جست‌وجوی استاد و پژوهشگر

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.

Alan Lindsayمشاهده پروفایل

دانشیار

• Applied Partial Differential Equations
• Computational Mathematics
• Mathematical and Computational Biology
• +۳ مورد دیگر

Alan Lindsay is an Associate Professor in the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame, within the College of Science. He holds a Ph.D. from the University of British Columbia (2010) and a B.S. from the University of Edinburgh (2005). His research focuses on computational and analytical methods for partial differential equations (PDEs) modeling physical and biological systems, including Micro-Electromechanical Systems (MEMS), mathematical ecology, imaging, and inverse problems. His email is a.lindsay@nd.edu, and he is based in Crowley Hall. Education: Ph.D., Applied Mathematics, University of British Columbia, 2010 B.S., Mathematics, University of Edinburgh, 2005 Research Interests: Applied Partial Differential Equations Numerical Methods for PDEs Mathematical Biology and Biophysics Scientific Computing and Simulation MEMS and Micro-Electromechanical Systems Mathematical Modeling of Biological Processes Recent Research Trends: Lindsay’s work emphasizes computational techniques like boundary integral methods, kinetic Monte Carlo simulations, and bifurcation analysis to study diffusion processes, first passage times, and pattern formation in biological and physical systems. His studies bridge theoretical analysis and practical applications, such as optimizing T cell antigen recognition and modeling moth mating strategies. Grants & Advising: While no students are listed, his research is supported by grants in computational mathematics and biological modeling. His work often involves interdisciplinary collaborations with biologists and engineers. Labs/Teams: His research is conducted within the ACMS department, leveraging Notre Dame’s computational infrastructure.

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).

Soledad Villarمشاهده پروفایل

استادیار

• Optimization for Data Science
• Machine Learning
• Representation Learning
• +۵ مورد دیگر

Soledad Villar is an Assistant Professor in the Department of Applied Mathematics and Statistics and a member of the Mathematical Institute for Data Science at Johns Hopkins University. She also contributes to the Data Science and AI Institute . Her research focuses on computational methods for extracting information from data, emphasizing optimization for data science, machine learning, equivariant representation learning, and graph neural networks. Dr. Villar holds a PhD in Mathematics from the University of Texas at Austin and has been a research fellow at New York University and the Simons Institute at UC Berkeley. Her work bridges theoretical foundations with practical applications in fields like scientific computing and political analysis. Awards include the National Science Foundation CAREER Award (2024). Her research has addressed topics such as gerrymandering detection, fluid dynamics modeling, and graph representation learning. She collaborates on interdisciplinary projects and organizes academic events like the One World MINDS Seminar and the Cibercoloquio Latinoamericano de Matemáticas . Her research interests span computational methods, equivariant machine learning frameworks, and graph neural networks, with applications in physics, engineering, and data-driven decision-making. She actively engages in advancing machine learning techniques for scientific and engineering challenges.

• 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.

• Continuum Mechanics
• Material Modelling (Composites and metamaterials)
• Computational Modelling
• +۶ مورد دیگر

Zia Javanbakht is a Senior Lecturer at the School of Engineering and Built Environment , Griffith University , specializing in Mechanical Engineering and Industrial Design . As a chartered engineer with a PhD in Mechanical Engineering, he contributes to research in Continuum Mechanics , Material Modelling (composites and metamaterials), and Computational Modelling . He is affiliated with the Australian Centre for Precision Health and Technology (PRECISE) and has been involved in projects related to additive manufacturing, auxetic materials, and composite structures. Research Interests include the development of advanced computational models for material behavior, with a focus on auxetic structures , triply periodic minimal surfaces (TPMS) , and additively manufactured composites . His work addresses challenges in residual stress analysis , multiscale modelling , and machine learning applications in material deformation mechanisms. Scientific Awards Fellow (FHEA) of Higher Education Authority, Dublin, Ireland (since 2021) Key Funded Projects span collaborations with Gilmour Space Technologies (CRC-P grant for rocket fuel tanks), Bond University (concrete sensor testing), and internal Griffith University grants for equipment like the Transient Plane Source Thermal Conductivity Analyser . He actively supervises PhD and Master’s students in topics such as polymer-matrix composites , auxetic timber structures , and additive manufacturing failure models . Collaboration Networks include the AuxeticsLab and partnerships with industry leaders like Stoddart Group Pty Ltd and ATL Composites . His teaching portfolio covers Constitutive Material Modelling (7015ENG) and Computational Statics and Dynamics (7252ENG), reflecting his expertise in computational techniques and structural analysis.

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.

Stephen Eubankمشاهده پروفایل

استاد

• Computational Epidemiology
• Complex Systems
• Network Science
• +۳ مورد دیگر

Stephen Eubank serves as Professor of Biocomplexity and Professor of Public Health Sciences at the University of Virginia, where he holds a tenured position in the Department of Public Health Sciences and acts as deputy director of the Biocomplexity Institute. His work bridges computational modeling, epidemiology, and complex systems theory with significant contributions to large-scale simulation frameworks. Eubank's educational background includes: B.A. in Physics from Swarthmore College (1979) Ph.D. in Physics from the University of Texas at Austin (1986) Postdoctoral Associate in Fluid Turbulence at La Jolla Institute (1987) Postdoctoral Associate in Nonlinear Dynamics at Los Alamos National Laboratory (1991) His research centers on simulating socio-technical systems and computational epidemiology, with particular expertise in network structure dynamics and scaling phenomena. Eubank investigates how disease transmission and other diffusive processes interact with underlying network topologies, developing advanced simulation technologies for modeling realistic population behaviors. His work integrates physics-based approaches with public health applications through the Urban Infrastructure Suite framework. Eubank maintains an active research leadership profile as Principal Investigator for a key research group within the National Institutes of Health's MIDAS network. His career includes founding Prediction Company for market time-series analysis and serving as Visiting Scientist at ATR Kyoto for natural language processing research. As deputy director of the Biocomplexity Institute, Eubank leads the Urban Infrastructure Suite initiative—a collection of interoperable simulations modeling individual behaviors across entire urban regions. This work integrates physical infrastructure networks with social dynamics to model complex system interactions, building on his earlier TRANSIMS and EpiSims projects developed during his tenure at Los Alamos National Laboratory.

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.

PASCUAL ALVAREZ GOMEZمشاهده پروفایل

استاد

• Ground Engineering
• Thermal Engineering
• Production Technologies

PASCUAL ALVAREZ GOMEZ is a Professor at the University of Cádiz, affiliated with the Department of Industrial Engineering and Civil Engineering. His research focuses on Ground Engineering and Thermal Engineering, with a particular emphasis on geothermal heat pumps and thermal performance modeling. He is associated with the TEP221 Thermal Engineering research group and contributes to the PAIDI area of Production Technologies. Education: PhD in Industrial Engineering from the University of Cádiz (2014), with a thesis on "Vertical Ground Heat Exchanger Simulation Model for Geothermal Heat Pumps" Research: Specializes in hybrid thermal modeling, CFD analysis for evaporation rates, and machine learning applications in corrosion prediction His publications highlight advancements in geothermal systems, low-concentration photovoltaics, and biogas environment corrosion analysis. He employs both analytical and computational fluid dynamics (CFD) approaches for energy efficiency improvements.

Travis B. Thompsonمشاهده پروفایل

استادیار

• Mathematical Modeling
• Scientific Computing
• Machine Learning in Medicine
• +۵ مورد دیگر

Travis B. Thompson, Ph.D. is an Assistant Professor in the Department of Mathematics and Statistics at Texas Tech University, leading the TM4 (Texas Tech Translational and Theoretical Mathematical Modeling and Machine Learning in Medicine) research group. His academic journey includes postdoctoral work at Rice University, Simula Research Laboratory, and the University of Oxford, focusing on mathematics applied to neurodegenerative diseases. Education: Ph.D. in Mathematics from Texas A&M University (2013) Dr. Thompson develops theoretical mathematical models and applies scientific computing and machine learning to study neurological pathologies, particularly Alzheimer’s disease. His work explores complex biological processes on networks, translational healthcare applications, and nutritional security implications. Current research trends integrate neuroimaging data with finite element simulations to model tau progression , amyloid beta dynamics , and glymphatic clearance in age-related diseases. Scientific awards and honors were not explicitly mentioned in the provided materials. Dr. Thompson’s interdisciplinary approach connects computational neuroscience with biomedical engineering , utilizing techniques like diffusion tensor imaging and level set methods to analyze pathological protein spread and brain tissue mechanics . The TM4 research group focuses on network neurodegeneration , personalized medicine , and machine learning diagnostics . Their work spans from microfluidic cancer detection to computational modeling of brain clearance mechanisms , addressing challenges in both neurodegenerative diseases and biomedical engineering through rigorous mathematical frameworks.

Patrik Kovačovskýمشاهده پروفایل

استاد

• Sculpture in 3D virtual space and architecture
• Multimedia Art
• Digital Installations
• +۲ مورد دیگر

Patrik Kovačovský is a Professor and Studio Manager at the Department of Sculpture, Object, Installation at the Academy of Fine Arts in Bratislava. He works at the intersection of sculpture, architecture, and digital space, focusing on innovative artistic research and practice. Academy of Fine Arts in Bratislava (Department of Sculpture, 1990–1996) Art.D. studies, Academy of Fine Arts, 2001–2003 His research integrates traditional sculpture with virtual reality and architectural contexts, producing installations that explore memory, cosmic themes, and cultural identity. Recent works include Fluid Pictures (2011) and Kosmos (2006). 1996 Young Artist of the Year, Slovak National Gallery 2000 Annual Award, Galéria Klatovy/Klenová 2008 Visegrad Artist Residency 2010 Prohelvetia Residency in Zurich

François Gouletteمشاهده پروفایل

استاد

• 3D Point Cloud Processing
• LiDAR Perception
• Autonomous Systems
• +۷ مورد دیگر

François Goulette is a Professor and Deputy Director of the Computer Science and Systems Engineering Unit (U2IS) at ENSTA Paris, part of Institut Polytechnique de Paris. His research focuses on 3D point cloud processing, LiDAR perception, and autonomous systems within the Robotics Center (CAOR). His primary research interests lie in 3D point cloud processing , LiDAR perception , and autonomous systems . His work spans fundamental algorithm development to practical applications in autonomous driving, cultural heritage digitization, and robotics. He has made significant contributions to domain generalization of LiDAR perception, semantic segmentation of 3D point clouds, and point cloud registration techniques. The analysis of his recent publications reveals a strong focus on domain generalization for LiDAR perception systems, with multiple papers addressing challenges in 3D semantic segmentation across different environments. His work combines multi-scale architectures , unsupervised learning , and dataset creation to advance the state-of-the-art in autonomous systems perception. The research spans both theoretical algorithm development and practical applications in urban environments. François Goulette leads research activities within the Robotics Center (CAOR) at ENSTA Paris. His team develops advanced techniques for 3D environment understanding, with applications in autonomous vehicles, cultural heritage preservation, and industrial robotics. The research combines computer vision, machine learning, and robotics to solve challenging problems in 3D perception and scene understanding.