جست‌وجوی استاد و پژوهشگر

Zhao Yanxiangمشاهده پروفایل

دانشیار

• Computational Mathematics
• Numerical Analysis
• Mathematical Modeling
• +۴ مورد دیگر

Dr. Zhao Yanxiang is an Associate Professor in the Department of Mathematics at The George Washington University. His research focuses on computational mathematics, numerical analysis, and mathematical modeling with applications in biological chemistry and physics. He specializes in numerical methods for differential equations and phase-field modeling, particularly in areas such as cell motility and material science. Education: B.A., 2002, Dalian University of Technology M.S., 2005, Dalian University of Technology Ph.D., 2011, The Pennsylvania State University Research Interests: Dr. Zhao’s work bridges computational methods and real-world biological and physical systems. His key areas include optimal transport theory, phase-field modeling of biological processes (e.g., cell chemotaxis), and numerical schemes for nonlocal and multiscale systems. He has developed algorithms for applications in polymer physics, material phase transitions, and systems biology. His methodologies often integrate machine learning with traditional numerical analysis to solve complex PDEs and model dynamics across multiple spatial and temporal scales. Publications: His recent work emphasizes interdisciplinary applications of optimal transport, such as screening cell-cell communication via collective transport methods and analyzing phase-field models for biological systems. He has also contributed to the development of energy stable numerical schemes and physics-informed neural networks for solving challenging equations in materials science. Labs/Teams: While specific lab names are not mentioned, his research likely involves collaborations with computational biology and materials science groups, integrating mathematical modeling with experimental data.

Yannick Salaminمشاهده پروفایل

استادیار

• Quantum Optics
• Nonlinear Optics
• Photonics
• +۳ مورد دیگر

Dr. Yannick Salamin is an Assistant Professor at CREOL, The College of Optics and Photonics at the University of Central Florida (UCF). His research focuses on quantum properties of light and nonlinear optical systems, particularly exploring quantum states of light, macroscopic quantum phenomena, and applications in quantum technology. His work utilizes optical parametric oscillators (OPOs) to generate and characterize quantum states, aiming to advance computing and metrology. Dr. Salamin holds a B.S. in Electrical Engineering from the University of Applied Sciences of Western Switzerland (2010), an M.S. from Zhejiang University (2014), and a Dr.Sc. from ETH Zurich (2019). His postdoctoral research at MIT under Prof. Marin Soljačić furthered his expertise in quantum nonlinear photonics. His research group develops systems to harness quantum vacuum noise for probabilistic computing and machine learning. Key areas include controlling nonlinear driven-dissipative systems, biasing quantum vacuums, and creating macroscopic probability distributions. Recent work includes generating large Fock states and squeezed states via nonlinear bound states in the continuum. Research Themes: Quantum vacuum engineering, nonlinear photonics, stochastic computing, and quantum metrology. Applications: Quantum computing, advanced sensors, and probabilistic machine learning. Dr. Salamin has received prestigious awards including the 2025 Ralph E. Powe Award, ABB Research Prize (2021), and the ETH Medal (2020). His lab includes three Ph.D. advisees and multiple undergraduate researchers. He collaborates widely, with publications in Nature Photonics , Science , and Proceedings of the National Academy of Sciences . Current projects emphasize scalable quantum systems and novel photonic devices.

Felix Fritzenمشاهده پروفایل

استاد

• Data Analytics in Engineering
• Computational Mechanics
• Material Modeling
• +۷ مورد دیگر

Prof. Felix Fritzen is a Heisenberg Professor (W3) for Data Analytics in Engineering at the University of Stuttgart's Institute of Applied Mechanics (MIB). His work is embedded in the Cluster of Excellence Data-Integrated Simulation Science (SimTech). He leads the EMMA Emmy Noether group (2015–2020) and previously headed the KIT Young Investigator Group CAMM. His research focuses on data-driven surrogate models, uncertainty quantification, and computational mechanics of materials, with emphasis on nonlinear model reduction and multiscale simulations. Education: Ph.D. (Dr.-Ing., summa cum laude) from KIT (2011), Dipl.-Math. techn. (2007), Dipl.-Ing. (2006). Research interests include machine learning integration with mechanics, microstructure-property relations, and high-performance simulation techniques. Notable contributions involve FFT-based homogenization, reduced order modeling, and GPU-accelerated methods. He has authored over 60 peer-reviewed papers, including works on surrogate models for microstructure forecasting and thermoelastic material analysis. Awards include the KIT PhD Award (2012) and recognition as a GAMM Junior (2012–2014). His teaching includes courses on data processing for engineers and model order reduction. Funded projects include DFG grants (EXC-2075, HE 7919/1) and the Heisenberg Professorship (FR2702/8).

Martin Taylorمشاهده پروفایل

مدرس ارشد

• Pure Mathematics
• Applied Mathematics
• Partial Differential Equations
• +۳ مورد دیگر

Martin Taylor is a Royal Society Tata University Research Fellow and Senior Lecturer in the Department of Mathematics at Imperial College London. He is affiliated with the Pure Analysis and PDE group and the Geometry group within the Faculty of Natural Sciences. His research focuses on partial differential equations, general relativity, and kinetic theory. He has held postdoctoral positions at Princeton University (2018–2019) and Imperial College London (2016–2018), and completed his PhD in 2015 under Mihalis Dafermos at the University of Cambridge. Education: Martin Taylor earned his PhD in November 2015 from the University of Cambridge's Department of Pure Mathematics and Mathematical Statistics, supervised by Professor Mihalis Dafermos. His postdoctoral training includes roles at: Princeton University (Department of Mathematics and Gravity Initiative, 2018–2019) Imperial College London (Department of Mathematics, 2016–2018) Research Interests: Taylor’s work centers on partial differential equations (PDEs) , general relativity , and kinetic theory . He investigates stability properties of spacetime solutions, particularly focusing on black holes and cosmological models. His research integrates techniques from pure analysis, geometric PDEs, and mathematical physics to address fundamental questions in relativity. Publications: Taylor’s recent work emphasizes the stability of spacetime structures in relativity, including studies of Kerr black holes and Minkowski space. Earlier contributions include geometric analysis on sphere packing and collaborative efforts with leading researchers in the field. Advising & Grants: No formal advisees or grants are listed in the provided information. He has organized significant academic events such as the Münster--Imperial Day in PDE (2024) and workshops on Nonlinear Waves and Fluids (2024). His teaching includes Riemannian geometry courses at Imperial College London, alongside co-organizing seminars like the London PDE Seminar and London Analysis & Probability Seminar. Labs/Teams: Taylor is actively involved in Imperial’s Pure Analysis and PDE group and Geometry group, contributing to both research and academic leadership through event organization and collaborative initiatives.

• Wireless Communication
• Network Optimization
• Machine Learning
• +۳ مورد دیگر

Athanasios Gkelias is a Research Fellow in the Department of Electrical and Electronic Engineering at Imperial College London's Faculty of Engineering. His research focuses on advanced wireless communication systems, network optimization, machine learning applications, and quantum computing methodologies. He contributes to interdisciplinary projects involving IoT coalitions, adversarial signal detection, and cognitive behavior analysis. Key research areas include: (1) Network resource management in Software-Defined Networks (SDN), (2) Localization systems for indoor environments, (3) 3D reconstruction using photometric stereo and origami-based modeling, and (4) distributed optimization frameworks for ad-hoc networks. His recent work explores quantum approaches to combinatorial optimization and self-supervised learning methods. Publications span 20+ years with notable contributions in network coding, vehicular ad-hoc networks (VANETs), and cross-layer design for wireless mesh networks. His work emphasizes practical implementations through frameworks like iVisher for caller ID spoofing detection and cooperative MAC protocols for multi-antenna systems. No scientific awards are listed in the provided information. His research has been applied in healthcare technology through emotion understanding systems for Alzheimer’s patients and in battlefield communication through IoBT coalitions. Active participation in Imperial College's Engineering faculty reflects his commitment to advancing telecommunications and network science.

Gabriella Boscoمشاهده پروفایل

استاد

• Optical Communications
• Fiber Optics Transmission Systems
• Digital Signal Processing (DSP)
• +۳ مورد دیگر

Gabriella Bosco is a Full Professor at Politecnico di Torino in the Department of Electronics and Telecommunications. She specializes in optical communication systems, focusing on fiber optics transmission, digital signal processing (DSP), and nonlinear effects mitigation. Her roles include Editor-in-Chief of the IEEE/OSA Journal of Lightwave Technology and Board member of the IEEE Photonics Society. She earned her Telecommunication Engineering degree (1998) and PhD (2002) from Politecnico di Torino. Research Interests: Performance analysis and design of optical transmission systems Application of DSP in optical links Fiber-optic sensor networks and anomaly detection Nonlinear interference mitigation techniques Awards: OSA Fellow (2017) IEEE Fellow (2019) 2014 & 2015 Best Paper Award in Journal of Lightwave Technology Professional Service: Program Chair/General Chair of OFC 2017-2019 Editorial roles in major photonics journals EU-funded projects: E-Photon/One, Nobel, BONE Labs & Groups: Leading the OptCom Group, a pioneer in optical communications research since 2000. Collaborates with industry leaders including Huawei and Cisco.

John Rinzelمشاهده پروفایل

استاد

• Computational Neuroscience
• Biophysical Mechanisms
• Neural Computations
• +۳ مورد دیگر

John Rinzel is a Professor of Neural Science and Mathematics at New York University, affiliated with the Center for Neural Science and the Courant Institute. He holds academic positions within the College of Arts and Science and the Graduate School of Arts and Science. His research focuses on computational neuroscience, integrating biophysical mechanisms with mathematical modeling to understand neural computations at cellular and network levels. Education: Ph.D. in Mathematics (1973) and M.S. in Mathematics (1968) from NYU’s Courant Institute, and a B.S. in Engineering from the University of Florida (1967). Ph.D. in Mathematics, NYU Courant Institute (1973) M.S. in Mathematics, NYU Courant Institute (1968) B.S. in Engineering, University of Florida (1967) Research Interests: Rinzel studies biophysical mechanisms underlying neural computations, including dendritic computation, neuronal excitability, auditory pathway modeling, perceptual bistability, and rhythmic timing. His work combines theoretical models with experimental collaborations, emphasizing reduced biophysical models for cellular and network-level dynamics. Recent research trends include auditory streaming, perceptual dynamics, and rhythmic beat generation in music. His models explore gamma oscillations, thalamic spindle rhythms, and sleep-related neural excitability. Publications highlight applications in sensory processing, neural network oscillations, and cognitive functions like attention and memory coordination. No scientific awards explicitly listed. Rinzel leads a research group focusing on computational neuroscience, collaborating on projects involving auditory neuroscience, mathematical biology, and theoretical neurophysiology. His lab is part of NYU’s Center for Neural Science, fostering interdisciplinary approaches to brain function.

Mehrdad Ghandhari Alavijhمشاهده پروفایل

استاد

• Power System Dynamics
• Power System Stability and Control
• FACTS and HVDC Systems
• +۳ مورد دیگر

Mehrdad Ghandhari Alavijh is a Full Professor in Electric Power Systems at KTH Royal Institute of Technology. He leads research in power system dynamics, stability, and control, with a focus on FACTS (Flexible AC Transmission Systems), HVDC systems, and advanced control strategies. His work addresses challenges in grid reliability, renewable energy integration, and environmental sustainability. Education: Received M.Sc. (1995) and Ph.D. (2000) in Electrical Engineering from KTH. His research group develops innovative methods for secure, efficient power system operation, leveraging advanced monitoring and communication technologies. Research Interests: Power System Dynamics and Stability Transient Stability Assessment HVDC and FACTS Technologies Renewable Energy Integration Publications: Recent works focus on HVDC systems, battery storage, transient stability algorithms, and frequency control. His studies emphasize real-time monitoring and control strategies to enhance grid resilience against outages. Teaching: Supervises courses in power systems engineering, including degree projects in Electric Power Systems and Energy Innovation. Serves as an examiner and course responsible for multiple graduate programs.

Qi Tangمشاهده پروفایل

استادیار

• High-Performance Computing
• Computational Plasma Physics
• Fusion Simulations
• +۳ مورد دیگر

Qi Tang is an Assistant Professor in the School of Computational Science and Engineering (CSE) at Georgia Institute of Technology, part of the College of Computing. He joined Georgia Tech in 2024 after serving as a Staff Scientist at Los Alamos National Laboratory (LANL) from 2018 to 2024. His research focuses on computational plasma physics, high-performance computing, and scientific machine learning, with applications in fusion energy, plasma simulations, and structure-preserving neural networks. Education: Ph.D. in Applied Mathematics, Michigan State University, 2015 B.S. in Mathematics & Applied Mathematics, Zhejiang University, 2010 Research Interests: Qi’s work spans scalable numerical algorithms for exascale computing, fusion modeling, and scientific machine learning. Key areas include: High-order schemes, adaptive mesh refinement, and GPU acceleration for MHD and plasma simulations Structure-preserving neural networks for dynamical systems and multiscale physics Multi-physics modeling of tokamak disruptions and magnetic reconnection Grants & Collaborations: Principal Investigator (PI) for multiple DOE grants, including ASCR MMICC Center (CHaRMNET) Led a multi-institutional ASCR SciML team with LANL, ANL, and universities Recipient of LANL LDRD and NSF grants for fusion and plasma research Advising & Teaching: Advises Ph.D., master’s, and undergraduate students in CSE, physics, and engineering Teaches Parallel Computing Programming and Applications (CSE-6230) Co-advises students in DOE-funded programs and LANL collaborations Labs & Teams: Qi is affiliated with the DOE ASCR MMICC Center (CHaRMNET) and LANL’s Applied Mathematics and Plasma Physics Group. His team develops open-source tools like MFEM-based MHD solvers and structure-preserving ML frameworks.

• Visual Media Compression
• Machine Learning
• End-to-End Optimization
• +۵ مورد دیگر

Jona Ballé is an Associate Professor in the Department of Electrical and Computer Engineering at New York University Tandon School of Engineering. His research focuses on visual media compression, leveraging machine learning and end-to-end optimization to advance compression techniques for traditional and emerging modalities (e.g., AR, plenoptic imaging). He holds a PhD in signal processing from RWTH Aachen University (2012), followed by postdoctoral work at NYU’s Center for Neural Science and a Research Scientist role at Google (2017–2024). His contributions include foundational work on JPEG AI and leadership in conferences such as CLIC and DCC. Education: PhD in Signal Processing, RWTH Aachen University (2012) Master’s in Signal Processing, RWTH Aachen University (2007) Research: Ballé bridges machine learning and perceptual science to improve compression efficiency. Key areas include perceptual metrics, end-to-end optimization, and distributed coding. His work on Wasserstein distortion and Fourier basis models reflects cutting-edge advancements in compression theory. Impact: As co-organizer of the Challenge on Learned Image Compression (CLIC) and program committee member of the Data Compression Conference (DCC), he drives community progress. His industry collaborations and leadership in the JPEG AI standard highlight his translational research.

Gregory Eyinkمشاهده پروفایل

استاد

• Fluid mechanics
• Dynamical systems
• Partial differential equations
• +۵ مورد دیگر

Gregory Eyink is a Professor in the Department of Applied Mathematics & Statistics at the Whiting School of Engineering, Johns Hopkins University. He holds joint appointments in the Departments of Physics & Astronomy, Mathematics, and Mechanical Engineering. His research focuses on turbulence in fluids and plasmas, mathematical physics, dynamical systems, and nonequilibrium statistical physics. He contributed to the proof of the Onsager conjecture and studies of spontaneous stochasticity in turbulence. Education: Bachelor's in Mathematics and Philosophy, Ohio State University (1981) Ph.D. in Physics, Ohio State University (1987) Research Interests: Fluid mechanics, turbulence, and partial differential equations Mathematical physics and dynamical systems Geophysics, astrophysics, and plasma dynamics Statistical mechanics and renormalization group theory Awards & Honors: Fellow of the American Physical Society (APS) Fellow of the Institute of Physics (IOP) Stanislaw Ulam Visiting Scholar at Los Alamos National Laboratory (2005–2006) Grants & Labs: Recipient of funding from the Simons Foundation for turbulence research Co-author of seminal papers on turbulent reconnection and flux-freezing violations Developed stochastic Lagrangian models for vorticity dynamics Teaching: Current courses include Numerical Analysis and Turbulence Theory at Johns Hopkins.

Barry C. Sandersمشاهده پروفایل

استاد

• Quantum Computing
• Quantum Information Theory
• Quantum Algorithms
• +۳ مورد دیگر

Barry C. Sanders is a Professor and Director of the Institute for Quantum Science and Technology at the University of Calgary. He holds additional roles as Lead Investigator of the Alberta Major Innovation Fund Project on Quantum Technologies, a Thousand Talents Chair at the University of Science and Technology China, and Vajra Visiting Faculty at the Raman Research Institute. His research focuses on quantum computing, quantum algorithms, and optical implementations of quantum information tasks. Sanders has a PhD from Imperial College London (1987) and has held faculty positions at Macquarie University and the Australian National University. Education: BSc (University of Calgary, 1984), PhD (Imperial College London, 1987) Affiliations: Institute for Quantum Science and Technology, Alberta Major Innovation Fund Project His research interests include quantum-limited measurement, nonclassical light, quantum cryptography, and quantum algorithm development. He has pioneered work on Schrödinger cat states, qudit systems, and quantum error correction. Awards include Fellowships from the Institute of Physics, Optical Society of America, and Royal Society of Canada. His work bridges theory and application, with contributions to quantum technology commercialization and interdisciplinary collaborations. Editor-in-Chief, New Journal of Physics Past leadership roles: President of the Australian Optical Society, Founding Co-Chair of the Canadian Association of Physicists Division His recent publications explore quantum speedup in machine learning, topological quantum walks, and fault-tolerant quantum computing architectures.

Arnab Maityمشاهده پروفایل

دانشیار

• Functional Data Analysis
• Kernel Machine Regression
• Semiparametric Regression
• +۵ مورد دیگر

Arnab Maity is an Associate Professor in the Department of Statistics at North Carolina State University (NC State). His research focuses on functional data analysis, kernel machine regression, and semiparametric methods with applications in environmental epidemiology and epigenetics. He holds a Ph.D. in Statistics from Texas A&M University (2008), an M.S. from the same institution (2005), and a B.Stat. (Honors) from the Indian Statistical Institute (2003). Education: Ph.D. in Statistics, Texas A&M University, 2008 M.S. in Statistics, Texas A&M University, 2005 B.Stat. (Honors), Indian Statistical Institute, 2003 Research interests span functional data analysis, kernel methods, semiparametric inference, and their applications in genomic studies, environmental health, and epigenetics. He has contributed to statistical tools for analyzing high-dimensional gene-environment interactions and longitudinal data. Recent work includes advancements in robust kernel association testing and functional concurrent models. His publications reflect interdisciplinary collaboration, addressing challenges in genetic epidemiology, biostatistics, and environmental health. Notable grants include NIH-funded projects on imprint regulatory regions in childhood obesity and functional data integration in genomics. Awards include the ASA Noether Young Researcher Award (2014) and the Cavell Brownie Mentoring Award (2020). He has advised十余名博士生 and served on numerous editorial and review boards. Active in academic service, he chairs committees and organizes seminars on functional data analysis and statistical genetics.

Ngan Leمشاهده پروفایل

مدرس ارشد

• Numerical Analysis
• Stochastic Analysis and Modelling
• Partial Differential Equations
• +۵ مورد دیگر

Dr. Ngan Le is a Senior Lecturer in the School of Mathematics at Monash University, specializing in numerical analysis and stochastic partial differential equations. Her research focuses on theoretical and numerical advances in magnetic problems, anomalous sub-diffusion, and stochastic modeling. She has led or contributed to multiple research projects funded by the Australian Research Council, including work on mathematical models for magnetic devices and porous media systems. Dr. Le has published extensively in top-tier journals, with recent work addressing stochastic Stokes systems, Landau-Lifshitz equations, and fractional Fokker-Planck models. She is actively involved in supervising PhD students and collaborates internationally on topics spanning computational fluid dynamics and materials science. Education: Details not explicitly provided in the text. Her research interests span numerical methods for stochastic PDEs, finite element approximations, and nonlinear porous media equations. Key contributions include the development of robust numerical schemes for problems with variable-order fractional derivatives and multi-dimensional stochastic noise. Dr. Le's work emphasizes both theoretical rigor and practical applications in areas like magnetic materials and fluid dynamics. Recent publications highlight advancements in stochastic modeling of magnetic systems and diffusion processes, with a focus on long-term stability and error analysis. She has pioneered methods for the stochastic Landau-Lifshitz-Bloch equation and the Fokker-Planck equation with general forcing terms. Grants/Projects: Mathematics for future magnetic devices (2024-2027) A new numerical analysis for partial differential equations with noise (2023-2026) Mathematics for breaking limits of speed and density in magnetic memories (2020-2023) HDTHM: Mathematical and numerical methods for thermo-hydro-mechanical models in porous media (2019-2022) Dr. Le collaborates with leading institutions globally, including the Universities of Bremen, Tübingen, and Montpellier. Her research integrates computational techniques with stochastic analysis to address challenges in materials science and fluid dynamics.

Alexandre Poirierمشاهده پروفایل

دانشیار

• Econometrics
• Causal Inference
• Quantile Regression
• +۳ مورد دیگر

Alexandre Poirier is the Provost’s Distinguished Associate Professor of Economics at Georgetown University, where he has held positions since 2018. He previously served as an Assistant Professor at the University of Iowa (2013–2018). He holds a Ph.D. in Economics from UC Berkeley (2013), an M.A. in Statistics (2011), and a B.Sc. in Mathematics and Economics from Université de Montréal (2007). His research focuses on econometric theory, causal inference, and sensitivity analysis, with publications in top journals like Econometrica , Journal of Econometrics , and Quantitative Economics . He develops statistical software tools, including Stata modules regsensitivity and tesensitivity , and serves as an Associate Editor for Journal of Business & Economic Statistics and Econometric Reviews . His work emphasizes robustness to unconfoundedness assumptions and omitted variable bias. He teaches advanced econometrics at the Ph.D. and undergraduate levels, including courses on causal inference and panel data methods. Education Ph.D. in Economics, UC Berkeley, 2013 M.A. in Statistics, UC Berkeley, 2011 B.Sc. in Mathematics and Economics, Université de Montréal, 2007 Research Interests Prof. Poirier’s research bridges econometric theory and applied methods, with a focus on identifying causal effects under relaxed assumptions. Key areas include quantile regression, instrumental variables, sensitivity analysis for unobserved confounding, and panel data models. His work often addresses practical challenges in policy evaluation, such as omitted variable bias and endogeneity. Recent contributions include frameworks for comparing sensitivity parameters and assessing robustness of treatment effect estimates. Grants and Software Developed regsensitivity and tesensitivity Stata packages for regression and treatment effect sensitivity analysis Recipient of grants from the National Science Foundation and UC Berkeley’s Graduate Division Professional Service Associate Editor, Journal of Business & Economic Statistics (2021–present) Associate Editor, Econometric Reviews (2024–present) Moderator, arXiv Econometrics Section (2025–present) Referee for leading journals including American Economic Review , Econometrica , and Journal of the American Statistical Association