جستجوی استاد راهنمای دانشگاهی

Gholamreza Aminمشاهده پروفایل

دانشیار

• Performance Measurement and Management
• Mathematical Programming
• Productivity and Efficiency Analysis
• +۵ مورد دیگر

Gholamreza Amin is an Associate Professor of Management Science in the Faculty of Business at the University of New Brunswick (UNB), Canada. Previously, he taught at Sultan Qaboos University (Oman), where he received the Distinguished Research Award. He holds a Ph.D. in Operations Research from Azad University (2003), Iran. His roles include Associate Editor of the IMA Journal of Management Mathematics and SN Business and Economics , and participation in organizing international conferences. Dr. Amin's research focuses on Performance Measurement, Mathematical Programming, and Productivity Analysis using Data Envelopment Analysis (DEA), Ordered Weighted Averaging (OWA) Operators, and Inverse Optimization. He has published over 90 articles in top journals like European Journal of Operational Research and Annals of Operations Research , with over 3,200 citations. His recent work emphasizes DEA applications in mergers, efficiency evaluation, and environmental sustainability. Awards: UNB Merit Award (2019-2020), Teaching Excellence Award (2024), and Distinguished Research Award (Sultan Qaboos University). His teaching spans Operations Research courses including Management Science, Quantitative Methods, and Operations Management. He contributes to editorial work and conference organization, advancing the field's theoretical and applied dimensions.

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.

Eduardo Gildinمشاهده پروفایل

استاد

• Reservoir Modeling and Simulation
• Control and Optimization of Dynamical Systems
• Model Reduction Techniques
• +۵ مورد دیگر

Eduardo Gildin is a Professor of Petroleum Engineering and Associate Department Head for Graduate Studies at Texas A&M University's College of Engineering. He holds the L.F. Peterson '36 Professorship and directs the university's graduate studies in petroleum engineering. His research focuses on reservoir modeling, control optimization, model reduction techniques, and CO2 sequestration. Gildin has pioneered data-driven approaches for reservoir simulation, integrating machine learning and physics-based models to enhance efficiency and accuracy. Education: Ph.D. in Aerospace Engineering, University of Texas at Austin (2006) M.S. in Mechanical Engineering, University of São Paulo, Brazil (1998) B.S. in Mechanical Engineering, Faculdade de Engenharia Industrial, Brazil (1995) Research Interests: Model reduction of large-scale dynamical systems Control and optimization of reservoir operations CO2 storage and geological carbon sequestration Machine learning applications in reservoir engineering and drilling automation Geomechanics and compaction damage evaluation Key Awards: 2020: William O. and Montine P. Head Memorial Research Award 2017-2018: Dean of Engineering Excellence Award 2013-2019: Energi Simulation Chair in Robust Reduced Complexity Modeling 2021: Distinguished Membership in Society of Petroleum Engineers Grants and Advising: Gildin has secured major funding for projects on reservoir simulation, drilling automation, and CO2 storage. He advises graduate students on topics such as surrogate modeling and reinforcement learning applications in petroleum systems. His lab collaborates with industry partners to translate research into practical tools for reservoir management and subsurface operations. Labs and Teams: He leads the Reservoir Simulation and Control Lab, focusing on advanced computational methods for reservoir optimization. His team develops open-source drilling models and collaborates globally on projects like the DREAMS (Drilling and Extraction Automated System) initiative.

Olivier ALLIXمشاهده پروفایل

استاد

• Computational Mechanics
• Structural Mechanics
• Multiscale Modeling
• +۷ مورد دیگر

Olivier ALLIX is a Professor at the Laboratoire de Mécanique et Technologie (LMT) at École Normale Supérieure de Cachan (ENS-Cachan). His research focuses on computational mechanics, including multiscale modeling of composite materials, structural failure analysis, and non-intrusive coupling strategies. He has held leadership roles such as Head of LMT-Cachan and Vice-president of the International Association for Computational Mechanics (IACM). Expertise: Computational structural mechanics, material failure, inverse problems, and multiscale approaches. Editorial Roles: Associate editor of multiple journals including Computational Mechanics and Computer Methods in Applied Mechanics and Engineering . Awards: IACM Fellow, Euromech Fellow, and recipient of the Gay-Lussac Humboldt Prize (2019). His work integrates experimental mechanics with computational methods, emphasizing big data applications and model validation. He has organized major conferences like the World Congress on Computational Mechanics and co-led international research initiatives such as the IRTG ‘Virtual Material and Structures’ with Hannover University. Teaching includes advanced courses on structural dynamics, composite materials, and computational mechanics at the Master’s level. His research group collaborates with industries like Safran, IFPEN, and DGA on projects involving fatigue analysis, mooring systems, and composite testing.

Efthymios N. Karatzasمشاهده پروفایل

استادیار

• Numerical Analysis
• Computational Mathematics
• Optimal Control
• +۵ مورد دیگر

Efthymios N. Karatzas serves as an Assistant Professor in the Department of Mathematics at Aristotle University of Thessaloniki, Faculty of Sciences, within the Computer Science and Numerical Analysis Section. He maintains an active research profile in computational mathematics with strong institutional affiliations including collaborations with SISSA mathLab and FORTH Institute of Applied and Computational Mathematics. His academic credentials include: PhD in Mathematics, National Technical University of Athens (2015) Master's in Applied Mathematical Sciences – Computational Mathematics, NTUA (2009) Master's in Applied Mathematics, University of Patras (2001) Bachelor's in Mathematics (Computational Mathematics), University of Patras (1999) Dr. Karatzas' research program centers on advanced numerical techniques for partial differential equations , with pioneering work in reduced order modeling , embedded boundary methods , and optimal control systems . His expertise spans computational fluid dynamics, uncertainty quantification, and biomechanical applications, characterized by methodological innovation in handling geometrically complex domains through cut finite element approaches and shifted boundary formulations. Analysis of his 15 most recent publications reveals a cohesive research trajectory focused on developing efficient numerical frameworks for parametrized PDE systems. His work consistently bridges theoretical rigor with practical implementation, particularly in advancing reduced basis methods for fluid-structure interaction and biological modeling, demonstrating significant contributions to computational mathematics through high-impact journal publications. No major scientific awards are documented in the available sources. Dr. Karatzas demonstrates research leadership through project management roles including Scientific Manager for the ELIDEK project at NTUA (2019-2021) and Project Manager for the European Social Fund HEaD initiative at SISSA (2017-2019). His grant administration experience encompasses coordinating interdisciplinary teams and securing external funding for computational mathematics research. He maintains active collaborations with the SISSA mathLab in Trieste (particularly with Prof. Gianluigi Rozza's group) and the FORTH Institute in Crete, participating in international workshops including the Reduced Order Methods in CFD Summer School (2019) and SIAM UQ conferences. His research network spans computational mathematics groups across Europe with emphasis on advancing numerical methodologies for real-world engineering and biological applications.

Sara van de Geerمشاهده پروفایل

استاد

• Mathematics for machine learning
• High-Dimensional statistics
• Empirical processes
• +۲ مورد دیگر

Sara van de Geer is a Full Professor at the Seminar for Statistics within the Department of Mathematics at ETH Zürich since 2005. She previously held academic positions at the University of Leiden, Université Paul Sabatier (Toulouse), and others. She earned a Master's (1982) and Ph.D. (1987) in Mathematics from Leiden University. Her research focuses on high-dimensional statistics, empirical processes, and mathematical foundations of machine learning. Van de Geer has received prestigious recognitions including the Van Wijngaarden Award (2016), Knight in the Order of Orange-Nassau (2015), and membership in Leopoldina (2013). She served as President of the Bernoulli Society (2015–2017) and Chair of the Seminar for Statistics at ETH Zürich. Her contributions include landmark works on statistical learning theory and high-dimensional inference, with key publications in top journals like Annals of Statistics and SIAM/ASA Journal on Uncertainty Quantification. Her academic leadership includes organizing Saint Flour Lectures, Wald Lectures (2016), and delivering plenary lectures globally. Her research bridges theoretical statistics with applied methodologies, emphasizing rigorous mathematical frameworks for modern data analysis challenges.

Cheng Huangمشاهده پروفایل

استادیار

• Computational Fluid Dynamics
• Aerospace Propulsion
• Data-driven Modeling
• +۳ مورد دیگر

Cheng Huang is an Assistant Professor in the Department of Aerospace Engineering at the University of Kansas. His research focuses on computational fluid dynamics, aerospace propulsion, turbulent combustion modeling, and reduced-order modeling techniques. He is affiliated with the Computational AeroPropulsion Laboratory and can be contacted at chenghuang@ku.edu. Education: B.S. from Shanghai Jiaotong University M.S. and Ph.D. from Purdue University Research Interests: LES Modeling of Turbulent Reacting Flows Data-Driven and Reduced-Order Modeling of Complex Fluid Flows Combustion Instability Analysis in Aerospace Propulsion Recent Work Trends: His publications emphasize reduced-order modeling techniques for rocket combustion dynamics, rotating detonation engines, and multiscale fluid systems. Key methodologies include projection-based models, data-driven approaches, and nonlinear approximations of latent dynamics.

Ngoc Cuong Nguyenمشاهده پروفایل

پژوهشگر

• Computational Mechanics
• Molecular Mechanics
• Nanophotonics
• +۷ مورد دیگر

Ngoc Cuong Nguyen is a Principal Research Scientist in the Department of Aeronautics and Astronautics at MIT and a member of the MIT Center for Computational Engineering. His research focuses on computational mechanics, numerical simulation, and advanced numerical methods such as hybridizable discontinuous Galerkin (HDG) methods for multi-scale and multi-physics problems. Education: PhD in High Performance Computation for Engineered Systems (2005), National University of Singapore BEng in Aeronautical Engineering (2001), Ho Chi Minh City University of Technology Research Interests: Computational Mechanics, Molecular Mechanics, Nanophotonics Numerical Simulation & Optimization, Scientific Computing, Machine Learning Reduced Basis Methods, High-Order Methods (e.g., HDG), Uncertainty Quantification Key Projects: Development of HDG methods for fluid dynamics, structural mechanics, and electromagnetics Plasmonic nanostructure simulations using quantum hydrodynamic models Space weather modeling via GPU-accelerated HDG approaches Optimization of photonic crystals and nanostructured materials Large-eddy simulation (LES) of hypersonic flows and buffet phenomena Labs & Teams: Active contributor to the MIT Center for Computational Engineering, leading projects in numerical methods, computational fluid dynamics, and interdisciplinary applications of advanced simulation techniques.

Benjamin Stammمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• High-Performance Computing (HPC)
• +۷ مورد دیگر

Prof. Benjamin Stamm is a Professor of Numerical Mathematics at the University of Stuttgart, leading the Chair of Numerical Mathematics for High Performance Computing within Faculty 08. He holds a Ph.D. and master's degree in mathematics from École Polytechnique Fédérale de Lausanne (EPFL) and has previously worked at RWTH Aachen University, Sorbonne Université UPMC Paris 6, UC Berkeley, and Brown University. His research focuses on numerical analysis, scientific computing, and simulations, particularly efficient discretizations for PDEs, eigenvalue problems, error certification, reduced basis methods, and HPC implementations. He develops scalable numerical methods for problems in computational chemistry and physics, emphasizing accuracy, efficiency, and interdisciplinary collaboration with chemists, physicists, and materials scientists. Prof. Stamm’s work includes contributions to domain decomposition methods, polarization energy calculations, and software development like the ddX library. His publications span topics such as model order reduction, quantum simulations, and molecular dynamics. Collaborations and software tools underscore his commitment to bridging computational methods with real-world scientific challenges.

Daniel McKenzieمشاهده پروفایل

استادیار

• Derivative-free optimization
• Implicit neural networks
• Geometric methods in data science
• +۳ مورد دیگر

Daniel McKenzie is an Assistant Professor in the Department of Applied Mathematics and Statistics at the Colorado School of Mines. His research focuses on derivative-free optimization, implicit neural networks, and geometric methods in data science. He holds a B.Sc.(hons) and M.Sc. in Mathematics from the University of Cape Town (2010, 2014) and a PhD in Mathematics from the University of Georgia (2019). B.Sc.(hons): Mathematics and Applied Mathematics, University of Cape Town, 2010 M.Sc.: Mathematics, University of Cape Town, 2014 PhD: Mathematics, University of Georgia, 2019 His research explores the intersection of optimization theory and machine learning, with applications in spatial data modeling, geometric data analysis, and high-dimensional clustering. Recent work emphasizes curvature-aware algorithms, comparison-based optimization, and implicit network architectures like LatticeVision. His methods address challenges in non-stationary spatial data and convex game equilibria prediction. Key contributions include Fermat distance metrics for clustering, Jacobian-Free Backpropagation (JFB) for implicit networks, and zeroth-order algorithms for black-box optimization. While no scientific awards are listed, his publications reflect a strong focus on advancing optimization techniques for modern data science problems. No specific grants or advising roles are detailed in the provided text. His work bridges computational mathematics and applied AI, with potential applications in robotics, spatial statistics, and algorithmic game theory.

Jacquelien Scherpenمشاهده پروفایل

استاد

• Automation & Control Systems
• Nonlinear Control Systems
• Smart Energy Systems
• +۳ مورد دیگر

Jacquelien Scherpen is a Professor at the University of Groningen, currently serving as Rector Magnificus. She holds a position in the Engineering and Technology institute Groningen (ENTEG) within the Faculty of Science and Engineering. Her academic journey includes a PhD in Applied Mathematics from the University of Twente (1994) and post-doctoral roles at Delft University of Technology before joining Groningen in 2006. She has been a leader in systems and control research, with roles as Scientific Director of ENTEG (2013–2019) and Director of the Groningen Engineering Center (2016–2023). Her research focuses on model reduction, nonlinear control, smart energy systems, and distributed control applications in smart grids. Key achievements include the Automatica Best Paper Prize (2020), Knight of the Order of the Netherlands Lion (2019), and the Prince Friso Engineer of the Year Award (2023). She has held leadership roles in organizations like the European Control Association (EUCA) and is a Fellow of IEEE. Her professional activities include editorial roles in journals like the IEEE Transactions on Automatic Control and the International Journal of Robust and Nonlinear Control. She has organized major conferences, including the European Control Conference (ECC) 2021 and 2023. Her work bridges theoretical advancements with practical applications, emphasizing sustainability and engineering innovation.

Fabio Nobileمشاهده پروفایل

استاد

• Scientific Computing
• Uncertainty Quantification
• Numerical Analysis
• +۵ مورد دیگر

Fabio Nobile is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Department of Mathematics (MATH), holding the CADMOS Chair in Scientific Computing and Uncertainty Quantification. He leads the CSQI (Chair of Scientific Computing and Uncertainty Quantification) group. His work focuses on numerical methods for partial differential equations (PDEs), uncertainty quantification, stochastic modeling, and computational fluid dynamics. He is involved in collaborative projects involving fluid-structure interaction, cardiac electro-mechanics, and energy systems. Professor Nobile has extensive teaching experience, including courses on advanced analysis, stochastic simulation, and numerical integration of stochastic differential equations. He supervises numerous PhD students and has contributed to over 200 peer-reviewed publications, covering topics such as low-rank approximation methods, multilevel Monte Carlo techniques, and optimal control under uncertainty. His research emphasizes interdisciplinary applications, including biomedical engineering (e.g., personalized cardiac simulations) and renewable energy (e.g., probabilistic load forecasting). He collaborates with industries and academic institutions globally, advancing computational methodologies for engineering and scientific challenges.

Ata Zadehgolمشاهده پروفایل

استاد

• Computational Electromagnetics
• Signal and Power Integrity
• Applied Electromagnetics
• +۱۲ مورد دیگر

Ata Zadehgol is an Associate Professor (promoted to Full Professor in 2025) in the Department of Electrical and Computer Engineering at the University of Idaho, College of Engineering. He is the founding director of the Applied Computational Electromagnetics and Signal/Power Integrity (ACEM-SPI) Laboratory. His academic journey includes a Ph.D. from the University of Illinois at Urbana-Champaign (2011), an M.S. from UC Davis (2006), and a B.S. from the University of Washington (1996). He spent over a decade in the microelectronics industry before joining academia. Ph.D., Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 2011 M.S., Electrical and Computer Engineering, University of California, Davis, 2006 B.S., Electrical Engineering, University of Washington, Seattle, 1996 Dr. Zadehgol's research focuses on computational electromagnetics , signal and power integrity , and modeling of multi-scale and stochastic systems . His work spans from low-frequency to terahertz regimes, with recent expansion into quantum electrodynamics and photonics. He develops advanced computational algorithms for efficient and stable modeling of electromagnetic systems, including FDTD methods, reduced-order modeling, and machine learning applications. The research articles highlight a consistent focus on electromagnetic modeling , signal integrity , and computational efficiency . Key themes include FDTD sub-gridding, stochastic surface roughness in waveguides, stability of transfer functions, and macro-modeling for antennas and interconnects. The publications span IEEE Transactions, Applied Mathematics and Computation, and Electronics, reflecting interdisciplinary work bridging engineering, physics, and numerical methods. Best Poster-Paper Award, IEEE EDAPS, 2016 University of Idaho Presidential Mid-Career Award, 2020 Outstanding Faculty Award, College of Engineering, 2025 NSF Recognition for Novel Algorithm for Optical Interconnects, 2018 Dr. Zadehgol has secured significant research funding from the National Science Foundation (NSF) , NASA , Micron Technology , and Schweitzer Engineering Laboratories (SEL) . He advises graduate students in the ACEM-SPI Lab, though specific names are not listed. His lab supports research in computational electromagnetics, signal/power integrity, and quantum engineering applications. Future work includes advancing modeling techniques for quantum systems and high-frequency electronics. The Applied Computational Electromagnetics and Signal/Power Integrity (ACEM-SPI) Laboratory , which he founded and directs, serves as the central hub for his research group. The lab focuses on algorithm development for electromagnetic simulation, signal integrity analysis, and emerging applications in quantum science. It is supported by federal and industrial grants and collaborates with partners in academia and industry.

Wenlong Mouمشاهده پروفایل

استادیار

• Statistical Learning
• Reinforcement Learning
• Optimization
• +۸ مورد دیگر

Wenlong Mou is an Assistant Professor at the University of Toronto's Department of Statistical Sciences, with additional affiliation at the Vector Institute for AI. His research develops optimal statistical methods and efficient algorithms for data-driven decision-making, focusing on reinforcement learning, stochastic approximation, and causal estimation. He teaches advanced courses in theoretical statistics (STA3000) and stochastic processes (STA447/2006). Education: Ph.D. in EECS, University of California, Berkeley (2023) B.Sc. in Computer Science and Economics, Peking University Research Focus: Mou's work bridges statistical theory with machine learning practice. Key areas include: Theoretical foundations of reinforcement learning (e.g., Bellman equations, continuous-time systems) Efficient algorithms for semi-parametric estimation and debiasing Non-asymptotic analysis of stochastic optimization and MCMC methods High-dimensional statistical inference and causal modeling Publication Trends: Recent articles (2023–2025) emphasize reinforcement learning theory (policy evaluation, adaptive interpolation), causal inference (debiased estimators, propensity scores), and statistical computing (diffusion processes, Langevin algorithms). Methodological rigor and non-asymptotic guarantees characterize his work. Awards: INFORMS APS Student Paper Competition Finalist (2022) Advising and Labs: Actively recruiting PhD students with backgrounds in mathematics or deep learning. Students access GPU clusters via the Vector Institute. Grants unspecified in sources.

Carmen Guerra-Garciaمشاهده پروفایل

دانشیار

• Aerospace Engineering
• Low-Temperature Plasma Technologies
• Gas Discharge Physics
• +۶ مورد دیگر

Carmen Guerra-Garcia is the Charles Stark Draper Associate Professor of Aeronautics and Astronautics at MIT. She leads the Aerospace Plasma Group, focusing on the intersection of aerospace engineering, low-temperature plasma technologies, and gas discharge physics. Massachusetts Institute of Technology (PhD, SM) 2007-2014 Polytechnic University of Madrid (Ingeniero Aeronautico) 2007 Her research spans aircraft safety (lightning interaction, wind turbines, and charge control systems), plasma-assisted combustion , and space propulsion . She combines multi-physics modeling , computation , and experimentation to address challenges in carbon-free energy and aerospace electrification. Key trends in her recent work include plasma-assisted ignition of sustainable fuels , CO2 conversion for Mars ISRU , and dynamic interactions between plasmas and flames . Her articles highlight advancements in nanosecond pulsed discharges , self-pulsating streamers , and lightning risk mitigation . NSF CAREER Award (2024) Junior Bose Award for Excellence in Teaching (2024) Office of Naval Research Young Investigator Award (2021) International Fulbright Science and Technology Award (2009) First National Award in Aeronautical Engineering, Spanish Ministry of Education (2007) Guerra-Garcia’s grants include NSF CAREER funding for plasma-assisted combustion and ONR support for aerospace safety. She advises PhD students and collaborates with the Space Propulsion Lab and APS/AIAA technical committees.