جست‌وجوی استاد و پژوهشگر

• Aerospace Engineering
• Spacecraft Dynamics
• Orbital Mechanics
• +۵ مورد دیگر

Rafael Vazquez Valenzuela is a University Professor in the Department of Aerospace Engineering and Fluid Mechanics at the Higher Technical School of Engineering, University of Seville. His research focuses on control systems for aerospace applications, particularly in spacecraft dynamics, orbital mechanics, and partial differential equation (PDE) control systems. Professor Vazquez Valenzuela's research spans several key areas in aerospace engineering and control theory. His primary interests include spacecraft guidance and navigation, particularly for asteroid exploration and rendezvous operations. He has made significant contributions to the field of backstepping control for PDE systems, with applications ranging from fluid dynamics to spacecraft control. His work also extends to stochastic analysis of aircraft performance, thermoacoustic instability control, and the development of advanced algorithms for unmanned aerial vehicles. Notably, his research bridges theoretical control theory with practical aerospace applications, resulting in numerous high-impact publications in top journals. His publication record shows a clear trend toward increasingly sophisticated control algorithms for complex aerospace systems. Recent work focuses on predictive control for spacecraft operations near asteroids, chance-constrained optimization for halo orbit rendezvous, and prescribed-time control methods. His research consistently addresses the challenging intersection of theoretical control design and practical implementation constraints in aerospace applications. Professor Vazquez Valenzuela has been involved in numerous research projects, including "APPLICATION OF LEADING TECHNOLOGY TO UNMANNED AERIAL VEHICLES FOR RESEARCH AND DEVELOPMENT IN ATM (ATLANTIDA)" "Diseño de Algoritmos de Guiado y Control Innovadores para Aplicaciones Avanzadas de Rendezvous: Órbitas Halo y Exploración de Asteroides" "SESAR WP-E ComplexWorld Network" These projects highlight his expertise in both theoretical control systems and practical aerospace applications. He is affiliated with the INGENIERÍA AEROESPACIAL (GIA) research group at the University of Seville, where he collaborates with researchers on advanced control and navigation systems for autonomous aerospace vehicles.

Sourav Duttaمشاهده پروفایل

پژوهشگر ارشد

• Model Order Reduction
• Computational Fluid Dynamics
• Scientific Machine Learning
• +۴ مورد دیگر

Sourav Dutta is a Research Fellow at the Oden Institute for Computational Engineering & Sciences at the University of Texas at Austin, where he works with Clint Dawson in the Computational Hydraulics Group. Prior to joining UT Austin in October 2022, he was an ORISE postdoctoral fellow at the Coastal & Hydraulics Laboratory of the U.S. Army Engineer Research and Development Center (ERDC) from September 2017 to August 2022. Dr. Dutta's research lies at the intersection of physics-based computational methods and data-driven machine learning techniques for environmental flow problems. His work focuses on developing efficient numerical approximations by combining physical principles with modern machine learning algorithms. His primary research areas include: Model Order Reduction Computational Fluid Dynamics Scientific Machine Learning Uncertainty Quantification Applied Mathematics His publication record reveals a strong trajectory in reduced order modeling for environmental hydrodynamics, particularly for advection-dominated problems that challenge traditional numerical methods. His research has evolved from foundational work on hybrid numerical methods for porous media flows to cutting-edge applications of deep learning and neural ODEs in computational hydraulics. Dr. Dutta has presented his research at major venues including SIAM conferences, Computational Methods in Water Resources meetings, and the HydroML Symposium. In September 2022, he co-organized a minisymposium on Machine Learning and Data-Driven Methods for Forward and Inverse Problems at the SIAM Mathematics of Data Science meeting. His educational background includes a PhD in Mathematics from Texas A&M University (2017), supervised by Dr. Prabir Daripa, and an Integrated BSc & MSc in Mathematics & Computing from the Indian Institute of Technology, Kharagpur (2010). At the Oden Institute, Dr. Dutta is currently working on compound flood simulation capabilities within Adaptive Hydraulics (AdH), reduced order modeling for coastal engineering applications, and physics-based operator learning frameworks for environmental flows. He has also developed pyNIROM, an open-source Python package for non-intrusive reduced order modeling of time-dependent problems.

Vladimir Rokhlinمشاهده پروفایل

استاد

• Fast Algorithms
• Randomized Algorithms
• Numerical Harmonic Analysis
• +۳ مورد دیگر

Vladimir Rokhlin is the Arthur K. Watson Professor of Computer Science at Yale University, with additional appointments in Applied & Computational Mathematics and Mathematics. His research focuses on fast deterministic and randomized algorithms for computational mathematics, numerical harmonic analysis, and numerical linear algebra. He holds a Ph.D. from Rice University and an M.S. from the University of Vilnius. Key research interests include the development of efficient algorithms for solving integral equations, prolate spheroidal wave functions, and high-accuracy numerical methods. His work has led to breakthroughs in fast multipole methods and low-rank matrix approximations. Rokhlin has been recognized with prestigious awards such as the 2001 Leroy P. Steele Prize, membership in the National Academies of Sciences and Engineering, and the 2014 William Benter Prize. His recent publications emphasize advancements in quadrature formulas, scattering problems, and spectral methods for partial differential equations. His contributions bridge theoretical mathematics and computational science, with applications in electromagnetics, signal processing, and engineering. Current efforts include optimizing algorithms for complex geometries and improving the efficiency of numerical linear algebra techniques.

Hermann Eberlمشاهده پروفایل

استاد

• Mathematical Biology
• Engineering Mathematics
• Applied Differential Equations
• +۵ مورد دیگر

Hermann Eberl is a Professor in the Department of Mathematics & Statistics at the University of Guelph, affiliated with the College of Engineering and Physical Sciences. His research focuses on Mathematical Biology and Engineering, emphasizing modeling, analysis, and simulation of biological systems and bio/environmental engineering processes using differential equations and numerical methods. He leads the Computational Biomathematics Laboratory, which houses a diverse research group of undergraduates, graduate students, and postdocs. Key research areas include microbial biofilms, honeybee health dynamics, agricultural systems, and environmental bioprocesses. His work is supported by funding agencies such as NSERC, CFI, and OMAFRA. Students under his supervision have excelled in interdisciplinary journals and secured careers in academia, industry, and government. Dr. Eberl holds full supervisory privileges in the Mathematics and Biophysics graduate programs and actively mentors students pursuing MSc/PhD degrees. His lab is located in MacNaughton Building, with a focus on integrating mathematical modeling with real-world applications in biotechnology and environmental engineering. Research outputs span biofilm reactor modeling, quorum sensing mechanisms, and gut microbiome interactions, with notable contributions to the ADM1jl computational tool for anaerobic digestion simulations. Collaborations emphasize interdisciplinary problem-solving, reflecting his commitment to bridging mathematical theory with practical engineering challenges.

Hye-Won Kangمشاهده پروفایل

دانشیار

• Stochastic Modeling
• Mathematical Biology
• Biochemical Reaction Networks
• +۷ مورد دیگر

Hye-Won Kang is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). Her research focuses on stochastic modeling of biochemical systems, particularly in understanding complex biological processes such as enzyme clustering, microRNA signaling pathways in lung cancer and COPD, and multiscale reaction-diffusion systems. She holds a Visiting Research Fellowship from Merton College, University of Oxford. Her work integrates mathematical analysis, simulation, and experimental data to address challenges in systems biology. Key research areas include: Multiscale approximations for stochastic chemical networks Stochastic analysis of reaction-diffusion processes Modeling enzyme kinetics in metabolic diseases Gene regulatory networks and pattern formation Recent contributions include studies on glucosome condensate formation (2024), chemical systems with limit cycles (2023), and the role of microRNAs in lung cancer (2013). Her research has been published in high-impact journals such as Physical Biology , Bulletin of Mathematical Biology , and SIAM Multiscale Modeling and Simulation . She has advised PhD student Luan Chip Nguyen and has taught advanced courses in mathematical biology, statistics, and differential equations at UMBC since 2013.

• Applied Analysis
• Celestial Mechanics
• Mathematical Physics
• +۵ مورد دیگر

Harry Gingold is a Professor in the Department of Mathematics at West Virginia University, part of the Eberly College of Arts and Sciences. His research focuses on Applied Analysis, Celestial Mechanics, Mathematical Physics, Dynamical Systems, and Factorization of Power Series. He has contributed to foundational aspects of Geometry and Nonlinear Differential Equations. His research interests include the mathematical consistency of celestial mechanics equations, traveling wave solutions, symmetric nonlinear systems, and factorization methods for Taylor series. Recent work explores time-symmetric orbital dynamics and the implications of Newtonian mechanics in cosmological models. Key contributions span over four decades, with publications addressing differential systems, matrix factorization, and geometric approaches to inner product spaces. His work bridges pure mathematics with applications in physics and engineering. Dr. Gingold has advised no formally listed students but has authored over 100 publications. His research has been supported through institutional affiliations without explicit mention of grants. He is affiliated with the School of Mathematical and Data Sciences at WVU.

Michael Bainesمشاهده پروفایل

مدرس

• Numerical Analysis
• Computational Fluid Dynamics
• Finite Element Methods
• +۲ مورد دیگر

Michael Baines is an Associate Lecturer in the Department of Mathematics and Statistics at the University of Reading, affiliated with the School of Mathematical, Physical and Computational Sciences. His research focuses on developing advanced numerical methods for solving complex physical problems, particularly in fluid dynamics and environmental modeling. His educational background and professional appointments include: Visiting Professor at University of Leeds Former Director of the Institute of Computational Fluid Dynamics (ICFD) Research interests span: Computational fluid dynamics with emphasis on moving mesh methods Numerical symmetry preservation techniques Finite element applications in nonlinear systems Environmental transport phenomena modeling His recent publications demonstrate a consistent focus on adaptive numerical methods for environmental and physical systems, with evolving applications in climate science and multiphase systems. The work shows increasing sophistication in handling coupled physical phenomena through innovative computational frameworks. Laboratory affiliations: Numerical Analysis and Computational Modelling research group

Talitha Washingtonمشاهده پروفایل

استاد

• Applied Mathematics
• Dynamical Systems
• Nonstandard Finite Difference Schemes
• +۷ مورد دیگر

Talitha Washington, Ph.D., is a Professor of Mathematics and Executive Director of the Center for Applied Data Science and Analytics (CADSA) at Howard University. She also holds the Sean McCleese Endowed Chair in Computer Science, Race, and Social Justice and serves as co-Chair of the President's AI Advisory Council. Her work bridges mathematical research, data science, and social equity, with leadership roles extending to the NSF National Data Science Alliance and the Association for Women in Mathematics. Dr. Washington earned her B.S. in Mathematics from Spelman College, followed by M.S. and Ph.D. degrees from the University of Connecticut. She also received an honorary Doctor of Science from UConn. Her academic journey includes faculty positions at The College of New Rochelle, University of Evansville, and Clark Atlanta University, along with significant leadership roles at the National Science Foundation. Her research spans applied mathematics, dynamical systems, nonstandard finite difference schemes, and the ethical development of artificial intelligence. She is deeply committed to equity in STEM, focusing on inclusive data science education and the responsible use of AI in addressing societal challenges. Her recent publications explore AI ethics, discrete heat equations, image encryption, and curriculum design. Recent publications reflect a strong interdisciplinary focus, combining mathematical rigor with real-world applications in AI ethics, health equity, and engineering systems. Her work emphasizes dynamic consistency in numerical modeling and promotes fairness and diversity in technology development. 2019 BEYA STEM Innovator Award 2019 Outstanding Faculty Award from Howard University 2020 NSF Director’s Award for Superior Accomplishment Fellow of the American Mathematical Society (AMS) Fellow of the Association for Women in Mathematics (AWM) Fellow of the American Association for the Advancement of Science (AAAS) Fellow of the African Scientific Institute (ASI) Elected to Phi Beta Kappa and Sigma Xi Dr. Washington has advised major national initiatives including the NSF’s first Hispanic-Serving Institutions Program, which awarded $85 million in grants, and served as Program Director at NSF in the Division of Undergraduate Education and the Convergence Accelerator. She was the inaugural Director of the Atlanta University Center Data Science Initiative and leads the NSF National Data Science Alliance. Her grants and leadership focus on expanding access to data science for underrepresented communities and fostering interdisciplinary collaboration. She leads the Center for Applied Data Science and Analytics at Howard University, a hub for research and education that leverages data to address societal inequities. She also contributes to national advisory bodies such as the U.S. Census Bureau’s Scientific Advisory Committee and the AI Advisory Council at Howard. Her work fosters collaboration across institutions, particularly among HBCUs, to build a diverse talent pipeline in AI and data science.

• Sensor Informatics
• Medical Technology
• Machine Learning
• +۴ مورد دیگر

Fatemeh Yaghoobi is a Doctoral Researcher at Aalto University, affiliated with the Department of Electrical Engineering and Automation under the College of Engineering. She actively contributes to Sensor Informatics and Medical Technology research groups. Research Interests: Her work focuses on algorithm development for state estimation in nonlinear systems, leveraging Bayesian statistics and parallel computing. Key areas include probabilistic numerical methods, Kalman smoothers, and optimization techniques for machine learning applications. Publication Trends: Recent research highlights advancements in parallel-in-time computing for ODE solvers, statistical linear regression for state-space models, and iterative Kalman smoother algorithms. These publications reflect interdisciplinary applications in machine learning, signal processing, and computational mathematics. Contact: Email: fatemeh.yaghoobi@aalto.fi

Bina J. Patelمشاهده پروفایل

استاد

• Ocular Disease
• Primary Eye Care
• International Optometry
• +۳ مورد دیگر

Bina J. Patel, OD, FAAO is a Professor in the Biomedical Sciences department at the New England College of Optometry (NECO) and serves as Director of International Programs. She also works as an Attending Optometrist at Joslin Diabetes Center’s Beetham Eye Institute. Patel holds a Doctor of Optometry (OD) and BS in Optometry from Indiana University, with additional residency training in Ocular Disease from Northeastern State University. Academic Focus: Ocular Disease, Diabetic Eye Health, Teleophthalmology Education: OD & BS in Optometry (Indiana University), Residency in Ocular Disease (Northeastern State University) Her research emphasizes diabetic eye care integration and telehealth imaging technologies , particularly through international collaborations. She has contributed to publications on comparative diagnostic methods and clinical efficiency in diabetic retinopathy screening. Patel has received numerous honors including the Foster Namias Award for Excellence in Didactic Instruction (2022), multiple Carroll Martus Awards (2020, 2019, 1992, 1991), and the AAO-Essilor Award (2021) for international contributions. She has been recognized as Instructor of the Year at NECO for over a decade. Professional Memberships: Fellow of American Academy of Optometry (AAO), Massachusetts Society of Optometry (MSO), VOSH International Leadership: Director of International Programs at NECO, active in global optometry advancement

Thomas Izginمشاهده پروفایل

پژوهشگر

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

Thomas Izgin is a researcher at the Institute of Mathematics, University of Kassel. He specializes in numerical analysis, focusing on the development and stability analysis of time integration schemes for differential equations. His work includes contributions to Modified Patankar-Runge-Kutta (MPRK) methods, positivity-preserving algorithms, and symbolic computation techniques like Pommaret bases for involutive Gröbner bases. Teaching: Courses on numerical methods for PDEs, ordinary differential equations, and mathematical modeling across multiple semesters, including block seminars on positivity-preserving integrators. Research: Investigates stability, convergence, and error analysis of numerical schemes, with applications to hyperbolic conservation laws, production-destruction systems, and biomathematics. Also contributes to computational physics through atomistic-continuum modeling of nanoparticle synthesis.

Fruzsina Agocsمشاهده پروفایل

استادیار

• Scientific Computing
• Numerical Analysis
• Computational Physics
• +۴ مورد دیگر

Fruzsina Agocs is an Assistant Professor in Computer Science at the University of Colorado, Boulder. Her research focuses on scientific computing, numerical analysis, and computational physics, particularly developing high-order numerical methods for ODEs/PDEs and early-universe physics. Previously, she was a research fellow at the Flatiron Institute's Center for Computational Mathematics and earned her PhD in cosmology from the University of Cambridge under advisors Anthony Lasenby, Mike Hobson, and Will Handley. She holds an MSci + BA in Physics. Research Highlights: Developed adaptive spectral solvers for oscillatory ODEs (e.g., riccati and oscode libraries). Advanced methods for scattering from periodic boundaries using integral equations. Investigated quantum initial conditions in curved spacetimes for inflationary cosmology. Explored computationally intensive models of closed universes and kinetic dominance in cosmology. Education: PhD in Cosmology, University of Cambridge (2021). MSci + BA in Physics, University of Cambridge. Software Contributions: Lead developer of riccati (adaptive oscillatory ODE solver). Core contributor to GAMBIT , a particle physics/cosmology collaboration tool. Outreach: Volunteer with NYC Audubon's Project Safe Flight to reduce bird-window collisions. Public science talks and articles on topics like meteor showers (in Hungarian).

Hang Zhouمشاهده پروفایل

پژوهشگر ارشد

• Functional and high-dimensional data analysis
• Complex-structured data analysis
• ODE/PDE modeling
• +۳ مورد دیگر

Hang Zhou is a Post-Doctoral Scholar at the University of California, Davis, specializing in advanced statistical and computational methodologies. His research focuses on functional and high-dimensional data analysis, complex-structured data including manifold and Wasserstein types, and ODE/PDE modeling. He is affiliated with the Mathematical Sciences Building at UC Davis. His research interests encompass functional data analysis, high-dimensional statistics, and the integration of differential equations into modeling frameworks. He explores complex-structured data such as manifold and Wasserstein geometries, advancing methodologies for analyzing distribution-valued processes and error detection in numerical data. Zhou’s recent work bridges machine learning and statistical theory, exemplified by studies on deep regression for repeated measurements and event-attended graph ODE frameworks. His research also emphasizes conformal inference and optimal transport representations, reflecting a focus on robust statistical methods and interdisciplinary applications in data science. As a postdoctoral researcher, Zhou has not yet listed advisees or grants in the provided information. His website ( https://hg-zh.github.io/ ) provides additional details on his ongoing projects and collaborations.

Reza Sheikhiمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Turbulence
• Combustion
• +۴ مورد دیگر

Dr. Reza Sheikhi is a Professor In Residence and Associate Director at the School of Mechanical, Aerospace and Manufacturing Engineering at the University of Connecticut. He joined the Mechanical Engineering Department in 2018 and brings extensive expertise in computational fluid dynamics, turbulence modeling, and combustion simulation. Dr. Sheikhi's educational background includes: Ph.D. in Mechanical Engineering from University of Pittsburgh M.S. in Mechanical Engineering from State University of New York at Buffalo B.S. in Mechanical Engineering from Sharif University of Technology, Iran Dr. Sheikhi's research focuses on computational modeling of complex fluid flows and combustion processes. His work integrates advanced numerical methods with physics-based modeling to address challenges in turbulent reacting flows, particularly under extreme conditions such as supercritical pressures. He has made significant contributions to the development of filtered density function methods for large eddy simulation, entropy transport analysis in turbulent combustion, and the application of machine learning techniques to accelerate chemical kinetics computations. His research has broad applications in energy systems, propulsion, and environmental technologies. Analysis of Dr. Sheikhi's recent publications reveals a strong trend toward integrating machine learning with traditional computational fluid dynamics methods. His work increasingly focuses on developing data-driven frameworks for computationally efficient integration of chemical kinetics, applying deep learning to filtered density functions, and assessing neural ordinary differential equations for combustion simulations. These innovations address the longstanding challenge of balancing computational efficiency with accuracy in modeling complex turbulent reacting flows. Dr. Sheikhi has received significant recognition for his contributions to the field: Students Speak Outstanding Teachers of the College of Engineering at Northeastern University Fellow of the American Society of Mechanical Engineers (ASME) Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA) Dr. Sheikhi serves as an Associate Editor for the ASME Journal of Energy Resources Technology and is an active member of several professional organizations including the American Physical Society (APS), American Society for Engineering Education (ASEE), and the Combustion Institute. His academic career includes previous service as an Assistant Professor of Mechanical Engineering at Northeastern University before joining the University of Connecticut in 2018. Dr. Sheikhi's research group at UConn focuses on developing advanced computational methodologies for turbulent reacting flows, with particular emphasis on entropy generation analysis, supercritical fluid dynamics, and the integration of machine learning techniques with traditional CFD approaches. The team collaborates with researchers across multiple disciplines to address complex challenges in energy conversion systems and propulsion technologies.

• Dynamical systems
• Mathematical biology
• Computational topology
• +۴ مورد دیگر

Konstantin Mischaikow is a Professor of Mathematics at Rutgers, The State University of New Jersey, affiliated with the Department of Mathematics. His research focuses on dynamical systems, computational topology, mathematical biology, and machine learning applications in complex systems analysis. He specializes in rigorously analyzing nonlinear dynamics using topological methods, with emphasis on robotics control, ecological modeling, and parameterized ordinary differential equations. His work combines theoretical rigor with computational techniques, addressing challenges in global dynamics characterization, data-driven modeling, and topological data analysis. Notable contributions include the development of the DSGRN (Dynamic Signatures Generated by Regulatory Networks) database for systematically analyzing regulatory networks. His research often bridges pure mathematics with applied fields like robotics, systems biology, and granular materials physics. Recent studies focus on applying persistent homology to chaotic systems (e.g., Rayleigh-Bénard convection), identifying attractors via machine learning, and quantifying dynamics in high-dimensional spaces. He has collaborated extensively on projects involving granular media, synthetic biology circuits, and spatiotemporal chaos analysis. His academic contributions span over three decades, with a focus on merging topological tools with computational methods to solve real-world problems. While no specific awards are listed, his work has significantly impacted computational dynamics and topological data science.