Find Academic Supervisors

• Risk and Ruin Theory
• Stochastic Control Problems
• Stochastic Modelling in Insurance

Amine Lkabous is a Lecturer in Actuarial Science at the School of Mathematics Sciences, University of Southampton. He holds a PhD in Statistics from the University of Quebec in Montreal (2018) and an MSc in Actuarial Science from Laval University (2013). Prior to his current role, he was a Postdoc at the University of Waterloo’s Department of Statistics and Actuarial Science (2018–2020). His research focuses on developing probabilistic tools for quantifying risks in insurance and financial mathematics, particularly through stochastic control problems and Lévy process models. Key areas include ruin theory, stochastic modeling, and actuarial applications of Lévy processes. He teaches modules such as MATH2040/6131 (Financial Mathematics) and MATH3066/6130 (Actuarial Mathematics II). His recent publications (2019–2025) explore topics like occupation times in Lévy risk models, refracted processes with dividend pullbacks, and deep drawdown analysis. These works emphasize applications to insurance solvency and stochastic optimization. Amine supervises PhD students Mengni Yang and Quan Nguyen. He is actively accepting new PhD applications in his research areas.

• High-Dimensional Probability
• Stochastic Analysis
• Functional Inequalities
• +3 more

Yair Shenfeld is an Assistant Professor of Applied Mathematics at Brown University . Before joining Brown, he was a C.L.E. Moore Instructor and NSF Postdoctoral Fellow at MIT's Mathematics Department. He earned his PhD in Mathematics from Princeton University under Ramon van Handel and a B.Sc. in Mathematics from MIT. His research focuses on high-dimensional probability , exploring its intersections with analysis, geometry, and mathematical physics. Key areas include stochastic analysis, functional inequalities, optimal transport, renormalization group methods, and convex geometry. Notable contributions involve extremal problems in convex geometry and stability results for functional inequalities like the logarithmic Sobolev inequality. Shenfeld has presented at venues such as the BIRS Workshop on Stochastic Mass Transports (2022), where he discussed transportation along Langevin dynamics. His work often employs advanced techniques from probability theory, geometric analysis, and functional analysis to address foundational questions in mathematical physics. Education: PhD in Mathematics, Princeton University (Advisor: Ramon van Handel) B.Sc. in Mathematics, MIT Awards & Recognition: None explicitly listed in texts. Professional Activities: Active in collaborative research networks, with publications in top journals like Acta Mathematica , Duke Mathematical Journal , and Annals of Henri Poincaré . His talks and workshop participation highlight engagement with the broader mathematical community.

Ana DjurdjevacView profile

Assistant Professor

• Numerical Analysis
• Stochastic Processes
• Partial Differential Equations
• +3 more

Ana Djurdjevac is an Assistant Professor in the Department of Numerical Analysis and Stochastics at the Freie Universität Berlin , within the Department of Mathematics and Computer Science. Her research focuses on numerical analysis, stochastic processes, and partial differential equations, with particular emphasis on uncertainty quantification and mathematical modeling in evolving domains. She teaches advanced courses such as Numerical Methods for Stochastic Differential Equations and Stochastik I , reflecting her expertise in computational methods and probabilistic frameworks. Her work integrates theoretical analysis with practical numerical techniques, addressing challenges in domains such as fluid dynamics, quantum systems, and biological surface fluctuations. Recent contributions include studies on hybrid algorithms for particle systems, rough homogenization in stochastic dynamics, and synchronization mechanisms in conservation laws. Djurdjevac actively participates in academic events, including the 2025 SIAM Conference on Computational Science and Engineering, and collaborates on projects involving quasi-Monte Carlo methods for Bayesian inversion and domain decomposition techniques. Professional activities highlight her role in shaping emerging fields like stochastic PDEs on evolving domains and feedback loops in agent-based models. While no specific awards are listed, her prolific publication record and teaching roles underscore her contributions to computational science and applied mathematics.

Clement MouhotView profile

Professor

• Partial Differential Equations
• Functional Inequalities
• Stochastic Processes
• +2 more

Clement Mouhot is a Professor of Mathematical Sciences at the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge. He is affiliated with the Cantab Capital Institute for the Mathematics of Information, contributing to interdisciplinary research at the intersection of mathematics and information science. Professor Mouhot's research spans several interconnected areas of mathematical analysis. His primary focus lies in partial differential equations, particularly those arising in kinetic theory and mathematical physics. He has made significant contributions to the study of the Boltzmann equation, Vlasov-Poisson system, and related kinetic models. His work often addresses fundamental questions about regularity, stability, and long-time behavior of solutions to these equations. Mouhot's research also extends to functional inequalities, stochastic processes, and the mathematical foundations of statistical mechanics, demonstrating a deep connection between analysis and physical principles. Analysis of Professor Mouhot's publication record reveals a sustained focus on Landau damping phenomena, Boltzmann equation theory (particularly the non-cutoff case), and kinetic Fokker-Planck equations. His work frequently bridges multiple mathematical disciplines, combining techniques from harmonic analysis, functional analysis, and probability theory. A notable theme throughout his research is the establishment of quantitative decay estimates and convergence rates to equilibrium for various kinetic models, often requiring innovative approaches to handle the complex structure of these equations. Professor Mouhot has maintained a strong collaborative network, with frequent co-authorship with leading researchers in mathematical physics and analysis, including Cédric Villani (with whom he published the influential paper "On Landau damping"), José Alfredo Cañizo, Stéphane Mischler, and others. His research continues to address fundamental questions at the intersection of analysis and mathematical physics, with implications for understanding complex systems in statistical mechanics and plasma physics.

Mikael BacklundView profile

Assistant Professor

• Optical Microscopy
• Quantum Sensing
• Magnetic Resonance
• +4 more

Mikael Backlund is an Assistant Professor of Chemistry at the University of Illinois, Urbana-Champaign, affiliated with the College of Liberal Arts & Sciences. His research focuses on advancing optical microscopy and quantum sensing techniques to study nanoscale systems in chemistry, materials science, and biology. He joined UIUC in 2020 after postdoctoral work at Harvard University and holds affiliations with the Illinois Quantum Information Science and Technology Center (IQUIST) and the Center for Biophysics and Quantitative Biology (CBQB). Backlund earned his B.S. in Chemistry from UC Berkeley (2010) and his Ph.D. in Chemical Physics from Stanford University (2016), supported by the Robert and Marvel Kirby Fellowship. His postdoctoral research at Harvard explored quantum diamond sensors and opto-magnetic imaging. His lab develops cutting-edge methods like nanoscale magnetic resonance microscopy using diamond NV centers and quantum metrology approaches for single-molecule imaging. Research areas include quantum-inspired super-resolution, biophysical systems analysis, and condensed matter physics. Projects bridge experimental and theoretical work, emphasizing precision and innovation. Backlund has been recognized with prestigious awards including the NSF CAREER Award (2025), Beckman Young Investigator Award (2023), and Scialog Fellowship (2025). His work intersects quantum science, biophysics, and materials chemistry, with applications in both fundamental and applied research.

James HopgoodView profile

Professor

• Statistical Signal Processing
• Machine Learning
• Data Science
• +5 more

James Hopgood is a Professor of Statistical Signal Processing at the University of Edinburgh's School of Engineering, leading the Institute for Data, Imaging, and Communications. He holds roles as Dean of Quality and Enhancement for the College of Science and Engineering and Director of the EPSRC/MoD CDT in Sensing, Processing & AI for Defence and Security (SPADS). His research focuses on statistical signal processing applications in medical imaging, acoustic sensing, and multi-target tracking. Hopgood earned his M.A., M.Eng., and Ph.D. from the University of Cambridge, joining Edinburgh in 2004. He has developed algorithms for Bayesian signal processing, acoustic source localization, and medical imaging, with over 100 publications. As Editor-in-Chief of the IET Journal of Signal Processing since 2011, he contributes to academic leadership. Teaching includes courses on signal processing, probability, and sensor networks, emphasizing visualization and real-world applications. Current projects span adaptive optical imaging for medical diagnostics and probabilistic graphical models for multi-target tracking. His work bridges academia and industry, collaborating with companies like Leonardo and Agilent Technologies. He supervises numerous PhD students and leads multidisciplinary initiatives in defense, healthcare, and autonomous systems. **Education**: PhD in Statistical Signal Processing, University of Cambridge (2001) MEng in Electrical and Information Sciences, University of Cambridge (1997) MA, University of Cambridge (2000) **Research Interests**: His work spans model-based Bayesian methods, acoustic source localization, medical imaging (e.g., fluorescence lifetime imaging), and multi-target tracking. Recent projects include optimizing UAV trajectories in dynamic environments and developing algorithms for distributed sensor networks. **Grants & Collaborations**: Funded by EPSRC/MoD, GSK, and Leonardo, his projects address defense sensing, drug development, and clinical imaging. Collaborations include Firefinch (software/data science) and the UDRC Summer School. **Labs/Teams**: Leads the Acoustics and Audio Group and the SPADS CDT, fostering innovation in sensing and AI for defense/security applications.

• Stochastics
• Probability Theory
• Stochastic Processes
• +2 more

Dr. Franz Nestmann serves as a Researcher at the Institute of Stochastics within the Department of Mathematics at Karlsruhe Institute of Technology (KIT). His academic profile includes teaching responsibilities across multiple semesters, with current appointments for Sommersemester 2025 including Mathematics 2 for Economics, associated exercises, and a specialized seminar in mathematical economics. His research concentrates on fundamental stochastic processes including Poisson processes and Markov chains, with significant applications in mathematical economics and fractal geometry. This interdisciplinary work bridges theoretical probability frameworks with practical economic modeling, particularly evident through his advanced courses in stochastic systems and geometric analysis of complex structures.

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +3 more

Lisa Hartung is a Full Professor (W3) at Johannes Gutenberg University Mainz's Department of Mathematics since 2025, leading research in stochastic processes. She previously held Associate (2019-2025) and Assistant Professor (2019) positions at the same institution and served as a Courant Instructor at NYU's Courant Institute (2016-2018). Her educational background includes a PhD from Bonn University (2016) under Prof. Anton Bovier in Probability Theory. Key research interests span branching Brownian motion , Gaussian free fields , random energy models , and stochastic processes on evolving networks , with applications in statistical mechanics and mathematical physics. Her work frequently addresses extremal processes, phase transitions, and complex temperature phenomena. Analysis of her recent publications reveals dominant trends in log-correlated random fields, variable-speed branching processes, and hard-wall boundary conditions. Articles consistently explore asymptotic behavior, maximum processes, and structural properties of stochastic systems, with increasing interdisciplinary connections to computer science through data stream modeling. Scientific recognition includes: Förderpreis der Fachgruppe Stochastik (2018) Hartung secures substantial research funding as PI for multiple high-impact projects: the ANR-DFG grant Random Energy Models (2020), DFG's SPP 2265 Random Geometric Systems (2020), TRR146 Multiscale Simulation Methods (2020), and Carl-Zeiss project TOPML (2022). She also serves on the executive board of DMV Fachgruppe Stochastik (since 2023) and coordinates Jugend trainiert Mathematik for grades 8/9. Her research group participates in the DFG-funded network Stochastic Processes on Evolving Networks and collaborates internationally through workshops on interacting particle systems and stochastic population models.

Patrick FlynnView profile

Assistant Professor

• Partial Differential Equations
• Kinetic Theory
• Fluid Mechanics

Patrick Flynn is an Assistant Professor at the University of California, Los Angeles (UCLA), specializing in Partial Differential Equations , Kinetic Theory , and Fluid Mechanics . His research bridges mathematical analysis with physical modeling in these domains. Research Trends : Flynn's recent publications focus on nonlinear dynamics in kinetic and fluid systems. Key topics include mixing in random fluid flows, well-posedness of kinetic equations, scattering theory for Vlasov systems, and self-organization in biophysical models. Collaborations with prominent researchers like Yan Guo, Benoit Pausader, and Huy Nguyen highlight interdisciplinary applications in plasma physics, fluid mechanics, and mathematical biology. Selected Contributions : 2024 : Studies on the Vlasov-Poisson-Landau system and random fluid mixing. 2021 : Analysis of scattering maps and surface tension limits in fluid models. 2019 : Modeling of self-organized clusters in run-and-tumble processes.

• Plasma Physics
• Low Temperature Plasmas
• Plasma Modeling and Simulation
• +5 more

Ralf Peter Brinkmann is a Professor and Chair holder of Theoretical Electrical Engineering at Ruhr University Bochum, Faculty of Electrical Engineering and Information Technology, since 2000. He is actively involved in research as a member of the Center for Plasma Science and Technology and a board member of the Collaborative Research Centre 591 ("Universal Behaviour of Non-Equilibrium Plasmas: Heating, Transport, and Structure Formation") since 2003. His office is located at Room ID 1/127, Universitätsstraße 150, D-44801 Bochum, with contact via brinkmann@tet.rub.de. His educational background includes: Diploma in Physics (1984) from Ruhr-University Bochum Diploma in Electrical Engineering (1985) from Ruhr-University Bochum PhD (Dr. rer. nat.) in theoretical astrophysics (1986) from Ruhr-University Bochum Brinkmann's research centers on low-temperature plasma physics, specifically modeling and simulation of technical plasmas, theory of plasma edge layers, and model-based plasma diagnostics. He investigates electron dynamics, power absorption mechanisms, and sheath behavior in capacitive coupled radio frequency discharges, magnetron plasmas, and atmospheric pressure plasma jets, with focus on kinetic effects and electromagnetic phenomena. Analysis of his recent publications (2023-2025) reveals strong emphasis on electromagnetic particle-in-cell simulations, time-resolved plasma transition studies (E-H, capacitive-inductive), and thermodynamic quasi-equilibria in high-power magnetrons. Key trends include rotational spoke dynamics in ExB plasmas, nonlocal electron transport, and diagnostic tools like the multipole resonance probe. Scientific awards: No scientific awards listed for Brinkmann in the provided text Brinkmann has advised PhD and Master's students (specific names unlisted) and secured major research funding through Collaborative Research Centre 591 under the German Research Foundation. His work includes industry collaborations with Siemens AG/Infineon Technologies and academic partnerships with Stanford University, University of Chicago, and Old Dominion University. He leads research within the Center for Plasma Science and Technology and Collaborative Research Centre 591, developing advanced plasma models and diagnostic probes. His team focuses on computational frameworks for plasma edge layers, magnetron discharge kinetics, and industrial plasma process optimization, particularly for semiconductor and thin-film applications.

Dipak DeyView profile

Professor

• Bayesian analysis
• Bioinformatics
• Biostatistics
• +8 more

Dipak Dey is a Professor in the Department of Statistics at the University of Connecticut . His work bridges theoretical and applied statistics, with a focus on Bayesian methodologies and computational statistics. Affiliations : University of Connecticut, Department of Statistics Contact : dipak.dey@uconn.edu , Office: AUST 327, Phone: (860) 486-4755 His research interests span Bayesian analysis , Biostatistics , Computational statistics , Statistical genetics , and Spatio-temporal modeling . He has pioneered techniques in spatial curvature processes and scalable Bayesian algorithms for large datasets. Recent publications highlight his contributions to Bayesian spatial modeling (blockNNGP, curvature processes), survival analysis (skew-t distributions, cure rate models), and computational statistics (variable selection in Gaussian processes, fast inference algorithms). Applications include insurance data , epidemiology , and environmental statistics . Scientific Awards : Board of Trustees Distinguished Professor (University of Connecticut) He actively collaborates on interdisciplinary projects and mentors researchers in advanced statistical methodologies for complex data structures.

• Biomedical Engineering
• Computational Neuroscience
• Medical Imaging
• +3 more

Santtu Söderholm is a doctoral researcher affiliated with the Mathematics Research Centre. His academic focus lies within biomedical engineering and computational neuroscience, utilizing numerical simulation techniques to analyze cerebral blood flow and electrical conductivity in brain tissue. His research interests include: Computational modeling of cerebral hemodynamics Dynamic brain imaging techniques EEG source reconstruction Multi-compartment head modeling Impact of blood circulation on biomedical signals Finite element method applications in neuroimaging Recent publications highlight his work on diffusion approximation in microcirculation analysis (2025), dynamic impact modeling (2024), and the Pressure–Poisson equation's role in simulating arterial flow (2023). Santtu has also contributed to open-source datasets and presented on computational assessment methods at a conference in 2018.

Flavio SARTORETTOView profile

Associate Professor

• Numerical Analysis
• Computational Science
• Medical Signal Processing
• +4 more

Flavio SARTORETTO is an Associate Professor in Scientific Computing at Ca' Foscari University of Venice. He holds a Mathematics degree from the University of Padua and has held academic positions at University of Padua (1982-1992) and Sapienza University of Rome (1992-1993) before joining Ca' Foscari in 1993. His research focuses on numerical analysis, computational methods, and interdisciplinary applications including environmental modeling, cognitive processes, and assistive technologies. Key research areas include numerical solutions of PDEs, meshless methods, EEG signal analysis, and e-learning tools for impaired individuals. He has participated in major research projects such as EC Network (1992-1995), PRIN initiatives (1997-2010), and contributed to software development for geomechanical models and air quality systems. His work spans computational fluid dynamics, robotics applications, and cognitive studies. Recent publications highlight advancements in mesh refinement strategies, robotic assistive devices, and spatial cognition research. He has reviewed for prestigious journals and served in academic committees for state examinations and international conferences. He maintains active involvement in academic service, including roles in evaluation committees and contributions to professional societies like SIAM and CICAP.

Remco HartkampView profile

Associate Professor

• Fluid Dynamics
• Electrochemistry
• Nanofluidics
• +4 more

Remco Hartkamp is an Associate Professor at Delft University of Technology (TU Delft), leading the Interfacial Fluid Phenomena Group within the Process & Energy Department of the Faculty of Mechanical Engineering. His academic journey includes a PhD from University of Twente and Swinburne University of Technology (2013), an MSc in Mechanical Engineering from University of Twente (2009), and a BSc in Mechanical Engineering (2007). Prior to his current role, he held postdoctoral positions at Vanderbilt University and MIT. His research focuses on interfacial fluid phenomena, combining molecular dynamics, lattice Boltzmann simulations, and experimental techniques to study electrokinetic transport, electrochemical conversion, and energy storage. Key areas include nanofluidic systems, electrolyte behavior in confined environments, and bubble dynamics in electrolysis. Collaborations span institutions like KIT (Germany), Imperial College London, and Marcoule ISC (France). Notable achievements include securing an NWO Vidi grant (2023) and overseeing a dynamic team of PhD and MSc students. Recent projects involve CO₂ electrolysis optimization and laser-induced crystallization. His work has been published in high-impact journals such as ACS Nano , Nature Materials , and Physical Review Letters .

Ferdian JovanView profile

Assistant Professor

• Mobile Robotics
• Multimodal Learning
• Time Series Analysis
• +11 more

Ferdian Jovan is an Assistant Professor (Lecturer) at the University of Aberdeen, affiliated with the Aberdeen - South China Normal University Joint Institute within the School of Natural and Computing Sciences. He is also a visiting research fellow at the Faculty of Engineering, University of Bristol. PhD in Computer Science, University of Birmingham (2019) MSc in Computational Logic, Technische Universitaet Dresden (2014) BSc in Computer Science, Universitas Indonesia (2010) His research focuses on mobile robotics, multimodal learning, and time series analysis , with applications in service robotics, energy, healthcare, and extreme environments. He is particularly interested in AI systems that function efficiently in dynamic, resource-constrained settings. His expertise bridges statistical machine learning and real-world robotic deployment. His recent publications show a strong trend in applied AI for robotics and health , including work on digital biomarkers for Parkinson’s disease, battery health prediction, multirobot windfarm maintenance, and adaptive planning. These works have been presented at top venues like KDD, IAAI, and published in journals such as Autonomous Robots and Journal of Field Robotics. DAAD AInet Fellow for Human Centered AI (2023) Ferdian advises PhD students and is actively involved in research collaborations across the UK and internationally. He has held research positions at the University of Bristol, Royal Holloway University of London, and the University of Oxford. His work often involves interdisciplinary teams and real-world data, emphasizing robust and deployable AI solutions. He is associated with the Department of Computer Science at the Meston Building, University of Aberdeen, and leads research in intelligent systems for extreme and dynamic environments.