Find Academic Supervisors

Dr. Hak-Keung Lam is a Reader in the Department of Engineering at King's College London, part of the Faculty of Natural, Mathematical & Engineering Sciences. He holds an IEEE Fellowship and has been a Clarivate Web of Science Highly Cited Researcher since 2018. His research focuses on fuzzy control systems, neural networks, stability analysis, and their applications in biomedical and engineering domains. Education: Dr. Eng. (2000), B. Eng. (1995), both from Hong Kong Polytechnic University. Research Interests: Fuzzy modeling, neural network-based control, computational intelligence, machine learning, and biomedical applications such as ECG/EEG signal classification. His work bridges theoretical advancements with practical implementations in robotics, autonomous systems, and healthcare technology. Publications: Over 480 publications (as of 2023) in top-tier journals and conferences, with a focus on control systems, fuzzy logic, and intelligent systems. Recent work includes fault-tolerant control, cyber-physical systems, and explainable AI. Awards: IEEE Fellow (2019), 1st Place in PhysioNet Computing in Cardiology Challenge (2022). Grants/Projects: Active projects include fuzzy control system stabilization, autonomous robots in healthcare environments, and networked control of robotic systems. Labs/Teams: Center for Robotics Research, contributing to solutions for societal challenges through robot-centric approaches.

• Fuzzy Control Systems
• Neural Networks
• Control Theory
• +4 more

Leif Ristroph is an Assistant Professor of Mathematics at the Courant Institute of Mathematical Sciences , New York University . His research bridges experimental physics and applied mathematics , focusing on fluid-structure interactions in both biological and geophysical contexts. Key research areas include: Biophysical Flows : Aerodynamics of insect flight ( Applied Mathematics Laboratory ), hydrodynamics of fish schooling, and flow-sensing mechanisms via the fish lateral line system Geophysical Flows : Shape evolution during erosion, dissolution patterns in fluid flows, and bubble formation dynamics Recent publications (2019-2015) explore: Flow interactions in flapping swimmers and hovering systems Evolutionary optimization of wing shapes and self-sculpting processes Stability mechanisms in tandem flapping and insect flight His work has been featured in major media outlets like Nature , New York Times , and ScienceDaily . Ristroph's lab at NYU's Applied Mathematics Laboratory combines physical experiments, computational models, and theoretical analysis to study complex fluid-structure interactions.

• Fluid Dynamics
• Fluid-Structure Interactions
• Biophysics
• +6 more

Ralph Blumenhagen is a Senior Researcher and Research Group Leader at the Max-Planck-Institut für Physik (Werner-Heisenberg-Institut) in Munich. He holds the position of Privatdozent at the Ludwig Maximilian University of Munich since 2007. His academic career includes a PhD from the University of Bonn (1994), postdoctoral positions at the University of North Carolina, Institute for Advanced Study in Princeton, Humboldt-Universität zu Berlin, and University of Cambridge. Research Interests: Superstring Theory and its various formulations String model building including heterotic strings, D-brane models, and M/F-theory vacua Flux compactifications focusing on moduli stabilization and string cosmology String phenomenology addressing low energy effective actions, D-brane instantons, and swampland conjectures Non-geometric string backgrounds involving double field theory and non-commutative geometry His publications reveal a strong focus on theoretical foundations of string theory with applications to particle physics and cosmology. Blumenhagen's work often bridges abstract mathematical structures with potential physical implications, particularly in connecting string theory to observable phenomena. Advising: Dr. Blumenhagen has supervised numerous PhD and Master's students, with thesis topics spanning swampland conjectures, string inflation, non-geometric backgrounds, flux compactifications, and D-brane physics. His students have produced significant contributions to string phenomenology and mathematical aspects of string theory. Research Group: He leads a research group at the Max Planck Institute focused on theoretical aspects of string theory, with particular emphasis on connecting string theory to observable physics through model building and phenomenological investigations. The group maintains active collaborations with researchers worldwide.

• Superstring Theory
• String model building
• Heterotic strings
• +13 more

Christian Wald is a Post-doctoral researcher at Technical University Berlin working under Professor Gabriele Steidl, focusing on generative modeling, flow matching, and stochastic processes in machine learning. His research bridges theoretical probability with practical medical imaging applications, particularly in MRI reconstruction and analysis. He completed his PhD at Humboldt University of Berlin in 2017 with a thesis on p-adic quantum groups. His academic journey transitioned from pure mathematics to interdisciplinary machine learning research, reflecting his versatile expertise. Wald's primary research explores generative models through the lens of optimal transport and flow matching, with significant contributions to Wasserstein geometry and conditional distance metrics. His work frequently integrates stochastic processes to enhance medical image reconstruction, demonstrating strong cross-disciplinary impact in both theoretical machine learning and clinical applications. Recent publications highlight innovations in sliced MMD flows, Bayesian OT methods, and uncertainty-aware medical image analysis. Analysis of his 15 most recent publications (2019-2025) reveals a consistent trajectory toward unifying geometric probability with deep learning. Key themes include flow-based generative modeling for medical time-series data, optimal transport applications in image reconstruction, and novel kernel methods for distribution matching. His work spans both foundational theory (e.g., Fisher-Rao curves) and high-impact medical applications (e.g., coronary calcium scoring). No specific scientific awards are documented in the provided text, though his publications appear in prestigious venues including ICLR, IEEE TMI, and Physics in Medicine & Biology. Wald maintains extensive collaborations with the medical imaging group at Technical University Berlin, particularly with Andreas Kofler and Gabriele Steidl. His co-authored works demonstrate consistent contributions to MRI reconstruction pipelines and segmentation frameworks, though no formal advising roles or grant leadership are indicated. Current projects focus on uncertainty quantification in active learning for medical image segmentation. He operates within Gabriele Steidl's research group at Technical University Berlin, which specializes in mathematical imaging and machine learning. The team combines expertise in optimization, probability theory, and deep learning to solve medical imaging challenges, with Wald contributing core algorithmic innovations in generative modeling and stochastic reconstruction.

• Generative Modeling
• Flow Matching
• Stochastic Processes
• +3 more

Mark Van Raamsdonk is a Professor of Physics at the University of British Columbia (UBC). He earned his bachelor's degree in Mathematics and Physics from UBC, pursued a Ph.D. in Physics at Princeton University, and conducted postdoctoral research at Stanford University. His research centers on quantum gravity through the lens of the AdS/CFT correspondence in string theory, exploring connections between quantum information theory and gravitational physics. He investigates how quantum entanglement and other properties of quantum states relate to the structure of spacetime via holographic principles. Scientific Awards: 2014 Canadian CAP/CRM Medal in Theoretical and Mathematical Physics Canada Research Chair Sloan Foundation Fellow

• Quantum Gravity
• String Theory
• AdS/CFT Correspondence
• +2 more

Eddie C. Red is an Associate Professor of Mathematics and Computational Sciences at Morehouse College , where he currently serves as the Interim Dean of the Science, Technology, Engineering, and Mathematics (STEM) Division. He earned his B.S. from Morehouse College (class of 2000) , followed by his M.S. and Ph.D. from Florida Agricultural and Mechanical University . Dr. Red also completed post-doctoral education at Lawrence Berkeley National Laboratory . Interim Dean, STEM Division Former Chair, Mathematics and Computational Science Division Former Chair, Physics & Dual-Degree Engineering Department Dr. Red’s research interests bridge atomic physics, quantum mechanics, and computational modeling , with a focus on: Photoionization cross-sections Bound states in the continuum Velocity map imaging techniques Mathematical formulations for quantum operators His work has resulted in publications in Physical Review A, Communications Physics, and the Journal of Physics B , alongside numerous conference presentations. Dr. Red has led the NuMaSS (Nuclear, Materials, and Space Science) Summer Enrichment Program for K-12 students and directed the Research Experience with Diversification Laboratory , emphasizing student training and research. Scientific awards include: Principal Investigator for Department of Energy National Nuclear Security Administration awards Dr. Red has served on multiple faculty governance committees, including the Admissions Committee , Faculty Grievance Committee , and Faculty Research Committee .

• Computational Physics
• Quantum Mechanics
• Atomic Physics
• +3 more

Francesca Da Lio is a Professor at the Department of Mathematics, ETH Zurich, where she has held a titular professorship since 2014. Her research focuses on nonlinear elliptic and parabolic partial differential equations (PDEs), with applications in stochastic and deterministic optimal control, homogenization, front propagation, and geometric analysis. She has pioneered work on conformally invariant variational problems and nonlocal PDEs, including fractional harmonic maps and stability analysis for critical points. PhD in Mathematics (1998) and Summa Cum Laude Degree in Mathematics (1994) from University of Padova. Her research explores the interplay between nonlinearity and non-locality, particularly in problems arising from geometry, mathematical finance, and physics. She has led major Swiss National Fund (SNF) projects, including grants for geometric analysis and conformally invariant variational theory. Her work on 3-commutators, integrability by compensation, and Morse index stability has advanced the understanding of harmonic maps and elliptic systems. Francesca Da Lio has mentored numerous PhD, postdoctoral, and Master/Bachelor students, including Dominik Schlagenhauf, Jerome Wettstein, and Ali Hyder. She has served on hiring committees for full professorships at ETH Zurich and co-organized international conferences such as 'Recent Advances in Nonlocal and Nonlinear Analysis' and 'Topics in Sub-Elliptic PDEs.' Scientific Awards: Italian Scientific Qualification as Full Professor in Mathematical Analysis (2013). She contributes to editorial boards, including Advances in Calculus of Variations , and participates in academic services like refereeing for SNF projects and international journals.

• Partial Differential Equations
• Mathematical Analysis
• Geometric Analysis
• +2 more

James Massey is a Senior Research Fellow at the University of Cambridge, affiliated with the Department of Engineering under the Energy Group. His research focuses on computational fluid dynamics (CFD), turbulent reacting flows, and hydrogen combustion, supported by funding from Mitsubishi Heavy Industries (MHI). He holds a PhD in Engineering (2015-2019) and an MEng in Mechanical Engineering (2011-2015) from The University of Manchester. PhD in Engineering, University of Cambridge (2015-2019) MEng in Mechanical Engineering, The University of Manchester (2011-2015) His work spans hydrogen combustion , thermo-acoustics , and large eddy simulation (LES) , targeting emissions prediction, flame stabilization, and combustion instability. Key themes include mitigating CO/NOx emissions, analyzing swirl-stabilized flames, and developing skeletal mechanisms for hydrogen-hydrocarbon blends. Recent publications emphasize multi-regime combustion modeling , thermo-acoustic instability analysis , and machine learning applications in LES. His research often involves cross-institutional collaboration with MHI and contributions to combustion physics through DNS and LES frameworks. Sugden Award (2024) for best paper in The Combustion Institute British Section James contributes to teaching as a lecturer for courses like 4A13 Combustion and Engines (2023-2025) and ETB-1 Clean Fossil Fuels (2022-2023). He is a fellow of Robinson College and an active member of the Institute of Physics Combustion Physics Group and The Combustion Institute British Section.

• Computational Fluid Dynamics
• Turbulent Combustion
• Hydrogen Combustion
• +3 more

Louis-Pierre Arguin is a Professor of Mathematics at Baruch College, City University of New York, within the Weissman School of Arts and Sciences. His academic journey includes a Ph.D. in Mathematics from Princeton University, an M.Sc. in Physics from the University of Montreal, and a B.Sc. in Mathematics from the same institution. His research bridges probability theory, number theory, and statistical mechanics, with a focus on extreme value statistics of the Riemann zeta function and spin glass systems. Key areas include logarithmically correlated random fields, branching random walks, and connections between random matrix theory and number-theoretic functions. His work demonstrates how probabilistic methods can solve deep problems in analytic number theory, particularly regarding the distribution of extreme values of the zeta function on the critical line. Arguin's scholarly impact is reflected in publications in premier journals including Annals of Mathematics , Communications on Pure and Applied Mathematics , and Probability Theory and Related Fields . His research program explores the Fyodorov-Hiary-Keating conjecture, large deviations of Selberg's central limit theorem, and disorder chaos in spin glasses, revealing profound connections between number theory and statistical physics. Award for Excellence in Scholarship (Weissman School, 2022) Bourbaki Seminar Presentation (2019) Andre-Aisenstadt Prize (CRM Montreal, 2015) His mentorship includes supervising doctoral dissertations through Baruch's Mathematics Ph.D. program, while his grant portfolio features multiple National Science Foundation awards, including a CAREER grant focused on statistics of extrema in complex systems. He actively serves on departmental and university committees including the Executive Committee of the Mathematics Department and the Real Analysis Qualifying Exam Committee. Arguin maintains a robust research group collaborating with institutions worldwide and frequently presents at major international venues including the Institute for Advanced Study, Princeton University, and the Centre de Recherches Mathématiques in Montreal.

• Probability Theory
• Number Theory
• Statistical Mechanics
• +5 more

Herman L. Verlinde is the Class of 1909 Professor of Physics and current chair of the Department of Physics at Princeton University. He earned his Ph.D. in theoretical physics from the University of Utrecht and previously taught at the University of Amsterdam (1995–1998). His leadership role underscores his active engagement in academic governance. Verlinde's research bridges string theory , black hole physics , and quantum information . His recent work explores quantum aspects of black hole horizons, entanglement dynamics, and holographic principles. Core interests include: Quantum gravity and string theory formalisms Black hole information paradox Topological field theory applications Cosmological implications of quantum entanglement His publications consistently focus on high-energy theoretical physics, with recurring themes of holography (AdS/CFT), quantum gravity, and string-theoretic approaches to particle physics. Recent articles emphasize quantum error correction in black holes and gauge-gravity duality. Scientific Awards: IBM Einstein Fellow, Institute for Advanced Study PIONIER Fellowship (Netherlands Organization for Science) Fellow, Royal Dutch Academy of Sciences Alfred P. Sloan Research Fellowship He currently advises doctoral students including Dongyeob Kim, Tommaso Marini, and Damiano Tietto. His research group explores quantum gravity and string theory at Princeton's Jadwin Hall.

• String Theory
• Black Hole Physics
• Quantum Gravity
• +4 more

Prof. Radek Erban is a Professor of Applied Mathematics at the Mathematical Institute , University of Oxford. He is affiliated with the Oxford Centre for Industrial and Applied Mathematics and works across interdisciplinary fields including Mathematical Biology, Stochastic Processes, and Reaction-Diffusion Systems. His research focuses on: Multiscale modeling of biological and chemical processes Stochastic simulation algorithms for reaction-diffusion systems Mathematical analysis of collective behavior in biological systems Computational methods for chemotaxis and gene regulatory networks Partial differential equation models for biological phenomena Recent work explores multi-resolution simulations of ions, morphogen gradient modeling, and hybrid numerical methods for stochastic processes. His publications span journals in applied mathematics, computational biology, and physical sciences. Current projects involve bridging atomistic and continuum models for chemical systems.

• Applied Mathematics
• Mathematical Biology
• Multiscale Modelling
• +5 more

James C. Gee is a Professor of Radiologic Science in Radiology at the University of Pennsylvania's Perelman School of Medicine. He serves as Director of the Penn Image Computing and Science Laboratory and Co-Director of the Translational Biomedical Imaging Center , with affiliations in Bioengineering and Applied Mathematics graduate groups. His research focuses on biomedical image analysis, specialization in segmentation, registration, and morphometry applied to neurodegenerative diseases and multi-organ systems. Education : B.S. in Computer Science/Electrical Engineering (University of Washington, 1987), Ph.D. in Computer and Information Science (University of Pennsylvania, 1996) Research : Quantitative medical imaging methods, brain connectomics, neurodegeneration mapping, and translational imaging technologies Publications : 15+ recent works on AI-driven image analysis for Alzheimer's disease, cardiac amyloidosis, and radiomics applications Leadership : Directs MSE-DS Online Degree Program, co-chairs Radiology DCOAP Committee, and founded RISE (Radiology Initiative to Support Inclusive Excellence) His laboratory develops advanced computational tools like ITK-SNAP for biomedical imaging, with applications in both in vivo clinical imaging and ex vivo histology . The work spans cross-disciplinary collaborations in computer science, neuroscience, and clinical medicine.

• Biomedical Image Analysis
• Medical Imaging Computing
• Image Segmentation
• +5 more

Associate Professor Jiakun Liu (FAustMS) is affiliated with the School of Mathematics and Statistics, University of Sydney . He holds a BSc from Zhejiang University (2006) and a PhD from the Australian National University (2010). Following a Simons Postdoctoral Fellowship at Princeton (2010-2013), he served as Lecturer, Senior Lecturer, and Associate Professor at the University of Wollongong (2013-2024), securing an ARC DECRA in 2014 and an ARC Future Fellowship in 2024. Specializes in nonlinear elliptic/parabolic PDEs with applications in geometry and optimal transportation Research focuses on Monge-Ampère/Hessian equations , regularity theory, and geometric flows Contributions to convex geometry , minimal surfaces, and stochastic PDEs . His 2024-2023 publications in Communications on Pure and Applied Mathematics , Advanced Nonlinear Studies , and Archive for Rational Mechanics demonstrate expertise in free boundary regularity , noncompact Minkowski problems , and global geometric analysis . Recognized with ARC Future Fellowship and conferences organized across Australia-China collaborations.

• Nonlinear Elliptic and Parabolic Partial Differential Equ.ations
• Regularity Theory of Monge-Ampère Equations
• Hessian Equations
• +6 more

Michele Dolce is a Lecturer and Scientist at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences (SB) and the Chair of Mathematical Analysis, Calculus of Variations and PDEs (AMCV). He previously held positions as a Postdoc at EPFL and a Research Associate at Imperial College London, where he worked under Prof. Michele Coti Zelati. His academic journey includes a PhD from the Gran Sasso Science Institute. Current affiliation: EPFL, School of Basic Sciences (SB), Department of Mathematics (MATH), AMCV Past affiliation: Imperial College London His research focuses on the mathematical analysis of Partial Differential Equations (PDEs) in fluid dynamics and kinetic theory. Key areas include hydrodynamic stability, long-time behavior of viscous vortex systems, and time-decay properties of kinetic models like the Boltzmann and Wave Kinetic Equations. Recent work explores vortex merging phenomena and Taylor dispersion in rotationally symmetric flows. Scientific activities include organizing workshops such as "Long time dynamics in random and deterministic systems" (2025) and co-organizing events like the "Deterministic and random features of fluids" summer school (2023) and the "Enjoying Probability and Fluids in Lausanne" workshop (2023). His publications span journals including Communications in Mathematical Physics, Archive for Rational Mechanics and Analysis, and Journal of Mathematical Fluid Mechanics. Supported by Swiss National Science Foundation (SNF Ambizione grant PZ00P2_223294) Partially funded by GNAMPA (INdAM group)

• Partial Differential Equations
• Fluid Dynamics
• Kinetic Theory
• +4 more

Qin Li is an Associate Professor in the Mathematics Department at the University of Wisconsin-Madison. She holds affiliations with the Wisconsin Institutes for Discovery and serves as a senior PI at the Institute for Foundations of Data Science. Her research focuses on numerical analysis, scientific computing, and inverse problems, with a strong emphasis on kinetic theory and multiscale PDEs. Her work spans computational methods for inverse transport and radiative transfer equations, Bayesian approaches in optical tomography, and optimization techniques for solving stochastic and deterministic PDEs. Recent publications highlight applications of diffusion models, Wasserstein gradient flow, and random sampling in inverse problems, as well as control theory for Vlasov-Poisson systems and reconstruction of chemotaxis kernels. She leads a research group within the Mathematics Department and has received funding from the National Science Foundation (NSF), the Office of Naval Research (ONR), and the Wisconsin Alumni Research Foundation (WARF). Her lab, Kinetic At Madison, explores nonlinear hyperbolic PDEs and their applications. She also contributes to teaching as a TA Supervisor.

• Numerical Analysis
• Scientific Computing
• Inverse Problems
• +8 more