جست‌وجوی استاد و پژوهشگر

Solesne Bourguinمشاهده پروفایل

دانشیار

• Stochastic Analysis
• Fractional Brownian Motion
• Malliavin Calculus
• +۶ مورد دیگر

Solesne Bourguin is an Associate Professor in the Department of Mathematics and Statistics at Boston University. She is a member of the Probability and Statistics research group. Her research focuses on advanced topics in stochastic analysis, including stochastic dynamical systems, fractional Brownian motion, Malliavin calculus, Stein's method, and free probability theory. She teaches graduate and undergraduate courses such as Probability Theory II and Linear Algebra. Her research interests emphasize the theoretical foundations of stochastic processes, with applications to nonlinear statistics and random matrices. Recent work explores quantitative analysis of stochastic iterative algorithms and Gaussian approximations in high-dimensional settings. She has contributed to understanding fluctuation dynamics in multiscale systems driven by fractional Brownian motion and has published in leading journals like the Electronic Journal of Probability and Stochastic Processes and their Applications. Bourguin's recent publications highlight her expertise in topics like functional Gaussian approximations, moderate deviation principles, and spherical Poisson waves. These studies often combine techniques from Malliavin calculus and Stein's method to derive precise probabilistic estimates. She also investigates the limiting behavior of correlated Wishart matrices in high-dimensional regimes, contributing to random matrix theory. Scientific awards and grants are not explicitly listed in the provided information. She actively participates in academic activities, including organizing Boston University's Statistics and Probability Seminar Series. Her teaching reflects her commitment to both foundational and advanced mathematical education, spanning topics from linear algebra to stochastic processes.

Tomoki Ohsawaمشاهده پروفایل

دانشیار

• Geometric Mechanics
• Hamiltonian Dynamics
• Mathematical Physics
• +۴ مورد دیگر

Tomoki Ohsawa is an Associate Professor of Mathematics at The University of Texas at Dallas. He holds a Ph.D. in Applied Mathematics from the University of Michigan (2010), with prior degrees including an M.S. in Physics (2008), M.A.Sc. in Aerospace Engineering (2005), and B.Eng./M.Eng. in Aeronautics & Astronautics from Kyoto University (2001/2003). His research focuses on geometric mechanics, Hamiltonian dynamics, and geometric control theory, with applications to quantum systems and fluid dynamics. Key research interests include the study of symplectic integrators, vortex dynamics in Bose-Einstein condensates, semiclassical analysis, and stabilization of mechanical systems via controlled Lagrangians. His work bridges pure mathematics (geometric mechanics) with computational methods and engineering applications. Major grants include NSF funding for 'Stabilization of mechanical systems with broken symmetry' ($240,574) and 'Shape Dynamics of Vortices: Theory and Numerics' ($164,965). Awards include the 2020 Notable Paper Award and a Simons Foundation visiting professorship (2015). Teaching responsibilities span advanced topics like Geometric Mechanics, Optimal Control Theory, and courses in calculus and linear algebra. He has advised multiple independent studies on semiclassical dynamics and quantum control.

Yuhua Zhuمشاهده پروفایل

استادیار

• Machine Learning
• Reinforcement Learning
• Optimization Algorithms
• +۴ مورد دیگر

Yuhua Zhu is a tenure-track Assistant Professor in the Department of Statistics and Data Science at the University of California, Los Angeles (UCLA). Her research focuses on the intersection of partial differential equations (PDEs) and machine learning, with particular emphasis on using differential equations to analyze and design efficient reinforcement learning and optimization algorithms. Education: Ph.D. in Mathematics, University of Wisconsin-Madison (2019), advised by Shi Jin M.S. in Mathematics, University of Wisconsin-Madison (2015) B.S. in Mathematics, Shanghai Jiao Tong University (2014) Postdoc at Stanford University (2019–2022), mentored by Lexing Ying Research Interests: Her work spans sequential decision-making (e.g., continuous-time reinforcement learning, multi-armed bandits), optimization (gradient-free methods, asynchronous-SGD, convergence analysis), and uncertainty quantification in kinetic equations (sensitivity analysis, high-dimensional uncertainty). She develops PDE-based frameworks for model-free reinforcement learning and consensus-based optimization methods for distributed learning systems. Recent Work Trends: Her publications emphasize PDE-driven approaches to reinforcement learning, consensus-based optimization in federated learning, and uncertainty quantification in kinetic equations. Recent topics include addressing adversarial attacks in federated learning, high-order discretization in policy evaluation, and stabilization of kinetic equations with reflective boundary conditions. Awards & Grants: No specific awards mentioned in the text. Current recruiting focuses on graduate students in reinforcement learning and optimization for machine learning problems. Labs & Collaborations: Collaborations include work with Jose Carrillo, Shi Jin, Lexing Ying, and others on consensus-based methods and kinetic theory applications in machine learning.

Ana Djurdjevacمشاهده پروفایل

استادیار

• Numerical Analysis
• Stochastic Processes
• Partial Differential Equations
• +۳ مورد دیگر

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.

Tobias Jahnkeمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• Partial Differential Equ
• +۳ مورد دیگر

Prof. Dr. Tobias Jahnke is a faculty member at the Karlsruhe Institute of Technology (KIT) in the Faculty of Mathematics , affiliated with the Institute for Applied and Numerical Mathematics and Research Group 3: Scientific Computing . His office is located at Kollegiengebäude Mathematik (20.30), Room 3.042 , and he offers individual appointments via email at tobias.jahnke@kit.edu . His research focuses on Numerical Analysis and Scientific Computing , particularly for highly oscillatory differential equations , partial differential equations , and stochastic reaction systems . Recent work includes methods for nonlinear Dirac equations , Maxwell equations in heterogeneous media , and dispersion-managed optical systems . Courses taught cover Mathematical Finance , Splitting Methods , and Wavelet Applications . Recent publications emphasize error bounds , high-frequency wave propagation , and ADI schemes . Collaborations include projects on frequency combs and quantum dynamics . He leads seminars and lectures on Scientific Computing , Numerical Methods , and Wavelet Analysis .

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +۳ مورد دیگر

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.

Dipak Deyمشاهده پروفایل

استاد

• Bayesian analysis
• Bioinformatics
• Biostatistics
• +۸ مورد دیگر

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
• +۳ مورد دیگر

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.

Torkel Erhardssonمشاهده پروفایل

دانشیار

• Probability Theory
• Stochastic Processes
• Statistical Inference
• +۷ مورد دیگر

Torkel Erhardsson is an Associate Professor (Docent) in the Department of Mathematics at Linköping University, Sweden. He is affiliated with the Division of Applied Mathematics (TIMA), where he conducts research in probability theory, stochastic processes, and statistical inference. His research interests include: Bounds for distances between probability distributions using Stein's method and couplings Applications to normal and compound Poisson approximations Bayesian nonparametric inference and asymptotics of posterior distributions Higher-dimensional autoregressive processes with random coefficients Reciprocal chains and reciprocal random fields on undirected graphs His recent publications (2008–2023) in journals such as Journal of Applied Probability , Advances in Applied Probability , and IEEE Transactions on Automatic Control reflect a strong focus on theoretical probability and its applications, particularly in approximation methods and stochastic modeling. The work shows increasing emphasis on graphical models and reciprocal processes in recent years. There are no listed scientific awards or honors in the provided materials. Torkel Erhardsson actively contributes to the academic life at Linköping University, participating in seminars in statistics and mathematical statistics. There is no public information indicating student advising, grant funding, or leadership of specific research labs or teams.

• Geometric Measure Theory
• Harmonic Analysis
• Partial Differential Equations
• +۳ مورد دیگر

Xavier Tolsa Domenech is a Researcher at the Department of Mathematics of the Autonomous University of Barcelona (UAB), affiliated with ICREA (Catalan Institution for Research and Advanced Studies). He holds a Ph.D. in Mathematics from UAB (1998) and a Doctor Enginyer Industrial from the Polytechnic University of Catalonia (1995). His research focuses on Geometric Measure Theory, Harmonic Analysis, and Partial Differential Equations, with particular expertise in rectifiability, singular integrals, harmonic measure, and Riesz transforms. He leads projects such as INTEGRALES SINGULARES, TEORIA GEOMETRICA DE LA MEDIDA Y EDP's (2021–2025) and GAPT: Geometric Analysis and Potential Theory (2021–2026). His recent work addresses topics like two-phase harmonic measure problems, solvability in rough domains, and Carleson measure conditions. He collaborates internationally with institutions in Spain, the US, and other countries. His research has been published in high-impact journals like Annals of Mathematics and Journal of Functional Analysis .

• Applied Mathematics
• Statistics
• Strategy
• +۷ مورد دیگر

Prof. Hanqin Zhang is a Professor in the Department of Analytics And Operations at the National University of Singapore (NUS Business School). He holds a Doctor of Science from the Chinese Academy of Sciences (1991) and a Master of Science from Xian Jiaotong University (1985). His research focuses on applied mathematics, statistics, strategy, transportation logistics, control engineering, and numerical methods. He has served as an associate editor for journals like European Journal of Operational Research and TOP , and has reviewed for top-tier journals including Operations Research and Queueing Systems . His recent work addresses optimal inventory policies, queueing systems, and stochastic control, with notable contributions to dual-sourcing strategies, staffing optimization in ticket queues, and fluid models of parallel service systems. He has supervised students in stochastic processes and inventory management, reflecting his expertise in bridging theoretical advancements with practical applications in operations research. Notable research trends include leveraging order-tracking information for inventory systems, analyzing ergodicity in Markov chains, and optimizing multi-source supply chains under uncertainty. His publications span high-impact journals such as Operations Research and Queueing Systems , showcasing a focus on stochastic systems and operations management.

Sijue Wuمشاهده پروفایل

استاد

• Partial Differential Equations
• Fluid Mechanics
• Harmonic Analysis
• +۳ مورد دیگر

Sijue Wu is the Robert W. and Lynne H. Browne Professor of Mathematics at the University of Michigan. Born in 1964 in China, she earned her B.S. (1983) and M.S. (1986) from Beijing University and her Ph.D. (1990) from Yale University. Her academic career includes positions at the Courant Institute (NYU), Northwestern University, University of Iowa, and University of Maryland before joining Michigan in 2009. She has been an invited speaker at the 2002 International Congress of Mathematicians. Her research focuses on partial differential equations and fluid mechanics, particularly water wave equations and free boundary problems. Key contributions include establishing well-posedness for 2D and 3D water wave problems using harmonic analysis and Clifford algebra. She has also studied vortex sheets, self-gravitating fluids, and Muskat interface dynamics. Her awards include the 2001 Satter Prize, Morningside Silver (2001), and Gold Medals (2010), the latter being the first awarded to a woman. Her work spans diverse areas such as Sobolev regularity analysis, nonlinear wave interactions, and geometric fluid dynamics. She has collaborated with researchers like Nathan Totz, Shuang Miao, and Sohrab Shahshahani, advancing mathematical frameworks for fluid interface stability and singularity formation.

Ruihua Liuمشاهده پروفایل

استاد

• Financial Mathematics
• Computational Finance
• Stochastic Optimal Control
• +۵ مورد دیگر

Ruihua Liu is a Professor in the Department of Mathematics at the University of Dayton, College of Arts and Sciences. He has been a full-time faculty member since 2004, achieving tenure as Associate Professor in 2010 and promotion to Full Professor in 2016. Educational Background: Ph.D., Engineering Science (Control Theory and Application), Nankai University, China, 1994 Ph.D., Mathematics, University of Georgia, 2001 M.S., Computer Science, University of Georgia, 2001 M.E., Engineering Science, Nankai University, 1988 B.E., Engineering Science, Nankai University, 1985 Ruihua Liu's research lies at the intersection of financial mathematics and stochastic control. His work emphasizes computational finance , particularly in developing numerical methods such as recombining trees and lattice models for pricing options under complex dynamics. A central theme in his research is the use of regime-switching models to capture structural changes in financial markets. He investigates optimal investment and consumption strategies , often incorporating realistic features like proportional transaction costs. His analytical focus includes stochastic optimal control, optimal stopping, and singular control problems, with applications in portfolio optimization and stock liquidation strategies. The 15 most recent publications show a consistent trajectory in applying advanced stochastic analysis to financial decision-making under uncertainty. Key trends include the development of computational algorithms for regime-switching frameworks, solving optimal control problems with state-dependent switching rates, and modeling market behaviors using multi-scale and diffusion processes. These works span top journals in applied mathematics, control theory, and financial engineering, indicating a strong interdisciplinary impact. Scientific Awards and Honors: No specific awards mentioned in the provided text. Advising and Grants: While no formal list of students is provided, his extensive publication record with multiple collaborators suggests active involvement in mentoring graduate students and junior researchers. Though specific grants are not listed, his research output in high-impact journals implies successful acquisition of external funding to support his work in financial mathematics and stochastic modeling. Laboratories and Research Teams: Ruihua Liu is affiliated with the Department of Mathematics at the University of Dayton. While no named lab or center is mentioned, his collaborative work with researchers such as G. Yin, Q. Zhang, and P. Eloe suggests participation in a research group focused on stochastic systems and financial applications.

Owen Jonesمشاهده پروفایل

استاد

• Operational Research
• Stochastic Modeling
• Environmental Science
• +۷ مورد دیگر

Professor Owen Jones is a Chair in Operational Research at the School of Mathematics, Cardiff University. His work focuses on applying mathematical modeling and optimization techniques to address challenges in environment, energy, and sustainability, including renewable energy systems, water management, and disaster risk quantification. Operational Research Stochastic Modeling Environmental Data Analysis Renewable Energy Optimization His research spans environmental science, hydrology, and computational methods, with recent work leveraging approximate Bayesian computation (ABC) and generative adversarial networks (GANs) to model climate impacts, water systems, and biological dynamics. Articles highlight innovations in wastewater surveillance, bat roost detection, and tidal energy optimization. Professor Jones supervises advanced students in areas like spatio-temporal modeling, emergency response simulation, and multifractal processes. He has secured grants for interdisciplinary projects, including UKRO-funded freshwater solutions, GCRF wastewater monitoring, and EU Horizon2020 climate adaptation initiatives. He is part of the Operational Research group at Cardiff University and has collaborated with institutions such as the Australian Department of Agriculture, Fisheries and Forestry, and the EU Horizon2020 program.

• Risk Theory
• Actuarial Mathematics
• Bankruptcy Theory
• +۴ مورد دیگر

Χατζηκωνσταντινίδης Ευστάθιος is a Professor at the University of Piraeus in the Department of Statistics and Actuarial Science. His academic career spans decades with significant contributions to actuarial mathematics and statistical theory. He teaches undergraduate courses in Actuarial Mathematics, Risk Theory, and Reliability Theory, and postgraduate courses in Generalized Linear Models and Risk Management. Education: PhD in Mathematics (1990) - Aristotle University of Thessaloniki (Grade: Excellent) BSc in Mathematics (1985) - Aristotle University of Thessaloniki (Grade: Excellent) Research Focus: His work centers on risk theory, bankruptcy modeling, compound Poisson processes, and optimal experimental designs. He has developed innovative approaches to: Perturbed risk models with diffusion processes Copula-based dependence modeling in insurance Gerber-Shiu function analysis for dividend strategies Recursive methods for compound distributions His research bridges theoretical mathematics with practical actuarial applications. Publication Trends: Analytical studies of stochastic processes dominate his recent work, focusing on: Ruin probabilities and deficit distributions Dependence modeling using copulas Optimal dividend strategies Computational methods for risk models Earlier publications emphasize experimental design optimization and reliability theory. Administrative Leadership: Chair of Department of Statistics and Actuarial Science (2007-2009) Director of Postgraduate Program in Actuarial Science & Risk Management Member of Senate of University of Piraeus Deputy Chair of Ministry of Finance's Actuarial Examination Committee Research Projects: Has participated in multiple funded projects including: 'Optimal Experimental Designs' (Greek Ministry of Education) 'Modern Model for Actuarial Studies Preparation' (University of Piraeus) EPEAEK programs on statistical applications