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Anna-Mariya OtsetovaView profile

Researcher

• Stochastic Processes
• Partial Differential Equations
• Fluid Dynamics
• +3 more

Anna-Mariya Otsetova is a Doctoral Researcher at the Department of Mathematics and Systems Analysis, Aalto University (School of Science). Her research focuses on stochastic processes, partial differential equations, fluid dynamics, and mathematical analysis. She holds a Master’s degree from Lund University (2023) and a Bachelor’s degree from Lund University (2021). Her work bridges stochastic methods with fluid mechanics and complex analysis, addressing topics like ergodicity in neural fields and capillary wave dynamics. She has published in prestigious journals such as the Journal of Differential Equations and presented at international conferences including the European Congress of Mathematics. She collaborates with institutions globally, evident from her visiting researcher role at Technische Universität München. Education: Master’s Degree (2023): Axisymmetric capillary water waves on cylindrical fluid jets, Lund University Bachelor’s Degree (2021): Properties of the Ahlfors function, Lund University Research Interests: Fluid dynamics, stochastic processes, nonlinear PDEs, and complex analysis. Her recent work explores ergodic properties of stochastic systems and geometric aspects of fluid flows.

• Optimal Transport
• Mathematical Optimization
• Machine Learning
• +3 more

Prof. Bernhard Schmitzer is a Professor at the Institute of Computer Science, University of Göttingen. He leads the Optimal Transport research group and is a member of the Campus Institute Data Science (CIDAS). His work focuses on developing mathematical frameworks and algorithms for optimal transport theory, with applications in data analysis, image processing, and computational mathematics. He teaches courses such as the Journal Club Optimal Transport for Data Analysis , Scientific Writing , and Visualization . His research integrates geometric methods, stochastic systems, and numerical optimization to address complex problems in healthcare imaging and theoretical modeling. Key contributions include advancements in entropic transport kernels, domain decomposition techniques, and applications in medical imaging (e.g., PET and tomography). Despite his extensive publication record, no scientific awards are explicitly mentioned. He collaborates on interdisciplinary projects involving CIDAS and oversees the Optimal Transport research group at the university.

• Inverse Problems
• Data Assimilation
• Applied Mathematics
• +7 more

Roland Potthast is a Professor specializing in applied mathematics and inverse problems with significant contributions to data assimilation and meteorological modeling. His work bridges mathematical theory and atmospheric sciences, with applications in weather prediction and geophysical systems. His research interests include inverse problems, data assimilation, and mathematical modeling of atmospheric dynamics. He has developed and refined methods such as the range test, no-response test, and particle filters for use in both theoretical and operational contexts. His work often involves solving ill-posed problems and improving the accuracy of numerical weather prediction through advanced statistical and filtering techniques. The 15 most recent publications reveal a consistent focus on data assimilation techniques, particularly particle and ensemble filters, applied to meteorological models and inverse scattering problems. There is a strong trend toward nonlinear and non-Gaussian methods, localization strategies, and the integration of remote sensing data into forecasting systems. His work spans both theoretical developments in applied mathematics and practical implementations in operational weather models. The following scientific awards have not been explicitly mentioned in the provided text. Roland Potthast has collaborated extensively with researchers across institutions on data assimilation and inverse problems, though specific details about student advising or grant funding are not available in the current dataset. His publications suggest leadership in developing novel mathematical frameworks for environmental modeling and remote sensing applications. There is no explicit mention of specific laboratories or research teams in the provided text, though his frequent collaborations with institutions such as DWD (German Weather Service) and involvement in operational NWP frameworks suggest integration within large-scale meteorological research consortia.

• Geometric analysis
• Nonlinear partial differential equations
• Conformal geometry
• +3 more

Fengbo Hang is an Associate Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. His research focuses on geometric analysis, nonlinear partial differential equations, and conformal geometry. He holds a Ph.D. from NYU (2001) and a B.S. from Tsinghua University (1993). His work bridges geometric analysis with PDEs, exploring topics like Sobolev mappings, Faddeev knots, and Q-curvature. Education: Ph.D., Mathematics, New York University (2001); B.S., Mathematics, Tsinghua University (1993). Research Interests: Geometric analysis, nonlinear PDEs, conformal geometry, Sobolev spaces, and mathematical physics. His recent work includes sharp inequalities (e.g., Moser-Trudinger-Onofri), geometric variational problems, and Paneitz operator studies. Notable Contributions: Publications in top journals like Communications in Pure and Applied Mathematics and Journal of Differential Geometry . His research emphasizes geometric PDEs and curvature invariants, with applications in topology and mathematical physics.

• Partial Differential Equations
• Dispersive and Wave Equations
• Low Regularity Well-Posedness
• +7 more

Justin Forlano is a Lecturer in the School of Mathematics at Monash University. He holds a PhD in Mathematics from Heriot-Watt University, completed in 2020. He has held postdoctoral positions as a Postdoctoral Research Associate at the University of Edinburgh (2023–2024) and as Hedrick Assistant Adjunct Professor at the University of California, Los Angeles (2020–2023). Education: PhD in Mathematics, Heriot-Watt University, 2016–2020 His research focuses on nonlinear dispersive partial differential equations, particularly in low regularity settings. Key areas include well-posedness, harmonic analysis, Fourier restriction, Strichartz estimates, invariant and quasi-invariant measures, and probabilistic methods in PDEs. He works on equations such as the modified KdV, BBM, fractional nonlinear Schrödinger, and intermediate long wave equations. The recent article trends reveal a strong emphasis on probabilistic and measure-theoretic approaches to nonlinear dispersive PDEs. His work frequently explores the transport of Gaussian and other measures under nonlinear flows, almost sure well-posedness, and long-time behavior of solutions in critical and supercritical regularity regimes. This reflects a deep integration of probability, analysis, and dynamical systems in modern PDE theory. Scientific Awards: No awards listed in the provided text. Advising and Grants: There is no mention of students advised or grants received in the provided information. However, his active research output and collaborations suggest engagement in the academic community. He has published in high-impact journals such as Transactions of the American Mathematical Society , Communications in Partial Differential Equations , and Proceedings of the Royal Society of Edinburgh . Labs and Research Teams: No specific lab or research team is named, but Justin Forlano collaborates extensively with researchers across institutions and countries, including Lorenzo Tolomeo, Kihoon Seong, Andreia Chapouto, Guo Li, Tadahiro Oh, and Didier Pilod. These collaborations span topics in stochastic wave equations, fractional NLS, and integrable systems, indicating active participation in international research networks.

Juan Rios OcampoView profile

Lecturer

• Urban Planning
• Systems Dynamics
• Sustainable Development
• +3 more

Juan Rios Ocampo is a Lecturer in the Department of Management Science at Strathclyde Business School, University of Strathclyde. He holds a BS in Economics from the National University of Colombia, an ME in Systems Engineering from the same institution, and a PhD in Management from the University of New South Wales (UNSW), Sydney, as a Scientia Scholar. His research focuses on policymakers' mental models, urban planning decision-making, and their impact on societal well-being in cities, employing systems dynamics and agent-based modeling. Research Interests: Cross-disciplinary research combining social, economic, and environmental sciences, with applications in urban development, energy security, and the digital divide. Projects include analyzing Colombia’s socio-economic challenges related to energy markets and digital inequality, as well as urban growth strategies in Sydney. Publications: Recent works address urban housing affordability, unintended environmental challenges in urban growth, and renewable energy policy. His articles span journals like Frontiers in Sustainability , Nature Cities , and Renewable and Sustainable Energy Reviews . Professional Activities: Principal Investigator for projects on modeling complex problems and urban policy mapping. Participated in the Cultivating Sustainable Research Cultures Programme (2025). Collaborations include interdisciplinary teams at Colombia’s Ministry of ICT and UNSW. Awards: Scientia Scholar (UNSW).

• Partial Differential Equations
• Nonlinear Analysis
• Elliptic Equations
• +4 more

Marta Latorre Balado is a mathematical researcher affiliated with the Universitat de València, specifically within the Department of Mathematical Analysis. She is associated with the research group EDPNOL (Nonlinear Partial Differential Equations), focusing on advanced topics in PDE theory. She earned her PhD in 2018 with a dissertation on elliptic equations involving the 1-Laplacian operator, supervised by Dr. Sergio Segura de León. Her research lies at the intersection of nonlinear analysis and partial differential equations, particularly those with singularities, gradient dependencies, and variational structures. Her publications reveal a strong focus on existence, uniqueness, and regularity of solutions for equations involving the 1-Laplacian and related operators. Key themes include singular terms, L^1 and Marcinkiewicz data, dynamical boundary conditions, and blow-up behavior in reaction-diffusion systems. Her work contributes to the theoretical foundations of nonlinear PDEs with applications in mathematical physics and image processing. Notable trends in her research include the use of BV (bounded variation) spaces, comparison principles, and self-similar solutions. She frequently collaborates with experts such as Sergio Segura de León, Razvan Iagar, and Ariel Sánchez. Marta has not been awarded any listed scientific prizes in the provided data and has no known advisees. She has co-authored 11 publications since 2017, primarily in reputable journals like Journal of Mathematical Analysis and Applications and Nonlinearity . She is part of an active research team in nonlinear PDEs at her institution, contributing to both theoretical developments and collaborative projects in mathematical analysis.

• Partial Differential Equations
• Nonlinear PDEs
• Nonlocal Equations
• +9 more

Marcos Solera Diana is an Assistant Professor in the Department of Mathematical Analysis at the Faculty of Mathematics, Universitat de València. His research lies at the intersection of partial differential equations, functional analysis, and applied mathematics, with a focus on nonlocal and nonlinear phenomena. His primary research interests include: Nonlinear and nonlocal partial differential equations Gradient flows in discrete and metric spaces Applications to image processing and clustering Geometric flows and inequalities Instability and non-uniqueness in fluid mechanics His recent publications, spanning from 2020 to 2023, demonstrate a consistent focus on PDEs in the context of metric random walk spaces and graphs. These works explore the heat flow, total variation flow, and doubly nonlinear diffusion, contributing to the theoretical understanding of evolution equations in discrete settings. The research trends indicate a strong foundation in analysis and a growing interest in connections with geometry and fluid dynamics. Marcos earned his PhD in 2021 from the Universitat de València with a thesis on Gradient flows in random walk spaces , supervised by Dr. José Manuel Mazón Ruiz and Dr. José Julián Toledo Melero. He is an active member of the EDPNOL (Nonlinear Partial Differential Equations) research group. He has collaborated extensively with researchers such as J. Julián Toledo and José M. Mazón Ruiz, resulting in publications in high-quality journals including Journal of Functional Analysis , Calculus of Variations and Partial Differential Equations , and Advances in Calculus of Variations .

Jose Julian Toledo MeleroView profile

Professor

• Partial Differential Equations
• Nonlocal Diffusion
• Calculus of Variations
• +7 more

Jose Julian Toledo Melero is a Professor in the Department of Mathematical Analysis at the Faculty of Mathematics, Universitat de València. He is a leading researcher in nonlinear partial differential equations, with a focus on nonlocal diffusion, evolution equations, and optimal transport in metric random walk spaces. His work bridges pure and applied analysis, with strong collaborations with mathematicians such as José M. Mazón and Julio D. Rossi. His research interests include: Nonlinear Partial Differential Equations Nonlocal Diffusion and Operators Calculus of Variations and Optimal Transport Evolution Problems in Metric Spaces Geometric Analysis and Minimal Surfaces Doubly Nonlinear and Degenerate Equations The recent articles show a consistent focus on analytical and geometric properties of nonlocal models, particularly in discrete and metric random walk settings. His work frequently appears in top journals such as Calculus of Variations , SIAM Journal on Mathematical Analysis , and Journal of Evolution Equations , often in collaboration with leading experts. The publications reveal a deep engagement with both theoretical foundations and structural analysis of solutions. He has co-authored influential books such as Nonlocal Diffusion Problems (AMS, 2010) and Variational and Diffusion Problems in Random Walk Spaces (Birkhäuser, 2023), which are key references in the field. His research is highly cited, indicating significant impact in the mathematical community. He advises no publicly listed students in the provided data. There is no mention of specific grants or teaching roles, but his extensive publication record and book authorship reflect a sustained and high-level research career. He maintains an active research profile with upcoming events such as the 2025 CIMPA-UCA School on optimal transport and PDEs, suggesting ongoing leadership and contribution to the international mathematical community.

• Numerical Analysis
• Partial Differential Equations
• Fluid Mechanics
• +9 more

Pep Mulet Mestre is a full Professor in the Department of Mathematics at the Faculty of Mathematics, Universitat de València. He is a member of the ANIMS research group, focusing on Numerical Analysis, Images, Multiresolution, and Simulation. University: Universitat de València School: Faculty of Mathematics Department: Department of Mathematics Research Group: ANIMS His primary research interests include numerical analysis, partial differential equations, fluid mechanics, image processing, and high-order finite difference methods. He has made significant contributions to WENO schemes, total variation-based image restoration, and models for sedimentation and traffic flow. The 15 most recent publications highlight a consistent focus on high-order accurate numerical methods, particularly WENO and IMEX schemes, applied to conservation laws, image processing, and biological models. His work bridges theoretical numerical analysis with practical applications in fluid dynamics and epidemiology. He has collaborated extensively with researchers such as Raimund Bürger, Rosa Donat, Antonio Baeza, and David Zorío, resulting in over 60 indexed publications since 1996. Dr. Mulet earned his PhD from the Universitat de València in 1992 with a thesis on local cohomology and duality in non-commutative Gorenstein rings, supervised by Dr. A. Verschoren. He advises graduate students and leads research projects in numerical methods for PDEs and scientific computing, though specific advisees are not listed in the provided text. There is no mention of specific grants or scientific awards in the available information.

Jan-Frederik PietschmannView profile

Professor

• Nonlinear Partial Differential Equations
• Gradient Flows
• Parameter Identification in PDEs
• +3 more

Jan-Frederik Pietschmann is a Professor for Inverse Problems at the University of Augsburg's Faculty of Mathematics, Natural Sciences and Technology. He previously held the same position at TU Chemnitz (2018–2023) and served as a Research Associate at institutions including the University of Münster and TU Darmstadt. University of Cambridge (PhD, Mathematics, 2008–2011) University of Münster (Diploma, Mathematics and Physics, 2008) Habilitation in Mathematics, University of Münster, 2018 His research focuses on the analysis of nonlinear PDEs , gradient flows , and parameter identification in PDEs . Recent work explores applications to cell invasion , pedestrian dynamics , neurite growth , and organic semiconductors . Publications highlight his expertise in optimal transport , cross-diffusion systems , and uncertainty quantification . Notable trends in his 15 most recent articles include Nonlinear PDEs for biological and physical systems Optimal transport on networks and metric graphs Computational methods for inverse problems His current team includes Gianna Götzmann, Carmen Tretmans, and Team Assistant Juliane Krautz. Contact details: jan-f.pietschmann@uni-a.de , +49 821 598-3926.

Jon LeeView profile

Professor

• Mathematical Optimization
• Combinatorial Optimization
• Integer Programming
• +3 more

Jon Lee is the G. Lawton and Louise G. Johnson Professor of Engineering at the University of Michigan's College of Engineering. He previously held faculty positions at Yale University and the University of Kentucky, and served as an adjunct professor at New York University. Before his academic career, he was a Research Staff member at IBM T.J. Watson Research Center where he managed the mathematical programming group. Lee's research focuses on mathematical optimization, particularly combinatorial optimization, integer programming, and maximum-entropy sampling. His work bridges theoretical foundations with practical applications in experimental design, statistical modeling, and computational mathematics. He has made significant contributions to D-optimal design theory, perspective relaxations for nonconvex optimization, and generalized inverse computations. His recent publication trends (2022-2025) show consistent work in maximum-entropy sampling problems, D-optimal design algorithms, convex relaxations for nonconvex optimization, and generalized inverse computations. The articles demonstrate increasing sophistication in handling large-scale optimization problems while maintaining theoretical rigor, with particular emphasis on algorithmic efficiency for real-world applications. Lee has received notable recognition including: INFORMS Computing Society Prize (2010) Fellow of INFORMS (since 2013) As an academic leader, Lee has served as founding Managing Editor of Discrete Optimization (2004-06), currently serves as Co-Editor of Mathematical Programming, and is on editorial boards for Optimization and Engineering and Discrete Applied Mathematics. He chaired the Mathematical Optimization Society (2008-10) and the INFORMS Optimization Society (2010-12). His textbook A First Course in Combinatorial Optimization (Cambridge University Press) and open-source book A First Course in Linear Optimization have become standard references in the field. Lee maintains active research collaborations through his work with the Mathematical Optimization Society and INFORMS, and has participated in significant research programs including the Fall 2017 program on Bridging Continuous and Discrete Optimization at the Simons Institute.

Mireille E. BrouckeView profile

Professor

• Mathematical System Theory
• Hybrid Systems
• Control Theory for Complex Specifications
• +6 more

Mireille E. Broucke is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto's Faculty of Applied Science and Engineering. She holds a PhD in Electrical Engineering and Computer Sciences from UC Berkeley (2000) and has extensive industry experience in control systems at companies like Texas Instruments and Lockheed. Her academic career includes visiting roles at UC Berkeley and the University of Bologna. B.S. (1984), University of Texas at Austin M.S. (1987), University of California, Berkeley PhD (2000), University of California, Berkeley Her research focuses on mathematical system theory , particularly hybrid systems , reach control problems , and geometric control . Recent work explores systems neuroscience , modeling brain processes through control theory. She has developed adaptive internal models and robust parameter adaptation frameworks. Key article trends include adaptive control in neuroscience, reach control theory for robotics, and geometric methods in linear systems. Her collaborations span institutions like TU Delft and the University of Bologna, with applications in quadcopter motion control and multi-robot coordination . Scientific Awards Member of the Institute of Electrical and Electronics Engineers (IEEE) Professional Engineer of Ontario Broucke advises students in control theory , robotics , and hybrid systems . She has led projects at research units like PATH (UC Berkeley) and PARADES (Rome). Her teaching portfolio includes courses on robot control , nonlinear systems , and geometric control theory . She contributes to linear quadratic optimal control and motion planning algorithms .

Emanuele SpadaroView profile

Professor

• Geometric Measure Theory
• Calculus of Variations
• Partial Differential Equations
• +5 more

Emanuele Spadaro is a Full Professor in the Mathematical Department at Sapienza Università di Roma, where he leads advanced research in geometric measure theory, calculus of variations, and partial differential equations. He is the Principal Investigator of the ERC-funded project HiCoS (Higher Co-dimension Singularities), which investigates singularities in minimal surfaces and free boundary problems, particularly in higher co-dimensions. His research focuses on deep analytical and geometric aspects of variational problems. Key areas include the structure of free boundaries, regularity of solutions, minimal surfaces, and obstacle problems—both classical and fractional. His work combines tools from geometric measure theory, nonlinear PDEs, and variational methods to understand the behavior of singularities and geometric evolution processes. The recent publications show a consistent trend toward understanding fine structures in free boundary problems and minimal surfaces, with a strong emphasis on higher co-dimension phenomena. His collaborations with prominent mathematicians such as Camillo De Lellis, Matteo Focardi, and others reflect a high-impact research trajectory in geometric analysis. Emanuele Spadaro has secured significant funding through the European Research Council (ERC) under Horizon 2020 (Grant agreement No. 759229). He currently supervises two PhD students: Luca Di Fazio and Paolo Di Donato, contributing to the next generation of researchers in analysis and geometry. He teaches core mathematics courses including Analisi Matematica 1 and Equazioni alle derivate parziali . His office is located in Room 103 of the 'G. Castelnuovo' Mathematical Department, and he holds weekly office hours by appointment on Mondays from 9:00 to 11:00. Project: HiCoS – Higher Co-dimension Singularities Funding: ERC Starting Grant (2017), Horizon 2020 (Grant No. 759229) Students: Luca Di Fazio, Paolo Di Donato

Ed BuelerView profile

Professor

• Glaciology
• Ice Sheet Modeling
• Computational Mathematics
• +7 more

Ed Bueler is Professor of Mathematics (Applied) in the Department of Mathematics & Statistics within the College of Natural Science & Mathematics at the University of Alaska Fairbanks. His academic work bridges computational mathematics and glaciology, with particular emphasis on ice sheet modeling and numerical methods for partial differential equations. His research interests focus on computational glaciology , specifically ice sheet modeling through the Parallel Ice Sheet Model (PISM) which he has significantly contributed to developing. His work spans continuum mechanics , finite element methods , and high-performance computing implementations for geophysical applications. He actively researches numerical solutions to partial differential equations, particularly using the PETSc framework and Firedrake for fluid dynamics and solid mechanics problems. Bueler's recent publications demonstrate consistent contributions to computational glaciology with emphasis on numerical accuracy, computational efficiency, and high-resolution ice sheet simulations. His work shows strong integration between theoretical mathematics and practical geophysical modeling, particularly in developing stable numerical schemes for complex ice dynamics problems. The evolution of his research shows increasing sophistication in handling coupled systems and parallel implementations. He organizes the UAF Math 692 Graduate Seminar on Fluids and Solids: Mathematics and Numerics, fostering interdisciplinary connections between mathematics, geophysics, and engineering. Bueler has authored the book PETSc For Partial Differential Equations: Numerical Solutions in C and Python which serves as a key resource for computational scientists. As part of the Geophysical Institute at UAF, Bueler collaborates within the SIP (presumably Simulation and Inversion Problems) group, contributing to the university's strength in Arctic research. His teaching portfolio includes advanced courses in real analysis, numerical linear algebra, and specialized topics in computational mathematics.