جست‌وجوی استاد و پژوهشگر

• Space Plasma Physics
• Solar Physics
• Helioseismology
• +۴ مورد دیگر

Professor Robert von Fay-Siebenburgen is a faculty member at the University of Sheffield, serving as Professor in the Department of Applied Mathematics and Head of the Solar Physics & Upper-Atmosphere Research Group (SPARG). His research focuses on space plasma physics, particularly solar atmospheric heating, MHD waves, and helioseismology. He holds a CSc (Physics) from the Hungarian Academy of Sciences and a PhD from K.U. Leuven, both with Summa cum laude honors. Affiliations: School of Mathematical and Physical Sciences, Department of Applied Mathematics, SPARG Education: 1997 CSc (Physics) with Summa cum laude, Hungarian Academy of Sciences 1996 PhD (Applied Maths) with Summa cum laude, K.U. Leuven 1991 M.A. in History, Eötvös University 1988 M.Sc. in Physics and Astronomy, Eötvös University Research Interests: Solar Physics, Space Weather, Computational Magnetohydrodynamics (CMHD), Helioseismology, Stability of MHD shear flows. His work bridges theoretical models with observational data from satellites like SOHO and TRACE. Notable projects include studying resonant MHD waves, nano-scale reconnection, and coronal seismology. Grants & Awards: Over 30 grants as PI/co-PI, including EU and UKSTC funding. Recipient of top prizes in Hungarian research conferences (1st and 2nd place in 1987). Member of prestigious societies like RAS, AAS, and IAU. Labs/Teams: Leads SPARG and co-leads the Solar Physics and Space Plasma Research Centre (SP2RC). Involved in international collaborations like WISER and ESA/NASA missions.

Fikile R. Brushettمشاهده پروفایل

استاد

• Electrochemical Energy Storage
• Redox Flow Batteries
• Electrochemistry
• +۳ مورد دیگر

Fikile R. Brushett is a Professor and holds the Chevron Chair of Chemical Engineering at the Massachusetts Institute of Technology (MIT), within the Department of Chemical Engineering under the School of Engineering. His research focuses on electrochemical energy storage systems, particularly redox flow batteries, catalyst synthesis, and environmental applications like CO₂ capture. Brushett has received numerous accolades, including the Allan P. Colburn Award and Charles W. Tobias Young Investigator Award for his impactful publications. His work integrates experimental and computational methods to advance energy technologies, emphasizing sustainable materials and system optimization. He leads projects addressing global energy challenges through innovations in battery design, electrolyte development, and process modeling. Education: Ph.D. in Chemical Engineering, University of Illinois at Urbana-Champaign (2010) M.S.E. in Chemical Engineering, University of Illinois at Urbana-Champaign (2009) B.S.E. in Chemical Engineering, University of Pennsylvania (2006) Research: Brushett’s lab explores electrochemical energy conversion/storage, microfluidics, and interfacial phenomena. Key areas include redox flow battery optimization, scalable carbon materials for energy storage, and CO₂ capture via electrochemical methods. His team develops novel diagnostic tools (e.g., microelectrode sensors) and models for system efficiency. Awards: Over two dozen honors, including named chairs and fellowships from organizations like ACS and GEM. Notable recognitions include the 2024 Chevron Chair and 2022 AIChE Colburn Award. Grants & Labs: Active in MIT’s Electrochemical Energy Lab, focusing on grid-scale storage solutions. Collaborates on projects funded by DOE, industry partners (e.g., Chevron), and foundations. Leads initiatives in material sustainability and battery lifecycle analysis. Publications: Over 140 peer-reviewed articles (2022–2025) emphasize interdisciplinary innovations in energy storage, electrolyte design, and environmental electrochemistry. Recent work highlights advancements in non-aqueous systems, CO₂ capture, and Bayesian-optimized material synthesis.

Peter L. Olsonمشاهده پروفایل

استاد پژوهشی

• Geophysics
• Core Dynamics
• Geomagnetism
• +۲ مورد دیگر

Dr. Peter L. Olson is a Research Professor at Johns Hopkins University's Department of Earth and Planetary Sciences. His research examines Earth's deep interior dynamics including core-mantle interactions, geomagnetic field generation, and planetary evolution processes. Research Focus: Combines theoretical models, numerical simulations, and laboratory experiments to study core dynamics, geodynamo processes, and mantle convection. Current investigations include the slow carbon cycle and polar ice shelf dynamics through interdisciplinary collaborations. Publications: Recent work explores geomagnetic reversal mechanisms, core-mantle boundary interactions, and planetary dynamo diversity using advanced computational models and fluid dynamics experiments. Education: Ph.D. from University of California, Berkeley

Alan Kaptanogluمشاهده پروفایل

استاد

• Applied Mathematics
• Scientific Machine Learning
• Nuclear Fusion
• +۳ مورد دیگر

Alan Kaptanoglu is a Professor leading the Plasma Physics Group at New York University's Courant Institute. His research focuses on the intersection of applied mathematics, scientific machine learning, and nuclear fusion. He develops theoretical and computational tools for plasma modeling, control, and optimization in complex dynamical systems. Key areas include stellarator design optimization, machine learning-driven MHD simulations, and reactor-scale plasma confinement solutions. His work advances fusion energy research through innovations in magnetic field shaping, coil optimization, and data-driven modeling of plasma behavior. Research interests span plasma turbulence analysis, magnetohydrodynamics, and the application of physics-informed neural networks (PINNs). He explores how magnetic geometry influences plasma stability and turbulence using machine learning techniques. His team addresses challenges in fusion reactor design, such as minimizing Lorentz forces in electromagnetic coils and optimizing permanent magnet configurations for stellarators. Recent work emphasizes data-driven methods for discovering interpretable models in fluids and plasmas, including reduced-order modeling and sparse regression approaches. He collaborates on projects like the DIII-D tokamak and ITER, advancing fusion energy solutions through interdisciplinary computational and experimental efforts. His group's contributions bridge plasma physics with advanced mathematics and machine learning to tackle grand challenges in energy and astrophysical systems. Advising and grants are not explicitly detailed in the provided texts, but his leadership role suggests active mentorship in graduate research. The NYU Plasma Physics Group serves as a hub for cutting-edge research, hosting internships, summer schools, and collaborations with industry/academic partners.

Francesco Fedeleمشاهده پروفایل

دانشیار

• Nonlinear Wave Phenomena
• Coastal and Ocean Engineering
• Fluid Mechanics
• +۱۱ مورد دیگر

Francesco Fedele is an Associate Professor at Georgia Tech, concurrently affiliated with the School of Civil and Environmental Engineering and the School of Electrical and Computer Engineering. He holds a Ph.D. in Civil Engineering from the University of Vermont (2004) and a Laurea (magna cum laude) from the University Mediterranea, Italy (1998). His research spans nonlinear wave phenomena, coastal engineering, fluid mechanics, and sustainable ocean energy, with a focus on rogue waves, stereo imaging, and computational methods. His work has been published in high-impact journals like Physical Review Letters and IEEE Transactions series. Research Interests include wave turbulence, signal processing for biomedical and radar applications, and mathematical modeling of tidal energy systems. Notable contributions involve analyzing extreme waves in the Mediterranean and North Sea, contributing to maritime safety and hurricane impact studies. Publications reflect expertise in fluid dynamics, oceanography, and computational methods. Recent work addresses the geometrical phases of nonlinear systems and interval-based finite element analysis under uncertainty. His research has been featured in Georgia Tech news for applications in rogue wave prediction and structural engineering. Dr. Fedele’s academic positions and collaborations include postdoctoral research at NASA Goddard Space Flight Center (pre-Georgia Tech tenure). No specific awards are explicitly listed in the provided text, though his work has garnered media attention for its societal impact.

Philbert Tsaiمشاهده پروفایل

عضو هیئت علمی

• Quantitative Biology
• Neurovascular Imaging
• Biomedical Engineering
• +۳ مورد دیگر

Philbert Tsai is an Associate Teaching Professor in the Department of Physics at the University of California, San Diego (UCSD). He has held roles as QBio Lab Coordinator/Project Scientist (2015–Present) and Associate Project Scientist (2011–2015), overseeing advanced laboratory setups and bio-imaging research projects. His work focuses on neurovascular systems, microscopy techniques, and cortical blood flow dynamics. Education: Ph.D., Physics, UC San Diego, 2004 Research Interests: Quantitative analysis of cortical microvascular networks Development of ultra-high-resolution imaging systems (e.g., STED, two-photon microscopy) Neurovascular coupling mechanisms and their impact on brain oxygen supply Biomedical engineering applications in neuroscience research Lab & Projects: QBio Lab: Advanced instrumentation including confocal microscopes, 3D printers, and wet-lab equipment Developed vectorized models of mouse brain vasculature and ultra-wide-field multiphoton imaging systems Grants & Awards: No specific awards listed in provided text Collaborations: Worked extensively with colleagues like Dr. David Kleinfeld and Dr. Berislav Zlokovic on neurovascular projects.

Joachim Reuderمشاهده پروفایل

استاد

• Meteorology
• Atmospheric Boundary Layer
• Wind Energy Meteorology
• +۳ مورد دیگر

Joachim Reuder is a Professor and Research Group Leader at the Geophysical Institute, University of Bergen, affiliated with the Bjerknes Centre for Climate Research. His research focuses on turbulence in the atmospheric boundary layer, wind energy meteorology, and the application of drones for atmospheric measurements. He leads the Meteorology research group and collaborates with the Bergen Offshore Wind Centre. His work combines field observations with advanced simulations, emphasizing stable boundary layers, wind turbine wake dynamics, and lidar technology validation. Key projects include the ISOBAR Arctic field campaigns, COTUR offshore turbulence studies, and the SAMURAI-S drone-based turbulence investigation. Publications highlight contributions to lidar data analysis, boundary layer modeling, and wind energy applications. He has organized conferences and contributed to editorial roles, underscoring his influence in atmospheric science and meteorology.

Tobias Grafkeمشاهده پروفایل

دانشیار

• Rare Events
• Fluid Dynamics
• Large Deviation Theory
• +۳ مورد دیگر

Dr. Tobias Grafke is Associate Professor of Mathematics at the University of Warwick, specializing in applied and computational mathematics. His research develops tools to analyze stochastic systems in fluid dynamics, climate science, and active matter. Current projects focus on predicting rare events like AMOC collapse and turbulence proliferation using large deviation theory and numerical methods. Recent articles (2023-2025) investigate noise-induced climate tipping points, rogue wave mechanics, and scalable algorithms for stochastic PDEs. Grants include an EPSRC New Investigator Award (2020) and NSF/EPSRC joint funding. Teaches MA3J4 (Mathematical Modelling with PDE) and MA2K4 (Numerical Methods).

• Computational Fluid Dynamics
• Vehicle Aerodynamics
• Bluff-Body Flows
• +۳ مورد دیگر

Sinisa Krajnovic is a Professor of Computational Fluid Dynamics and Head of the Department of Mechanics and Maritime Sciences at Chalmers University of Technology. His research focuses on vehicle aerodynamics, bluff-body flows, and time-dependent numerical simulations, particularly in ground vehicle flows (trains, cars, buses) and high-speed train dynamics. He leads studies on flow control mechanisms, bi-stable wake phenomena, and turbulence modeling using advanced CFD techniques like LES and PANS. Recent work emphasizes active flow control optimization, snow-resistance performance of bogies, and aerodynamic interactions in platoons. His 247+ publications span topics including high-speed train aerodynamics, ship airflow control, and bluff-body wake dynamics. Collaborations involve experimental validation and industrial applications in rail and marine transportation.

Ameya Jagtapمشاهده پروفایل

استادیار

• Scientific Machine Learning
• Deep Learning
• Computational Physics
• +۸ مورد دیگر

Ameya Jagtap is an Assistant Professor (Tenure-Track) in the Department of Aerospace Engineering at Worcester Polytechnic Institute (WPI), USA. Prior to this, he served as an Assistant Professor of Applied Mathematics (Research) at Brown University from 2021 to 2024. He holds a Ph.D. and M.E. in Aerospace Engineering from the Indian Institute of Science (IISc), and completed postdoctoral research at TIFR-CAM (India) and Brown University's Division of Applied Mathematics. His research bridges mechanical/aerospace engineering, applied mathematics, and computation, focusing on scientific machine learning algorithms that integrate data and physics. Key areas include physics-driven deep learning, uncertainty quantification, multi-scale simulations, and novel neural network architectures like quantum and graph networks. He serves on editorial boards for Neural Networks , Neurocomputing , and others. His work emphasizes interpretable neural operators for PDE solutions, domain decomposition methods, and adaptive activation functions to enhance PINN convergence. Notable contributions include XPINNs (extended physics-informed neural networks) and causal sweeping frameworks for PDEs. His research has been widely cited, particularly for PINN applications in supersonic flows and high-dimensional PDEs. Jagtap has delivered invited talks at institutions like Los Alamos National Laboratory, Tsinghua University, and the Alan Turing Institute. He is also recognized as a Top 2% World Scientist by Stanford University.

Ming Zhengمشاهده پروفایل

استاد

• High efficiency engines
• Combustion
• Emission control and diagnostics
• +۱۳ مورد دیگر

Ming Zheng is a Professor in the Department of Mechanical, Automotive & Materials Engineering at the University of Windsor, Faculty of Engineering. He is the Director of the Clean Combustion Engine Laboratory and holds a Canada Research Chair in Clean Diesel Engine Technologies. He is a Fellow of both SAE and ASME and a Professional Engineer (PEng). Education: Ph.D., Mechanical Engineering, University of Calgary, Canada, 1993 M.Sc., Thermal Energy and Automotive Engineering, Tsinghua University, China, 1988 B.Sc., Mechanical Engineering, Transport Technology Institute, China, 1982 PDF, Mechanical Engineering, Hokkaido University, Japan, 1995 Dr. Zheng's research focuses on clean and high-efficiency combustion technologies for internal combustion engines. His key interests include low-temperature combustion (LTC), homogeneous charge compression ignition (HCCI), active flow control aftertreatment for emission reduction, advanced ignition systems (e.g., multi-coil, corona), alternative and biofuels (e.g., ethanol, n-butanol), combustion modeling, diagnostics, and real-time adaptive control. His work aims to achieve simultaneous reductions in NOx and soot emissions while improving fuel efficiency. The analysis of his recent publications reveals a strong and consistent research trend centered on advanced combustion strategies using alternative fuels like ethanol and n-butanol. His work extensively explores dual-fuel combustion, the impact of fuel injection strategies, and the use of advanced control algorithms (e.g., extremum seeking control) to manage complex combustion processes. A significant portion of his research is dedicated to developing and optimizing active aftertreatment systems, such as Lean NOx Traps, to handle the unique exhaust characteristics of these clean combustion modes. Scientific Awards: SAE Fellow (2016) ASME Fellow University Award for Excellence in Research, Scholarship and Creative Activity (2007 and 2005) Canada Research Chair in Clean Diesel Engine Technologies (awarded 2003) Dr. Zheng has been a prolific advisor, supervising numerous PhD and Master's students on topics ranging from biofuel testing and low-temperature combustion to aftertreatment modeling and control. His research is highly collaborative, supported by significant grants and contracts from government agencies (NSERC, Auto21, CRC) and major industrial partners like Ford, International Truck and Engine Company, and Imperial Oil. He has secured over $2.6 million in cash awards and approximately $2.1 million in-kind contributions since 2003. Dr. Zheng leads the Clean Combustion Engine Laboratory , a state-of-the-art facility equipped with multiple modern diesel engine test cells (including a Ford common-rail engine and a Yanmar single-cylinder engine), advanced emission analyzers, real-time control systems (FPGA, Can-Bus), and sophisticated diagnostic and modeling tools (LabVIEW, GT-Power, Chemkin, MATLAB/Simulink). The lab specializes in experimental research on combustion, emissions, and aftertreatment, with a focus on active flow control technologies.

Lina von Sydowمشاهده پروفایل

استاد

• Computational Science
• Computational Finance
• Ice Sheet Modeling
• +۲ مورد دیگر

Lina von Sydow is a Professor in Computational Science at Uppsala University's Department of Information Technology. She serves as Section Dean for the Mathematical-Computer Science Section since July 2023. Her academic journey includes becoming an Associate Professor in 2000, Senior Lecturer since 1997, and leading the Department of Information Technology from 2018 to 2023. PhD in Domain Decomposition Methods (1995, Uppsala University) Postdoctoral Fellow at Oxford University (1996-1997) Her research spans computational science with dual focuses on Computational Finance and Ice Sheet Modeling . In finance, she develops numerical methods for option pricing using PDEs, radial basis functions, and stochastic volatility models. In climate science, she contributes to ice sheet dynamics through full Stokes models and adaptive time-stepping approaches, particularly in simulating grounding line migration. Recent publications (2025) address gender disparities in IT education, including comparative analysis of admission trends and intervention studies to boost female enrollment. Earlier works (2020-2015) focus on high-order finite difference methods for financial derivatives, BENCHOP benchmarking projects, and preconditioning techniques for PDEs. Scientific awards include Excellent Teacher (2013) She actively collaborates on educational reforms, co-authoring studies like Gender-aware course reform in Scientific Computing (2013). Her leadership roles include Head of Department (2018-2023) and Section Dean (2023-present), influencing academic governance and interdisciplinary research. Labs and teams: Works with Uppsala University's Computational Science group, Elmer/ICE project collaborators (e.g., Per Lötstedt, Gong Cheng), and international partners in numerical finance and climate modeling.

Audrey H. Sawyerمشاهده پروفایل

استاد

• Groundwater Hydrology
• Surface Water-Groundwater Interaction
• Hydrogeology
• +۷ مورد دیگر

Audrey H. Sawyer is a Professor at The Ohio State University's School of Earth Sciences, where she leads the Computational Hydrogeology Research Group. Her work explores how surface water-groundwater exchange mediates geologic and ecologic processes, with applications to ecosystem health, contaminant transport, and climate impacts on water resources. Research interests include River-groundwater exchange Coastal surface water-groundwater interactions Coupled fluid/heat flow and reactive transport Numerical modeling of hydrologic systems Climate change effects on groundwater recharge Current and former students (e.g., Jacob Clyne, Hannah Field, Kira Harris) have received prestigious awards including GSA Graduate Student Research Grants and NSF Fellowships. She also holds a Visiting Scientist appointment at Universitat Politècnica de Catalunya.

Xinyan Wangمشاهده پروفایل

استاد

• Low/Zero Carbon Combustion Engines
• Hydrogen/Ammonia Fuel Technologies
• Biofuels and E-Fuels
• +۶ مورد دیگر

Professor Xinyan Wang is a leading academic at Brunel University London, affiliated with the College of Engineering, Design and Physical Sciences and the Department of Mechanical and Aerospace Engineering. He serves as a Principal Editor for Fuel (Elsevier) , editorial board member for multiple journals, and committee member for Hydrogen Europe Research and UK Chinese Society of Automotive Engineering. PhD, Power Machinery and Engineering, Tianjin University MSc, Power Machinery and Engineering, Tianjin University BEng, Thermal Energy and Power Engineering, Jiangsu University His research focuses on low-carbon fuel technologies for internal combustion engines, including hydrogen/ammonia combustion, biofuels, and nanobubble applications. He develops advanced hybrid electric systems and specializes in engine design optimization for alternative fuels. His work spans experimental investigations, computational modeling (CFD/MD/Chemkin), and optical diagnostics of combustion processes. Recent publications (2024-2025) highlight trends in hydrogen combustion analysis, nanofluid applications, dual-fuel strategies, and 2-stroke engine optimization. Key themes include emission reduction, ignition process decoupling, and integration of machine learning with molecular simulations for fuel characterization. UKRI Future Leaders Fellowship (2020) Editorial roles at Fuel , Highlights of Vehicles , and MDPI journals BSI committee member for fine bubble technology He supervises research on topics including zero-carbon fuel combustion, numerical simulations (chemical kinetics, CFD), and optical diagnostics for spray/combustion analysis. His teaching includes vehicle propulsion systems and major engineering projects at undergraduate/graduate levels.

John Longleyمشاهده پروفایل

مدرس ارشد

• Aerospace Engineering
• Fluid Dynamics
• Turbomachinery
• +۳ مورد دیگر

Dr John Longley is a Senior Lecturer in Engineering at the Whittle Laboratory, Department of Engineering, University of Cambridge. He specializes in unsteady and non-uniform flow phenomena in turbomachinery and leakage flow effects on aerodynamic performance. Education: PhD at Cambridge University; BSc in Applied Mathematics Prior Employment: Ruston Gas Turbines (aerodynamic design of transonic turbines); MIT (VSTOL aerodynamic research) Research Focus: His work bridges theoretical and applied fluid dynamics in aerospace and mechanical engineering, particularly in turbomachinery optimization. Publication Trends: Recent research explores stator platform geometry impacts on turbine efficiency and unsteady flow modeling in gas turbines. Awards: ASME 2009 Gas Turbine Award for air-curtain over-tip seal research GPPS 2019 Best Paper Award (with Dr Derek Taylor) Contact: jpl@eng.cam.ac.uk | Phone: 01223 337583