جست‌وجوی استاد و پژوهشگر

Konrad Schindlerمشاهده پروفایل

استاد

• Photogrammetry
• Remote Sensing
• Computer Vision
• +۳ مورد دیگر

Prof. Konrad Schindler holds the position of Full Professor at the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich. He is also the Head of the Institute of Geodesy and Photogrammetry (IGP), leading research and educational activities in geomatics and computer vision. His career spans roles as a Photogrammetric Engineer, scientific assistant, postdoc researcher, and academic faculty across institutions including Graz University of Technology, Monash University, and TU Darmstadt before joining ETH Zürich in 2010. Education: Undergraduate studies in Geodesy (1992–1995), Graz University of Technology, Austria MEng in Photogrammetry and Geoinformation (1995–1999), Vienna University of Technology, Austria PhD in Computer Science (2001–2003), Graz University of Technology, Austria Research focuses on Photogrammetry , Remote Sensing , Computer Vision , and Image Understanding with interdisciplinary applications in environmental monitoring, geospatial analysis, and disaster response. He develops computational methods for 3D reconstruction, fusion of multi-modal data, and AI-driven solutions for satellite imagery interpretation. His work bridges geomatic engineering and machine learning to address challenges in urban mapping, climate modeling, and biological systems analysis. Publications reflect expertise in geospatial AI, diffusion models, and benchmarking datasets for disaster resilience. Notable works include Marigold (image analysis adaptation) and BRIGHT (building damage assessment). His research emphasizes practicality and scalability, such as affordable depth estimation and global biomass datasets. He has received the 2013 Marr Prize Honourable Mention (IEEE) and the 2012 U.V. Helava Award (ISPRS), alongside several Best Presentation Awards. His contributions span technical leadership, editorial roles (ISPRS Journal), and service to Swiss remote sensing commissions. Advising and grants: While no specific advisee names or grant details are listed, his career trajectory includes mentoring postdocs and junior faculty. He teaches advanced courses in Photogrammetry , Image Interpretation , and Machine Vision , integrating cutting-edge AI techniques into curricula. His research group collaborates on global-scale projects like canopy height mapping and satellite-based climate variable assessments. Labs/Teams: As Institute Head, he oversees the IGP lab at ETH Zürich, with prior affiliations including the Digital Perception Lab (Monash University) and the Computer Vision Lab (ETH Zurich). His work often involves multi-institutional collaborations focused on geospatial AI and environmental science.

Laurent Demanetمشاهده پروفایل

استاد

• Inverse Problems
• Wave Propagation
• Geophysical Imaging
• +۱۱ مورد دیگر

Laurent Demanet is a Professor of Applied Mathematics at the Massachusetts Institute of Technology (MIT), with a primary appointment in the Department of Mathematics and a joint appointment in Earth, Atmospheric, and Planetary Sciences (EAPS). He served as Director of MIT’s Earth Resources Laboratory from 2018 to 2024, focusing on seismic imaging and wave propagation to visualize Earth's subsurface structures. Undergraduate: Mathematical Engineering and Theoretical Physics, Université de Louvain, Belgium PhD: Applied and Computational Mathematics, California Institute of Technology (Caltech), 2006 (advisor: Emmanuel Candès, William P. Carey Prize for best dissertation in mathematical sciences) Postdoctoral: Szegö Assistant Professorship, Stanford University (2006-2009) His research centers on applied mathematics and scientific computing for geophysics, particularly inverse problems, wave propagation, and signal processing. He develops numerical algorithms and machine learning tools to analyze indirect, incomplete, and noisy data, enabling accurate subsurface modeling for environmental monitoring, carbon sequestration, hydrogen storage, and geothermal energy. His work also addresses seismic hazards induced by resource exploration and storage activities. Scientific awards and honors include: National Science Foundation CAREER Award (2012) Alfred P. Sloan Research Fellowship (2011) Air Force Young Investigator Award (2011) MIT Class of 1954 Career Development Professorship (2013-2016) As Director of MIT’s Earth Resources Laboratory and through his research group, Demanet has advanced computational methods for geophysical imaging, mentored students, and secured grants for interdisciplinary projects. His work bridges applied mathematics, computational science, and geophysics to solve pressing environmental and energy challenges. He leads research on subsurface visualization, integrating machine learning and numerical algorithms to improve seismic imaging and quantify predictive uncertainty. His lab's applications span natural hazard assessment, resource management, and sustainable energy solutions.

Petros Koumoutsakosمشاهده پروفایل

استاد

• Machine Learning
• Computational Fluid Dynamics
• Turbulence Modeling
• +۳ مورد دیگر

Petros Koumoutsakos is the Herbert S. Winokur, Jr. Professor of Computing in Science and Engineering at Harvard University's School of Engineering and Applied Sciences (SEAS), where he also serves as Area Chair for Applied Mathematics. His research integrates machine learning with computational science to advance understanding of complex systems, including fluid dynamics, turbulence modeling, and biomedical applications. He leads the CSE Lab, focusing on high-performance computing and interdisciplinary collaborations such as a recent study with Citadel Securities and Google Cloud to simulate heart disease in cloud environments. Key research interests include reinforcement learning for turbulence closures, generative models for PDE solutions, and physics-informed AI for biomedical imaging and wildfire prediction. He was awarded the PRACE HPC Excellence Award (2023) for contributions to high-performance computing. His work bridges computational methods with real-world applications, emphasizing interpretability and scalability in multiscale systems. Grants & Collaborations: Leadership in multi-institutional projects, including turbulence modeling via reinforcement learning and cloud-based HPC studies. Labs/Teams: Director of the CSE Lab, advancing AI, computational fluid dynamics, and biomedical simulations.

Andrew Childsمشاهده پروفایل

استاد

• Quantum Algorithms
• Quantum Information Processing
• Quantum Simulation
• +۲ مورد دیگر

Andrew Childs is a Professor at the University of Maryland, affiliated with the Department of Computer Science and the Institute for Advanced Computer Studies (UMIACS). He serves as Director of the NSF Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS) and is a Fellow at the Joint Center for Quantum Information and Computer Science (QuICS). His research focuses on quantum algorithms for simulating physical systems, algebraic problems, and quantum walk protocols, with applications in quantum computing and computational complexity. University of Maryland Institute for Advanced Computer Studies (UMIACS) Joint Center for Quantum Information and Computer Science (QuICS) NSF Quantum Leap Challenge Institute for Robust Quantum Simulation Childs' research spans quantum simulation, quantum Fourier transform, phase estimation, and Hamiltonian dynamics. He has developed techniques to reduce quantum computational resources for simulating quantum systems and explored limitations of quantum computers through hidden subgroup problems and non-unitary dynamics. His publications cover diverse areas including quantum walk optimization, Hamiltonian simulation methods, and applications to cryptography and condensed matter physics. Recent works address spatial search algorithms, product formulas for commutators, and quantum routing protocols. As an educator, Childs has taught courses on quantum algorithms and information processing at both the University of Maryland and University of Waterloo, with lecture notes and materials spanning multiple years. Contact: amchilds@umd.edu | Office: ATL 3359 | Affiliated with University of Maryland's quantum research institutes.

Yuriy Rogovchenkoمشاهده پروفایل

استاد

• Qualitative theory of differential equations
• Mathematical modeling in biology
• Perturbation methods
• +۲ مورد دیگر

Yuriy Rogovchenko is a Professor in the Department of Mathematical Sciences at the University of Agder. His research spans differential equations, mathematical modeling, and education innovation, with applications in biology, social sciences, and engineering. Rogovchenko has contributed extensively to mathematics education through projects like PLATINUM (Erasmus+ Strategic Partnership) and CPEA-ST-2019/10067 (Eurasia project). PhD in differential equations (Institute of Mathematics, Kyiv, 1987) Regular Associate at Abdus Salam ICTP, Trieste (2004-2011) Editor for 11 international journals Referee for over 70 journals Research Interests: Qualitative theory of differential equations, perturbation methods, mathematical modeling in interdisciplinary contexts. He focuses on enhancing conceptual understanding through inquiry-based learning and nonstandard problems. Publications: Recent works include advancements in linear system observability, parameter identification methods, and educational studies on exact differential equations. His collaborations with Svitlana Rogovchenko and Matthias Pätzold highlight applications in engineering and biology. Awards: Sørlandet kompetansefonds research award (2016).

Zongyi Liمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Scientific Computing
• Partial Differential Equations
• +۵ مورد دیگر

Zongyi Li is a Research Fellow at Massachusetts Institute of Technology , hosted by Kaiming He. They are currently pursuing a Ph.D. in Computing and Mathematical Sciences at Caltech (2019-2025), mentored by Anima Anandkumar and Andrew Stuart. Ph.D. candidate: Computing and Mathematical Sciences, Caltech (2019-2025) B.Sc. in Computer Science and Mathematics with a Jazz minor from Washington University in St. Louis (2015-2019) They focus on Neural Operators for learning solution operators in Partial Differential Equations (PDEs) , particularly in fluid mechanics and earth science . Their work models physical simulations with chaotic behaviors and complex geometries, showing applications in weather forecasting , carbon storage , and aerodynamics simulation . Publications emphasize resolution-invariant models , chaotic systems , and zero-shot super-resolution capabilities. Their research combines Fourier analysis , graph networks , and physics-informed loss functions to achieve state-of-the-art performance in PDE solving with up to 1000x speedup over traditional solvers. Fellowships: Kortschak Scholarship PIMCO Fellowship Amazon AI4Science Fellowship Nvidia Fellowship MIT Novo Nordisk AI Fellowship Code & Open-Source: Co-developer of the NeuralOperator library Implementations for Fourier Neural Operators , Graph Neural Operators , and Tensorized Neural Operators Media Recognition: Quanta Magazine MIT Tech Review NVIDIA Features Towards Data Science

Ali Mostafazadehمشاهده پروفایل

استاد

• Mathematical Physics
• Quantum Mechanics
• Scattering Theory
• +۳ مورد دیگر

Ali Mostafazadeh is a Professor at the Department of Mathematics, College of Sciences, Koç University. His research spans Mathematical Physics, focusing on Quantum Mechanics, Scattering Theory, and PT-Symmetry. He has made significant contributions to understanding non-Hermitian Hamiltonians, electromagnetic wave propagation, and geometric scattering phenomena. Education: PhD in Physics (1994) from The University of Texas, BA in Physics and Mathematics (1989) from Boğaziçi University His work explores the intersection of mathematics and physics, particularly through spectral singularities, transfer matrix methods, and nonlinear optical systems. Recent publications highlight advancements in broadband directional invisibility, exact Born approximations, and time-dependent Hilbert spaces in quantum systems. 2011 Outstanding Success Award 2007 TÜBİTAK Science Award 2006 Werner-von-Siemens Excellence Award 2001 TÜBA Outstanding Young Scientists Award 2001 Parlar Foundation Research Incentive Award

• Additive Manufacturing
• Aerospace Engineering
• Thermofluids
• +۱ مورد دیگر

Associate Professor Ng Bing Feng leads research in Additive Manufacturing, Aerospace Engineering, and Thermofluids at Nanyang Technological University's School of Mechanical & Aerospace Engineering. As Cluster Director for Smart & Sustainable Building Technologies at NTU's Energy Research Institute, he oversees projects spanning energy-efficient cooling technologies, advanced filtration systems, and bio-inspired materials. His interdisciplinary work integrates advanced manufacturing with environmental engineering, focusing on sustainable solutions for air quality management and thermal regulation. Current research explores radiative cooling technologies, acoustic agglomeration for emissions control, and crashworthy structures via biomimetic design. Professor Ng's publications demonstrate consistent innovation in multi-scale manufacturing and fluid dynamics, with growing emphasis on sustainable urban technologies. Methodologies combine experimental fluid dynamics with computational modeling and AI-driven design. Leadership Roles: Cluster Director, Energy Research Institute @ NTU Principal Investigator for multiple industry collaborations in sustainable materials Research Output: 42+ publications in high-impact journals Multiple patents in filtration technologies and cooling systems

Peter K. Kitanidisمشاهده پروفایل

استاد

• Groundwater Flow
• Hydrologic Modeling
• Inverse Problems
• +۳ مورد دیگر

Peter K. Kitanidis is a Professor in the Department of Civil and Environmental Engineering and the Institute for Computational and Mathematical Engineering at Stanford University . His research focuses on groundwater flow , hydrologic forecasting , and stochastic inverse modeling , with applications to pollutant remediation and CO₂ storage monitoring . Education : Diploma, National Technical University of Athens (1974) M.S., MIT (1976) Ph.D., MIT (1978) Research Interests : Groundwater modeling and contaminant transport Hydraulic tomography and aquifer characterization Stochastic methods for uncertainty quantification Bioremediation and enhanced in-situ pollutant decay Dilution and mixing processes in heterogeneous media Real-time river flow forecasting Scientific Awards : L.G. Straub Award (1979) W.L. Huber Research Prize (1994) ISI Highly Cited Researcher (2001) AGU Hydrologic Sciences Award (2011) ASCE Pioneers in Groundwater Lecturer (2011) Advising and Grants : Advised 20+ PhD and MS students (1978–2018) Principal investigator on NSF, EPA, and DOE-funded projects Developed software for groundwater data analysis and CO₂ monitoring Contributed to bioremediation protocols and hydraulic tomography algorithms Labs and Teams : Kitanidis Laboratory for groundwater crisis solutions Collaborated with Oak Ridge National Laboratory and Stanford Hydrogeology Group Mentored postdocs (2000–2017) in reactive transport and inverse modeling

Steffen Marburgمشاهده پروفایل

استاد

• Numerical Methods in Vibroacoustics
• Aeroacoustics
• Structural Optimization
• +۶ مورد دیگر

Prof. Dr. Steffen Marburg is a Full Professor at the Chair of Acoustics of Mobile Systems within the TUM School of Engineering and Design at the Technical University of Munich. His research focuses on numerical methods in vibroacoustics, structural optimization, and acoustic modeling for applications in automotive, maritime, and musical instrument domains. Education: PhD from Technical University of Dresden (1998). Academic Career: Junior Professor at TU Dresden (2004), Chair of Technical Dynamics at University of the Federal Armed Forces Munich (2010), Full Professor at TUM (2015–present). Editorial Roles: Co-Editor-in-Chief of Journal of Theoretical and Computational Acoustics, Associate Editor of Journal of the Acoustical Society of America, Editor of Acoustics Australia and Mechanical Systems and Signal Processing. His research integrates computational acoustics, boundary element methods, and machine learning to address noise control and structural optimization challenges. Recent work explores acoustic metamaterials, viscothermal losses, and data-driven modeling. He has co-authored over 150 publications and led advancements in multifrequency solution methods and noise-insulating structures. Scientific awards include the Innovation Award of the Industrieclub Sachsen e.V. (1999). His editorial contributions and leadership in journals highlight his influence in computational acoustics and structural dynamics.

Ron Fedkiwمشاهده پروفایل

استاد

• Computer Graphics
• Computational Fluid Dynamics
• Biomechanics
• +۶ مورد دیگر

Ron Fedkiw is the Canon Professor of Computer Science at Stanford University's School of Engineering. He holds a PhD in Applied Mathematics from UCLA. His research focuses on computational algorithms for applications in computational fluid dynamics, computer graphics, biomechanics, and machine learning. Fedkiw has pioneered techniques for simulating natural phenomena in film and video games, earning two Academy Awards for his contributions to visual effects. He leads the PhysBAM lab and collaborates with industry through consulting roles at Epic Games and former work with Industrial Light & Magic. Education: PhD in Applied Mathematics, UCLA (1996). Notable awards include the National Academy of Science Award, Packard Fellowship, and multiple teaching honors. His lab has graduated 40 PhD students, many of whom have made significant impacts in academia and industry. Research interests span fluid dynamics, cloth simulation, facial animation, and integrating machine learning with physical models. Key contributions include algorithms for two-way fluid-solid coupling, muscle-based facial modeling, and neural network approaches for cloth and deformable bodies. Current projects explore physics-informed machine learning and real-time interactive simulations. Scientific Awards include two Oscars, PECASE, and Okawa Foundation grants. His work bridges computational physics and visual effects, with over 140 research papers and a textbook on level set methods. Advising and grants: Supervised 40 PhD students, securing funding through NSF, ONR, and industrial partnerships. Lab collaborations include SAIL (Stanford AI Lab) and Epic Games. Future work focuses on AI-driven physical simulations and biomedical applications.

Melody Alsakerمشاهده پروفایل

دانشیار

• Medical Imaging
• Applied Inverse Problems
• Mathematical Modeling
• +۳ مورد دیگر

Melody Alsaker is an Associate Professor in the Department of Mathematics at Gonzaga University, where she has held this position since January 2016. Her research focuses on medical imaging and applied inverse problems, particularly in the field of electrical impedance tomography (EIT). She specializes in mathematical modeling, algorithm design, and biomedical image processing, with applications in pulmonary and thoracic imaging. Her work emphasizes improving EIT reconstruction techniques using the D-bar method, incorporating spatial priors, and developing real-time solutions for clinical applications. Notable contributions include the ACE1 EIT system for thoracic imaging and studies on stroke classification, air trapping in lungs, and surrogate measures of pulmonary function in children with cystic fibrosis. Alsaker's research bridges mathematics and engineering, addressing challenges in medical imaging accuracy and computational efficiency. Her collaborations span disciplines, including biomedical engineering, respiratory physiology, and clinical medicine.

Jeroen Trompمشاهده پروفایل

استاد

• Seismology
• Computational Geophysics
• Earth Imaging
• +۵ مورد دیگر

Jeroen Tromp serves as the Blair Professor of Geology and Professor of Geosciences and Applied and Computational Mathematics at Princeton University, where he also directs the Princeton Institute for Computational Science and Engineering (PICSciE). His work centers on theoretical and computational seismology with applications across Earth and planetary sciences. His research interests focus on imaging Earth's interior through advanced computational techniques. Key areas include surface waves, free oscillations, body waves, seismic tomography, numerical simulations of 3-D wave propagation, and seismic hazard assessment. His group develops open-source software for acoustic, elastic and poroelastic wave propagation, addressing problems in exploration geophysics, regional and global seismology, and helioseismology. Current research trends show strong emphasis on Mars seismology (InSight mission), iron spin crossover in the lower mantle, tilted transverse isotropy in Earth's inner core, and crosstalk-free waveform inversion techniques across multiple scales. Tromp actively mentors graduate students and leads collaborative projects involving seismic wavefield imaging across planetary bodies. His group maintains strong connections with NASA's InSight mission and develops computational frameworks for global centroid moment tensor inversions. The research team operates within the Department of Geosciences, leveraging high-performance computing resources through PICSciE to tackle large-scale inverse problems in seismology.

Oscar P. Brunoمشاهده پروفایل

استاد

• Partial Differential Equations
• Computational Science
• Numerical Analysis
• +۵ مورد دیگر

Oscar P. Bruno is a Professor of Applied and Computational Mathematics at the California Institute of Technology (Caltech). He holds a Licenciado from the University of Buenos Aires (1982) and a Ph.D. in Mathematics from New York University's Courant Institute (1989). Since 1998, he has been a Professor at Caltech, previously serving as Associate Professor (1995–98) and Executive Officer for Applied Mathematics (1998–2000). His research focuses on developing high-performance numerical methods for solving partial differential equations (PDEs), addressing challenges in complex geometries, singularities, and high-frequency phenomena. Key contributions include the Fourier Continuation (FC) method and integral-equation techniques, enabling solutions to previously intractable PDE problems in science and engineering. Prof. Bruno's expertise spans computational electromagnetics, computational fluid dynamics (CFD), solid mechanics, and mathematical physics. His work integrates numerical analysis, multiphysics modeling, and computational science to solve real-world problems in geophysics, optics, and fluid dynamics. He has received numerous awards, including membership in the National Academy of Sciences of Argentina (2020), the Vannevar Bush National Security Science and Engineering Fellowship (2016), and SIAM Fellow (2013). Bruno serves on editorial boards for journals like SIAM Journal on Scientific Computing and SIAM Journal on Applied Mathematics, and participates in national science advisory roles. His teaching includes advanced courses on applied mathematics methods (ACM/IDS 101 ab), emphasizing theoretical foundations and numerical techniques for PDEs. His research group develops cutting-edge solvers with applications in shock dynamics, optical tomography, and geophysical fluid dynamics.

Vladimir Kazeevمشاهده پروفایل

استادیار

• Numerical Analysis
• Tensor Methods
• Partial Differential Equ to the data-driven solution of PDEs
• +۳ مورد دیگر

Vladimir Kazeev is an Assistant Professor at the Faculty of Mathematics, University of Vienna , where he has held a faculty position since 2019. He also held previous academic appointments as a Szegő Assistant Professor at Stanford University (2017–2019), a postdoctoral researcher at the University of Geneva (2015–2017), and research positions at ETH Zurich (2011–2015), Russian Academy of Sciences (2008–2011), and Moscow Institute of Physics and Technology (2009). His research focuses on adaptive, data-driven numerical methods for differential equations, nonlinear low-parametric approximation, and numerical linear algebra. His work intersects computational mathematics, tensor methods, and high-dimensional problem-solving, particularly in the context of partial differential equations (PDEs) and stochastic modeling. The 15 most recent publications reveal a strong emphasis on quantized tensor-structured methods for PDEs, low-rank approximations, and high-dimensional numerical analysis. His research spans theoretical advancements in tensor decomposition, practical applications in chemical reaction networks, and novel discretization techniques for multiscale and degenerate diffusion problems. Scientific awards include the prestigious ETH Medal for outstanding doctoral theses (2016) Russian Academy of Sciences Medal for outstanding student works in mathematics (2011) Advising and teaching activities include supervising Jason Zhu (Stanford, 2019) and Simon Etter (ETH Zurich, 2014), as well as teaching advanced courses in tensor methods, numerical analysis, and PDEs at the University of Vienna, Stanford University, and the University of Geneva. His service to the community includes peer review for 15+ journals and co-organizing minisymposia at SIAM meetings.