Find Academic Supervisors

• Control Systems
• Optimization
• Dynamic Systems
• +7 more

Fedir Garashchenko is a Professor and Head of the Complex Systems Modelling Department at Taras Shevchenko National University of Kyiv. A Laureate of the State Prize of Ukraine in Science and Technology, Honored Worker of Science of Ukraine, and Honored Professor, he leads cutting-edge research in mathematical modeling and control systems. His educational foundation includes Postgraduate studies (1969-1972), Candidate of Science (1972-1975), and Ph.D. in Physical and Mathematical Sciences (1984), all from Taras Shevchenko National University of Kyiv. Garashchenko's research centers on control system modeling, analysis, and optimization with applications spanning dynamic systems stability , beam dynamics in particle accelerators , satellite attitude control , and computational finance . His pioneering work in practical stability theory and structural-parametric optimization bridges theoretical mathematics with real-world engineering solutions, particularly in aerospace and financial systems. Recent publications demonstrate expanding interdisciplinary reach into real-time image processing and adaptive data technologies. His scientific contributions are recognized through Ukraine's highest academic honors, including the State Prize of Ukraine in Science and Technology and the title of Honored Worker of Science. Garashchenko has secured major international grants including CRDF (USA) and STCU (USA/Canada/Sweden/Ukraine) projects, directing teams of approximately 20 PhD and Doctorate researchers. His current work focuses on future-oriented projects in trajectory optimization, microsatellite control systems, and advanced data compression techniques. He leads an active research group developing mathematical methods for accelerating/focusing systems and satellite gravimetry, with applications implemented at institutions including Moscow Radio Technical Institute and Yale University.

David GinsbourgerView profile

Professor

• Statistics
• Machine Learning
• Uncertainty Quantification
• +7 more

David Ginsbourger is a Professor and Head of Research Group at the Institute of Mathematical Statistics and Actuarial Science (IMSV) within the University of Bern, Switzerland. He maintains dual affiliations through his role at IMSV and as a member of the Multidisciplinary Center for Infectious Diseases (MCID), reflecting interdisciplinary engagement across statistical methodology and applied domains. His research program centers on advanced statistical methodologies with emphases on Gaussian process modeling, uncertainty quantification, and experimental design for computer experiments. Key contributions include novel kernel constructions for equivariant systems, sequential design strategies for excursion set estimation, and efficient computational frameworks for spatial distributional modeling. His work bridges theoretical statistics with practical applications in agriculture, chemoinformatics, environmental science, and risk assessment, demonstrating consistent innovation in handling complex prediction problems under uncertainty. Analysis of his 15 most recent publications (2024-2025) reveals persistent methodological development in Gaussian process theory alongside expanding application domains. Recurring themes include integration-free kernel design for structured data, rare event probability estimation, and multivariate forecast calibration. His research exhibits strong continuity in addressing computational challenges for large-scale inverse problems while increasingly incorporating domain-specific constraints from fields like molecular chemistry and agricultural science. Ginsbourger leads a dedicated research group at IMSV focused on advancing statistical frameworks for computer experiments and uncertainty quantification. The group maintains active collaborations across disciplines, particularly evident in recent work connecting statistical methodology to infectious disease modeling through MCID affiliations and agricultural optimization projects.

Remco I. LeineView profile

Professor

• Nonlinear Dynamics
• Nonsmooth Mechanics
• Stability Theory
• +6 more

Remco I. Leine is a Full Professor and Director of the Institute for Nonlinear Mechanics at the University of Stuttgart. He earned his M.Sc. from Delft University of Technology, Ph.D. from Eindhoven University of Technology, and Habilitation from ETH Zurich, where he later became a Titular Professor. His academic journey includes postdoctoral research at Technical University Munich, INRIA Rhône-Alpes, and ETH Zurich. Leine's research interests sit at the intersection of nonsmooth dynamics and nonlinear dynamics, focusing on stability theory for nonsmooth systems, friction-induced vibrations, bifurcations in nonsmooth dynamical systems, and multibody systems with impact and friction. Recent work explores Koopman–Hill stability analysis, nonsmooth integration algorithms, and model reduction techniques for complex mechanical systems. Key trends in his publications include advances in numerical methods (e.g., nonsmooth RATTLE algorithm, generalized-alpha method), stability analysis of periodic orbits, modeling of frictional and impact phenomena, and applications to systems like the tippedisk and vibro-impact energy sinks. His work bridges theoretical developments with practical simulations in fields such as rockfall dynamics and multibody contact problems. Scientific awards include a prestigious Fellowship from the Royal Dutch Academy of Sciences (2002) . Advising highlights numerous current and completed PhD students, such as Paul Haacker, Tianxiang Dai, and Jonas Breuling. He contributes to editorial work as a Topical Associate Editor for Nonlinear Dynamics and a scientific board member for JTCAM . The Institute for Nonlinear Mechanics , which he directs, is a hub for research on nonsmooth dynamical systems. He actively participates in workshops and conferences , including keynote lectures at ENOC 2024 and ECCOMAS 2021, and co-organizes European Network for Nonsmooth Dynamics symposia.

• Medical Data Analysis
• Functional Data Analysis
• Deep Neural Networks
• +4 more

Łukasz Kidziński is a Research Fellow at Stanford University's Clark Center, specializing in medical data analysis for high-dimensional sparse clinical observations. His methodological expertise spans functional data analysis, inverse problems in high dimensions, and deep neural networks, with recent applications in biomechanics and cancer immunology. Previously, he developed adaptive learning systems for educational environments. His academic background includes: Ph.D. in Mathematical Statistics, Université libre de Bruxelles (2014) M.S. in Mathematics, University of Warsaw (2011) M.S. in Computer Science, University of Warsaw (2010) B.S. in Mathematics & Computer Science, University of Warsaw (2009) Dr. Kidziński's research bridges statistical theory and clinical practice, focusing on translating complex data into actionable medical insights. Current projects emphasize smartphone-based human movement analysis (OpenCap system), spatial mapping of immune cells in cancer, and AI-driven diagnostics for osteoarthritis. His work uniquely integrates computational statistics with real-world healthcare challenges, particularly in decentralized clinical trials and biomechanical interventions. Analysis of his 15 most recent publications reveals three dominant trajectories: (1) smartphone-based biomechanics (6 articles, 2021-2023) advancing OpenCap for 3D motion capture; (2) spatial cancer immunology (4 articles, 2022-2024) mapping macrophage niches in colon/breast tumors; and (3) clinical AI applications (5 articles, 2021-2022) for osteoarthritis assessment and decentralized trials. These intersect statistics, computer vision, and translational medicine. His accolades include: Mobilize Center Distinguished Postdoctoral Fellow (2016) Swiss National Science Foundation grant (2015) ULB-UC Berkeley research exchange grant (2014) PASCAL Group Fellowship (2011) University of Warsaw Distinguished Students Fellowship (2007) As a Mobilize Center fellow, Dr. Kidziński leads NIH-funded projects on AI-enhanced clinical trials and biomechanical modeling. His collaborations span Stanford Bio-X, UC Berkeley, and European institutions, with open-source contributions like OpenCap enabling community-driven research. While no formal advisees are documented, his educational data mining work indicates student mentorship in adaptive learning systems. Active Grants: Mobilize Center Distinguished Fellowship, Swiss NSF Key Collaborators: Pierre Dillenbourg (EPFL), Scott Delp (Stanford), David Cohen (EPFL) Based in Stanford's Clark Center, he co-develops the Mobilize Center's mobile health ecosystem, contributing to open-source tools for biomechanical analysis (OpenCap) and knee cartilage assessment. His work leverages Bio-X's interdisciplinary infrastructure to bridge engineering, medicine, and data science for real-world clinical impact.

• Inverse Problems
• Model Reduction
• Partial Differential Equations
• +4 more

Abdallah El Hamidi is a Teacher-Researcher at the University of La Rochelle, affiliated with the CNRS Scientific Department INSIS (Research Section: CNU 26). His core responsibilities include leading the PRES project on Inverse Problems and co-facilitating the 'Model Reduction' research theme within the AB axis. Research Focus: El Hamidi specializes in mathematical methodologies with dual applications: Inverse Problems : Applied to non-linear diffusion coefficient identification in materials mechanics and medical diagnostics (cancerous tumor localization/inflammation detection post-mastectomy). Model Reduction : Developing stabilized computational methods for highly oscillating microstructures. His work intersects Partial Differential Equations, Regularization, and Data Assimilation. Collaborations: He partners with researchers including Marc Garbey, Aziz Hamdouni, Alain Cimetière, Ouali Amiri, and Francisco Chinesta.

• Nonsmooth Optimization
• Inverse Problems
• Image Reconstruction
• +5 more

Tuomo Valkonen is a Professor and Principal Investigator at the University of Helsinki , with significant contributions to mathematical optimization and inverse problems. He serves as the Managing Editor of the Journal of Nonsmooth Analysis and Optimization , advancing methodologies for nonsmooth and nonconvex optimization in imaging and PDE-constrained problems. His research spans Nonsmooth Optimization : Developing linearized block coordinate descent methods and forward-backward splitting in non-Hilbertian spaces. Inverse Problems : Applying these techniques to Electrical Impedance Tomography , Diffusion Tensor Imaging , and Magnetic Resonance Imaging . Optimal Transport : Innovating in unbalanced transport for point source localization and dynamic regularization via infimal convolution. Recent publications highlight his work on Online Optimization for real-time imaging. Multigrid Methods for total variation problems. Differential Estimates in bilevel PDE-constrained optimization. He has received the Magna Cum Laude Award at ISMRM 2013 for dynamic imaging work and authored a textbook on nonsmooth analysis (2024). His methods integrate stochastic and deterministic solvers, emphasizing efficiency in high-dimensional medical and geophysical imaging.

Ayman MoussaView profile

Lecturer

• Mathematical Analysis
• Numerical Analysis
• Partial Differential Equations
• +6 more

Ayman Moussa is a Lecturer at Sorbonne University, affiliated with the Jacques-Louis Lions Laboratory , with a part-time role at the Department of Mathematics (DMA) at École Normale Supérieure (ENS) since 2021. His research focuses on the mathematical and numerical analysis of models in biology, including population dynamics and particle transport . PhD in Mathematics (2009), Sorbonne University (UPMC) HDR (Habilitation à diriger des recherches) in 2018 His work spans cross-diffusion systems , Vlasov-Navier-Stokes coupling , and nonlinear parabolic equations , with a focus on stability, uniqueness, and entropy structures. Recent submitted work includes perturbative solutions for cross-diffusion and revisiting quasi-linear parabolic systems. He has held research grants such as the research investment bonus (2013–2017) and led international projects like the Franco-Japanese PHC Sakura (2019–2022). His teaching includes advanced courses on L p spaces , Sobolev spaces , and Vlasov equations at Sorbonne University, ENS, and institutions abroad. He also co-founded Aromaths , a student seminar at Sorbonne University, and is a passionate advocate for the sport Plumfoot , having served as chair of France Plumfoot and organized world championships.

• Remote Sensing
• Space Physics
• Signal and Image Processing
• +5 more

Professor Farzad Kamalabadi is a faculty member at the University of Illinois at Urbana-Champaign in the Department of Electrical and Computer Engineering . He holds affiliate appointments in the Department of Statistics and has served as Invited Visiting Professor at INRIA/CNRS. His research spans remote sensing, space physics, signal processing, and computational imaging . 2011-present: Full Professor, UIUC 2006-2011: Associate Professor, UIUC 2000-2006: Assistant Professor, UIUC 2009-2010: Invited Visiting Professor at Universite de Nice Sophia Antipolis Research interests include remote sensing , space physics , statistical inference , and switching dynamical systems . His work on graph-based clustering for SLDS has enabled efficient model reduction in high-dimensional systems. Scientific contributions appear in IEEE Signal Processing Letters , The Astronomical Journal , and Radio Science . Notable papers include Bayesian Exoplanet Detection (2020), Optimal Sensor Configuration (2008), and 3D Solar Corona Tomography (2009). 2020: NASA Group Achievement Award for ICON mission 2015: Center for Advanced Study Associate 2006: Xerox Award for Outstanding Faculty Research 2002: NSF CAREER Award Editorial roles : Associate Editor, IEEE Signal Processing Letters (2013-2017) Guest Editor, IEEE Signal Processing Magazine (2009) Teaching includes Digital Signal Processing , Optimization , and Pattern Recognition , with multiple recognitions in the Engineering Council Award and List of Excellent Teachers .

Coralia CartisView profile

Professor

• Numerical Optimization
• Algorithm Design and Analysis
• Nonlinear Optimization
• +6 more

Professor Coralia Cartis holds a Professorship in Numerical Optimization at the University of Oxford's Mathematical Institute, where she also serves as a Tutorial Fellow at Balliol College. Additionally, she is a Turing Fellow at The Alan Turing Institute in London, focusing on advanced optimization research with applications spanning machine learning and climate science. She obtained her PhD in Mathematics from the University of Cambridge in 2005, establishing the foundation for her expertise in algorithmic optimization. Research Focus: Her work centers on designing and analyzing algorithms for linear and nonlinear optimization problems, both convex and nonconvex, with emphasis on complexity theory and dynamical systems connections. Key application areas include compressed sensing, sparse approximation, machine learning, and inverse problems in climate modeling, where she develops methods for ocean biogeochemical model optimization and data assimilation. Publication Evolution: Recent publications (2024-2025) demonstrate expansion into high-order tensor methods and quartic regularization models, while maintaining her foundational work on cubic regularization. Her research consistently bridges theoretical complexity analysis with practical climate science applications, showing increasing interdisciplinary collaboration. Honors: Leslie Fox Prize in Numerical Analysis (2005, second place) Mathematical Programming Computation Journal Best Paper Prize (2019) INFORMS Simulation Society Outstanding Paper Prize (2021) ICM Invited Speaker (2022) EUROPT Fellow (2023) SIAM Fellow (2023) Academic Leadership: She actively contributes to Oxford's Numerical Analysis and Machine Learning research groups, supervises graduate students (though specific names aren't listed), and secures research funding for projects involving climate modeling and optimization theory. Her work with the Alan Turing Institute facilitates cross-institutional collaborations in data science. Research Infrastructure: Her optimization research is conducted within Oxford's Mathematical Institute, leveraging resources from both the Numerical Analysis group and the Machine Learning and Data Science research cluster, with additional computational support through The Alan Turing Institute.

Bin ZouView profile

Associate Professor

• Actuarial Science
• Financial Mathematics
• Stochastic Control and Optimization
• +3 more

Bin Zou is an Associate Professor in the Department of Mathematics at the University of Connecticut. His research focuses on actuarial science, financial mathematics, and stochastic control, addressing critical problems in insurance, reinsurance, and investment strategies. Ph.D. in Mathematical Finance (2015), University of Alberta M.S. (2009) and B.S. (2007) in Mathematics, Beijing Institute of Technology Bin Zou's work explores optimal insurance design, reinsurance games, and portfolio management under uncertainty. His recent studies analyze stochastic differential games for reinsurance chains, mean-variance investment strategies in contagious markets, and hedging mechanisms for cryptocurrency futures. His publications span top-tier journals like Insurance: Mathematics and Economics , SIAM Journal on Financial Mathematics , and European Journal of Operational Research . Key themes include moral hazard in insurance contracts, model ambiguity in risk management, and machine learning applications in actuarial contexts. Casualty Actuarial Society 2022 Individual Grant

• Inverse Problems
• Observer Theory
• Partial Differential Equations
• +7 more

Ghislain Haine is a Lecturer-Researcher in the DISC Department at the Higher Institute of Aeronautics and Space (ISAE-SUPAERO) since April 2013, specializing in applied mathematics with dual focus on teaching and research in control theory and inverse problems for infinite-dimensional systems. His academic background includes: PhD (October 2012): "Observers in infinite dimensions. Application to the study of some inverse problems", supervised by Karim Ramdani and Marius Tucsnak. Haine's research centers on inverse problems and observer theory for linear systems, leveraging mathematical frameworks from functional analysis and partial differential equations. His work addresses critical applications in medical imaging, meteorology, oceanography, and source identification, building on foundational methods like the observer approach introduced by Ramdani, Tucsnak, and Weiss. He actively teaches advanced mathematical courses including functional analysis, harmonic analysis, numerical analysis, and the theory of partial differential equations, emphasizing distributions, operators, semi-groups, and mixed finite element methods to bridge theoretical concepts with computational practice. Haine has co-supervised three doctoral students: Guillaume Delay (defended August 2018, now Lecturer at LJLL), Anass Serhani (defended September 2020, now Post-Doc at CERFACS), and Antoine Bendimerad-Hohl (ongoing since October 2022, co-supervised with Denis Matignon and Laurent Lefèvre). As a member of the Applied Mathematics research group within DISC, he contributes to fluid-structure interaction studies and maintains an ORCID profile (0000-0003-1550-1601) while balancing academic work with nature photography and drawing pursuits.

Clifford NolanView profile

Associate Professor

• Medical Imaging
• Geophysical Imaging
• Radar Imaging
• +5 more

Clifford Nolan is an Associate Professor in the Department of Mathematics and Statistics at the University of Limerick. He is affiliated with the Centre for Research Training in Foundations of Data Science and the Mathematics Applications Consortium for Science and Industry (MACSI). Education : PhD in Global Analysis of Linearized Inversion for the Acoustic Wave Equation from Rice University (1997), Master of Applied Analysis (Rice University, 1993), M.Sc. from Trinity College Dublin (1992), and B.Sc. from National University of Ireland - Galway (1990). His research focuses on Medical, Geophysical, and Radar Imaging , with specialization in Inverse Problems , Wave Propagation/Scattering , and Microlocal Analysis . Recent work includes time-harmonic diffuse optical tomography and microlocal analysis of seismic data. Key trends in his publications include applications of inverse problems to imaging technologies, mathematical analysis of wave equations, and collaborations in geophysical and radar imaging. His work often involves interdisciplinary approaches bridging mathematics with real-world engineering and medical applications. Teaching interests include Partial Differential Equations , Analysis , and Discrete Mathematics .

Achi BrandtView profile

Professor

• Multigrid Methods
• Multiscale Scientific Computation
• Fluid Dynamics
• +13 more

Prof. Achi Brandt is a leading figure in multiscale scientific computation at the Weizmann Institute of Science , where he holds a professorship in the Department of Applied Mathematics & Computer Science. He also serves as Professor in Residence at the Department of Mathematics, University of California, Los Angeles , and is Chief Scientist at VideoSurf , a video search engine leveraging computer vision. Affiliations: Weizmann Institute (Departments of Applied Mathematics & Computer Science) University of California, Los Angeles (Mathematics Department) VideoSurf (Chief Scientist) Research Focus: Multigrid Methods for PDEs Multiscale Algorithms in Physics & Engineering Computer Vision & Image Processing Optimization & Control Theory Quantum Field Theory Simulations Publications and Technical Reports demonstrate expertise in multigrid solvers for fluid dynamics, quantum equations, and computer vision tasks. His work bridges theoretical mathematics and practical applications in data assimilation, tomography, and machine learning. Collaborations: Extensive co-authorships with researchers in computer vision (Sharon, Galun, Basri), fluid dynamics (Thomas, Diskin), and optimization (Ron, Safro) indicate cross-disciplinary impact.

Yulong LuView profile

Assistant Professor

• Mathematical foundations of machine learning and data sciences
• Applied probability and stochastic dynamics
• Applied analysis and PDEs
• +2 more

Yulong Lu serves as an Assistant Professor at the School of Mathematics, University of Minnesota, and holds affiliated faculty status with the Data Science Initiative in the College of Science and Engineering. His research bridges theoretical mathematics and practical applications in data science, with active recruitment of undergraduate and graduate researchers. Lu earned his Ph.D. in Mathematics and Statistics from the University of Warwick under Andrew Stuart and Hendrik Weber. His academic trajectory includes an Assistant Professorship at the University of Massachusetts Amherst (2020-2023) and a Phillip Griffiths Research Assistant Professorship at Duke University (2017-2020) mentored by Jonathan Mattingly and Jianfeng Lu. His research spans mathematical foundations of machine learning , applied probability , stochastic dynamics , applied analysis , PDEs , Bayesian statistics , and uncertainty quantification . Recent work demonstrates deep integration of diffusion models, transformers, and operator learning to solve complex physical systems while establishing theoretical convergence guarantees. Analysis of his 15 most recent publications reveals dominant trends in physics-informed generative modeling for PDEs, in-context learning for dynamical systems, and theoretical analysis of deep learning approximations. His work consistently connects abstract mathematical frameworks with concrete scientific computing applications across fluid dynamics, quantum mechanics, and optimization. No scientific awards were documented in the provided materials. Lu actively mentors researchers through open positions for Ph.D. students (Fall 2026 intake), postdocs via Mathjobs, and UMN internships focused on deep learning theory and scientific applications. His group emphasizes self-motivated collaboration on theoretical and applied challenges in machine learning. He leads a research group developing theory for deep learning applications in scientific computing, with current projects including diffusion-based PDE solvers, transformer architectures for dynamical systems, and uncertainty quantification for inverse problems.

George MakrakisView profile

Professor

• Wave propagation
• Inverse problems
• Asymptotic methods for wave phenomena
• +1 more

George Makrakis is a Professor at the University of Crete, holding a Ph.D. from the National Technical University of Athens (1992). His academic base is within the university's Department structure, though specific school/department names are not detailed in available materials. His research centers on advanced mathematical analysis of wave phenomena, including wave propagation in complex media, inverse problems for wave equations, and asymptotic/semiclassical methods for linear partial differential equations. These approaches address high-frequency wave behavior with applications spanning geophysics, optics, and theoretical physics, emphasizing rigorous analytical frameworks for solving challenging PDE systems. Professor Makrakis maintains active academic operations with office Δ-332 and contact number 39-3708, reflecting ongoing institutional engagement without indication of part-time status or retirement.