supervision: Advised PhD students including Oliver König, Fabio Musco, and Robin Merkle. Current students focus on topics like deep learning for stochastic PDEs and continuous level Monte Carlo methods.

Xue-Mei LiView profile

Professor

• Stochastic Analysis
• Geometric Stochastic Analysis
• Malliavin Calculus
• +5 more

Xue-Mei Li is a Professor of Mathematics at Imperial College London and École Polytechnique Fédérale de Lausanne (EPFL). She holds chairs in Probability and Stochastic Analysis at both institutions. Her research focuses on stochastic analysis, geometric stochastic processes, and multi-scale systems, with contributions to areas like Malliavin calculus, fractional dynamics, and coarse curvature. Li has held positions at the University of Warwick, University of Connecticut, and others, supported by fellowships from the Alexander von Humboldt Foundation, Royal Society, and MSRI. Her work addresses fundamental questions in stochastic differential equations, geometric analysis, and their applications to complex systems. Education and Career: PhD in Mathematics, University of Warwick EPSRC Research Associate Faculty positions at the University of Connecticut (tenured Associate Professor) Research Interests: Her research spans stochastic differential equations (SDEs), stochastic partial differential equations (SPDEs), geometric stochastic analysis, and fractional dynamics. Notable contributions include the BEL formula, strict local martingales, and solutions to longstanding problems in strong completeness on non-compact manifolds. She explores interactions between stochastic processes and geometric structures, including coarse Ricci curvature and homogenization theory. Awards and Grants: Supported by NSF, EPSRC/UKRI, and Swiss NSF grants Awarded fellowships from Alexander von Humboldt Foundation, Royal Society, and MSRI Advising and Teams: PhD students: Johann Gehringer, Rhys Steel, Julian Sieber, and others Leading working groups on stochastic analysis and geometric dynamics

• Metocean Statistics
• Wave Modeling
• Coastal Engineering
• +5 more

Konstantinos Christakos is a researcher at the Norwegian Meteorological Institute and an adjunct researcher at the Department of Marine Technology, Norwegian University of Science and Technology (NTNU). His work focuses on metocean statistics, numerical wave modeling, and marine renewable energy applications. Doctoral dissertation: Wind-Generated Waves in Fjords and Coastal Areas (University of Bergen, 2021) Master’s thesis: Characterization of the coastal marine atmospheric boundary layer (MABL) for wind energy applications (University of Bergen, 2013) Christakos specializes in wave modeling for coastal and fjord environments, with a focus on wind-wave interactions, inhomogeneous wave dynamics, and open-source software development. He has contributed to tools like metocean-api , DNORA , and metocean-stats for metocean data analysis and visualization. His recent publications and conference presentations address Arctic wave energy resource assessment, downscaled wave hydrodynamics, and fjord-specific modeling challenges. He actively collaborates with institutions like SFI BLUES, SINTEF, and NOAA, and lectures on marine dynamics and stochastic methods at NTNU. Konstantinos participates in interdisciplinary teams and workshops, emphasizing computational modeling, machine learning integration, and environmental engineering for marine structures.

Mine ÇağlarView profile

Professor

• Probability
• Stochastic Processes

Mine Çağlar is a Professor in the Department of Mathematics at Koç University, specializing in probability theory and stochastic processes with applications in mathematical finance and risk analysis. Her work addresses fundamental problems in Markov additive processes, Lévy processes, and Brownian motion, contributing to both theoretical advances and practical financial modeling. Her academic credentials include: PhD in Statistics and Operations Research from Princeton University (1997) Master’s in Industrial Engineering from Bilkent University (1991) B.A. in Industrial Engineering from Middle East Technical University (1989) Professor Çağlar’s research centers on extreme event analysis in stochastic processes, particularly maximum drawdown, maximum loss, and optimal stopping problems. She investigates path properties of spectrally negative Lévy processes and develops mathematical frameworks for degenerate market models. Her work bridges abstract probability theory with real-world financial applications, including risk management and hedging strategies. Recent publications demonstrate sustained innovation in stochastic analysis, with a focus on long-time behavior of complex processes and boundary-crossing phenomena. Analysis of her 15 most recent publications (2018–2024) reveals a cohesive research trajectory emphasizing Markov additive processes (40% of articles), Lévy process extremes (30%), and financial applications (20%). Key methodological trends include path decomposition techniques, Monge-Ampère equations on Wiener space, and stochastic flow modeling. Her work increasingly integrates fluid dynamics concepts like Çinlar models for turbulence simulation, reflecting interdisciplinary expansion into applied mathematics. Her scholarly recognition includes: Hayri Körezlioğlu Research Award (2013) Parlar Foundation Research Incentive Award (2005)

Anja SchlömerkemperView profile

Professor

• Mathematical Analysis
• Partial Differential Equations
• Calculus of Variations
• +3 more

Prof. Anja Schlömerkemper holds the Chair of Mathematics in the Sciences at the University of Würzburg since 2011 and serves as Vice President responsible for Equal Opportunities, Career Planning, and Sustainability. She earned her PhD in Mathematics from the University of Leipzig (2002) and held postdoctoral positions at institutions including the University of Oxford and the Max Planck Institute for Mathematics in the Sciences. Her research focuses on mathematical analysis, particularly partial differential equations and calculus of variations, with applications to materials science and physics. She investigates mathematical methods to model material behavior at micro and macro scales, including elastic and magnetic materials. Her work bridges theoretical analysis and practical applications in continuum mechanics. She has contributed to fluid-rigid body interactions, magnetoelastic materials, and phase transitions. As Vice President, she promotes gender equality, supports early-career researchers, and advances sustainability across the university's research, teaching, and administration. Education: PhD in Mathematics (Leipzig, 2002) and Diploma in Physics (Göttingen, 1998). Professional Experience includes roles at the Universities of Bonn, Erlangen-Nuremberg, and Stuttgart, as well as the Max Planck Institute. Research interests also encompass homogenization theory, dislocation dynamics, and stochastic discrete systems. She leads interdisciplinary projects, collaborates internationally, and advises on academic policies.

Helen ByrneView profile

Professor

• Mathematical Biology
• Cancer Biology
• Tissue Engineering
• +3 more

Professor Helen Byrne is a Professor of Applied Mathematics at the University of Oxford’s Mathematical Institute and a Tutorial Fellow at Keble College. She specializes in mathematical modeling of biomedical systems, with a focus on tumor growth, wound healing, and tissue engineering. Her research bridges applied mathematics, statistics, and data science to address fundamental biomedical questions. She has held roles including Associate Head of Department (Planning & Equality), Senior Mathematics Tutor at Keble College, and Director of Equality and Diversity in the Mathematical, Physical and Life Sciences Division. Her work has been recognized through prestigious awards such as the 2021 Australian Laureate Fellowship and the 2019 Leah Edelstein-Keshet Prize. Her publications emphasize interdisciplinary applications of mathematical biology, including tumor-immune interactions, vascular dynamics, and epigenetic landscapes. Key contributions include modeling tumor-induced angiogenesis, liver assembloids, and atherosclerotic plaques. Current projects aim to develop novel mathematical frameworks for interpreting complex biomedical data. Research Grants: $3,021,288 ARC Australian Laureate Fellowship (2021) for 'New Approaches to Understand How Form and Function Shape Complex Systems.' Awards: 2021 Australian Laureate Fellowship (ARC) 2019 Leah Edelstein-Keshet Prize (Senior Award) 2020 Fellow of the Society for Mathematical Biology Labs/Teams: Part of the Mathematical Biology research group at Oxford, contributing to collaborative projects in tumor modeling and biomedical systems.

• Agricultural Climate Change Adaptation
• Farming Systems Resilience
• Climate Modeling
• +3 more

Julian Ramirez-Villegas is a Special Professor on Agricultural Climate Change Impacts and Adaptation at Wageningen University's Plant Production Systems group and a Principal Scientist/Leader of Data Science for Climate Action at CGIAR (Rome). He holds a PhD from the University of Leeds (UK). His research focuses on climate adaptation in farming systems, particularly in low/middle-income countries, with over 60 peer-reviewed publications in journals like PNAS and Nature. Recognized by the United Nations for climate adaptation work, he leads a team of researchers specializing in agricultural modeling, climate services, and socio-economic analysis. At Wageningen, his key research questions address adaptation effectiveness, interplay between adaptation forms (coping/systems change/transformation), and trade-offs between adaptation/mitigation in tropical systems. His work bridges academic research with global policy, emphasizing data-driven solutions for climate-resilient agriculture. Publications highlight innovative approaches in climate data modeling (e.g., CMIP5 projections, EcoCrop model applications), biodiversity conservation strategies, and policy frameworks for climate-smart agriculture. He collaborates widely with CGIAR centers and international organizations to translate research into actionable climate adaptation strategies.

Michael BaakeView profile

Professor

• Aperiodic Order
• Dynamical Systems
• Combinatorics
• +4 more

Michael Baake is a Professor of Mathematics at Bielefeld University, affiliated with the Faculty of Mathematics. His research focuses on Aperiodic Order, Dynamical Systems, Combinatorics, and Mathematical Physics. He leads multiple research projects, including the SFB/TR 358 'Integral Structures in Geometry and Representation Theory' and SFB 1283 'Taming Uncertainty'. His work bridges pure mathematics with applications in crystallography and stochastic systems. Affiliations: Representative for the Faculty Library, Co-Director of the Research Focus on Mathematical Modeling. Cooperations: Collaborates with international experts like Uwe Grimm, Daniel Lenz, and Nicolae Strungaru on topics such as aperiodic tilings and spectral theory. Research Groups: Leads a vibrant research group with members including Anna Klick, Daniel Luz, and Timo Spindeler, focusing on quasicrystals, combinatorial structures, and number theory applications. His contributions include co-editing the book 'Aperiodic Order: Crystallography and Almost Periodicity' and organizing workshops on spectral theory and dynamical systems. He actively engages in academic service, including editorial roles and conference organization.

• Stochastic Analysis
• Random Walks
• Percolation Theory
• +1 more

Sebastian Andres is Professor of Stochastics at TU Braunschweig's Institute of Mathematical Stochastics. His research focuses on random processes in disordered media, including homogenization of random walks, percolation theory, and heat kernel analysis. He holds a habilitation from University of Bonn and previously held positions at University of Cambridge and University of Manchester. Research Themes: Quantitative homogenization, invariance principles for degenerate random walks, and applications to mathematical physics. Teaching: Current courses include Probability Theory and Discrete Financial Mathematics; authored lecture notes on Martingale Theory for Finance.

Min ChenView profile

Professor

• Statistical Genomics
• Bioinformatics
• Bayesian Methods
• +5 more

Dr. Min Chen is a Professor in the Department of Mathematical Sciences at the University of Texas at Dallas (UTD), affiliated with the School of Natural Sciences and Mathematics. He holds an adjunct professorship at the University of Texas Southwestern Medical Center. His expertise spans statistical genomics, bioinformatics, Bayesian methods, and sampling techniques. He completed his Ph.D. in Statistics and Decision Science at the University of Texas at Austin and a postdoctoral fellowship in statistical genomics at Yale University. Education: B.S. in Computer Science (University of Science & Technology of China, 1994); M.A. in Statistics (University of Pittsburgh, 1999); Ph.D. in Statistics (UT Austin, 2006); Postdoc in Statistical Genomics (Yale University, 2008–2010). Research focuses on statistical methodologies in genomics, including genome-wide association studies, network-based modeling, and Bayesian integrative analysis. He also explores spatial modeling and ranked set sampling. His work addresses challenges in cancer genetics, epigenetics, and single-cell gene regulation. Notable awards include the NIH Career Development Award (2013), David Bruton Fellowship (2006), and R.L. Anderson Student Paper Award (2006). He is a member of the American Statistical Association and International Chinese Statistical Association. He advises graduate students in Data Science, Statistics, and Bioinformatics & Computational Biology (BCBM) programs. His teaching includes courses on advanced statistical methods and data science. Research contributions span over 40 peer-reviewed articles, with recent work in tumor pathology imaging, antibiotic resistance, and Alzheimer’s disease mechanisms.