جست‌وجوی استاد و پژوهشگر

Alistair Revellمشاهده پروفایل

استاد

• Turbulence Modelling
• Computational Fluid Dynamics (CFD)
• Bioengineering
• +۸ مورد دیگر

Alistair Revell is a Professor of Computational Engineering & Flow Physics at The University of Manchester, within the School of Engineering's Department of Mechanical and Aerospace Engineering. He holds leadership roles as Head of the Fluids and Environment Department and Deputy Head of the School of Engineering, contributing to the Future of Engineering project. His research focuses on turbulence modelling, computational fluid dynamics (CFD), and biomedical engineering applications, with expertise in high-performance computing and fluid-structure interaction. Revell has pioneered work in hybrid RANS-LES methods, machine learning for turbulence simulation, and interactive engineering tools using virtual reality. He has led projects in automotive/aerospace industries and nuclear energy sectors, emphasizing sustainable engineering solutions. Revell earned a MEng from UMIST (2002) and a PhD in Turbulence Modelling and CFD from The University of Manchester (2006). His academic journey includes research placements at ENSMA (France), IMFT (Toulouse), and Stanford University. He has held roles such as Temporary Lecturer, Director of Social Responsibility, and Deputy Head of Department. His work integrates computational methods with real-world applications, including cardiovascular flow analysis and energy harvesting systems. Research interests span turbulence simulation, biomedical CFD, and bioinspired engineering innovations. Recent articles highlight advancements in uncertainty quantification for wave-structure interactions, cardiovascular hemodynamics analysis, and machine learning-driven turbulence models. Revell's interdisciplinary projects address global challenges in energy efficiency, healthcare diagnostics, and sustainable engineering practices. He supervises PhD students in topics like physics-informed machine learning for turbulence models and bioinspired energy harvesting. Revell collaborates with institutions like the University of Melbourne, CIEMAT (Spain), and the Modelling & Simulation Centre (MaSC). His contributions align with UN Sustainable Development Goals for clean energy, health, and innovation.

Diego Rossinelliمشاهده پروفایل

استاد مدعو

• Computational Fluid Dynamics
• High-Performance Computing
• Multiphase Flow Simulations

Diego Rossinelli is an Adjunct Professor at Stanford University specializing in advanced computational methods for aerospace and biomedical systems. His research integrates high-performance computing with complex fluid dynamics, particularly in combustion systems and biomechanics.

Jan S Hesthavenمشاهده پروفایل

استاد

• Computational Mathematics
• Numerical Analysis
• Discontinuous Galerkin Methods
• +۷ مورد دیگر

Jan S Hesthaven is a Professor of Mathematics and Dean of the School of Basic Sciences at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He holds adjunct professorships at Brown University and the Technical University of Denmark. His research focuses on computational methods for partial differential equations, model order reduction, and high-order numerical methods like discontinuous Galerkin schemes. He leads the Chair of Computational Mathematics and Simulation Science (MCSS) and founded SCITAS, EPFL's Scientific IT and Application Support unit. Education: M.Sc. (1991) and Ph.D. (1995) in Numerical Analysis from Technical University of Denmark, with postdoctoral training at Brown University and NASA Langley. He earned a Dr.Techn. (2009) for contributions to nodal discontinuous Galerkin methods. Research interests include computational fluid dynamics, seismic monitoring of porous media, and machine learning integration with PDE solvers. Notable contributions include physics-informed neural networks, reduced basis methods for acoustics, and surrogate models for gravitational waves. He has received prestigious awards like the SIAM Fellowship (2014) and the Philip J. Bray Teaching Award (2004). Leadership roles: Dean of SB (2017–present), Director of CCV at Brown (2006–2013), and Deputy Director of ICERM (2010–2013). His work bridges fundamental mathematics with applications in engineering, physics, and environmental science. Grants and collaborations include NSF funding for model reduction and NASA partnerships. He supervises interdisciplinary projects on seismic data analysis and acoustic simulations. SCITAS, under his leadership, provides high-performance computing support across EPFL.

• Optimization Algorithms
• Machine Learning
• Multi-objective Optimization
• +۳ مورد دیگر

Dr Angus Kenny is a Casual Academic at UNSW Canberra within the Engineering and Technology Teaching school. His research focuses on optimization algorithms, machine learning, and their applications in engineering and mining. He has contributed to advancements in multi-objective optimization, automated machine learning pipelines, and structural design problems. Key research interests include developing hybrid algorithms for optimization tasks, improving hyperparameter search in machine learning frameworks (e.g., TPOT), and addressing complex problems in mining engineering such as constrained pit optimization and production scheduling. His work bridges theoretical algorithm development with practical engineering challenges. Kenny has published extensively in conferences like GECCO and journals like Scientific Reports, focusing on evolutionary computation and optimization techniques. He has no listed scientific awards but maintains an active publication record in computational engineering and machine learning domains. No advising or grant details are provided in the available text. His research activities are centered on algorithmic innovation and their real-world applications in engineering systems.

Vivin Vinodمشاهده پروفایل

پژوهشگر ارشد

• Multifidelity Methods
• Quantum Chemistry
• Machine Learning
• +۴ مورد دیگر

Vivin Vinod is a post-doctoral Research Associate at the University of Wuppertal, Germany, working in the field of scientific computing and high performance computing. His educational background includes: Bachelor's (Hons) in Physics with minors in Mathematics and Philosophy Master of Science in Economics with a focus on advanced computational simulations for econometrics and machine learning for financial swap assessment PhD in Computer Science from the University of Wuppertal Dr. Vinod's research specializes in multifidelity methods applied to quantum chemistry, with extensions into active learning and uncertainty quantification frameworks. His interdisciplinary work bridges computational physics, machine learning, and financial modeling, demonstrating expertise in developing algorithms for complex systems across scientific and economic domains.

Omar Ghattasمشاهده پروفایل

استاد

• Computational Geosciences
• Inverse Problems
• Uncertainty Quantification
• +۹ مورد دیگر

Dr. Omar Ghattas is a Professor and the Ernest & Virginia Cockrell Chair in Engineering at The University of Texas at Austin, leading the Center for Computational Geosciences within ICES. He holds courtesy appointments in Computer Science, Biomedical Engineering, the Institute for Geophysics, and the Texas Advanced Computing Center. His research focuses on computational geosciences, inverse problems, and large-scale simulation of geophysical and biological systems. Prior to UT Austin, he was a professor at Carnegie Mellon University for 16 years, earning degrees from Duke University (BS, MS, PhD, 1984–1988). Education: BS, MS, PhD in Engineering from Duke University (1984–1988). Affiliations: Director of the KAUST-UT Austin Alliance, member of multiple editorial boards, and recipient of prestigious awards including the Gordon Bell Prize. His research interests span computational geosciences, inverse problems, uncertainty quantification, and high-performance computing. Key areas include mantle convection modeling, seismic wave propagation, ice sheet dynamics, and subsurface flows. He has pioneered methods for large-scale inverse problems on supercomputers, addressing challenges in computational efficiency and statistical rigor. Recent work emphasizes real-time Bayesian inference for disaster prediction (e.g., tsunamis) and advanced neural operator techniques for high-dimensional Bayesian inverse problems. His publications explore topics like derivative-informed neural operators, multifidelity sampling, and scalable computational frameworks for geophysical applications. Awards: Allen Newell Medal, Gordon Bell Prize, SIAM Fellowship, and multiple HPC awards. Grants & Leadership: Directorships of ICES research centers and international alliances, funded by NSF and DOE. Labs/Teams: The Center for Computational Geosciences in ICES, collaborations with KAUST, and leadership in interdisciplinary computational science initiatives.

• Uncertainty Quantification
• Data-driven Methods
• Turbulent Flows
• +۴ مورد دیگر

Saleh Rezaeiravesh is a Lecturer in Engineering Simulation and Data Science within the Department of Mechanical and Aerospace Engineering. He holds a PhD in Scientific Computing (Numerical Analysis) from Uppsala University (2018) and previously worked at the FLOW Centre, KTH Royal Institute of Technology. His research focuses on Computational Fluid Dynamics (CFD), uncertainty quantification (UQ), and data-driven techniques applied to turbulent flows. He teaches courses such as Numerical Methods & Computing and Data-driven Modelling & Simulation. He is part of the active Fluids Research Group, contributing to projects on turbulent flow dynamics and CFD applications. Education: PhD in Scientific Computing (Uppsala University, 2018); MSc and BSc in unspecified fields. Research interests include scale-resolving simulations of wall-bounded turbulent flows, Bayesian optimization, and multifidelity modeling. He develops tools like the UQit Python package for uncertainty quantification in CFD. His work aligns with UN Sustainable Development Goals through contributions to efficient fluid dynamics modeling, aiding sustainable engineering solutions. Recent research trends in his articles emphasize numerical uncertainties, precision effects in simulations, and advanced machine learning techniques for turbulence modeling. His contributions address challenges in CFD accuracy, robustness, and computational efficiency. He actively supervises PhD students in areas like uncertainty quantification and data-driven optimization of fluid dynamics problems. He collaborates on interdisciplinary projects, including porous-fluid systems and flow control using Bayesian methods. His team participates in global research networks, with notable collaborations on turbulence statistics and LES modeling.

Akil Narayanمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• Approximation Algorithms
• +۳ مورد دیگر

Akil Narayan is a Professor in the Department of Mathematics and a member of the Scientific Computing and Imaging (SCI) Institute at the University of Utah. His office is located in WEB 4666 (SCI) and LCB 116 (Math). He has previously held positions as Assistant Professor at the University of Massachusetts Dartmouth (2012-2015) and Visiting Assistant Professor at Purdue University (2009-2012). His educational background includes: Ph.D. in Applied Mathematics from Brown University (2009) M.Sc. in Applied Mathematics from Brown University (2004) B.S. in Engineering Sciences and Applied Mathematics from Northwestern University (2003) B.S. in Electrical Engineering from Northwestern University (2003) Akil Narayan's primary research interests lie in numerical analysis, scientific computing, and approximation algorithms. His work spans multiple domains including uncertainty quantification, multifidelity modeling, optimization, and computational methods for partial differential equations. He has made significant contributions to the development of numerical methods for solving complex computational problems across various scientific and engineering disciplines. His research often bridges theoretical mathematics with practical applications in fields such as biomedical engineering, ecology, and power systems. Analysis of his recent publications reveals a strong focus on uncertainty quantification, multifidelity methods, and scientific machine learning. His work increasingly integrates traditional numerical methods with modern machine learning techniques, particularly in the development of physics-informed neural networks. There's also a notable emphasis on structure-preserving numerical methods and optimization techniques for computational models. His research has significant applications in biomedical imaging, particularly in electrocardiographic imaging and cardiac modeling. While specific scientific awards are not detailed in the available information, his extensive publication record in top-tier journals demonstrates recognition in his field. His work appears regularly in prestigious journals such as SIAM Journal on Scientific Computing, Journal of Computational Physics, and SIAM Review. Professor Narayan has advised numerous graduate students through the Department of Mathematics and the School of Computing at the University of Utah. His current advisees include Filip Belik, Haoyu Chen, John Turnage, and Yinqian Yu, working on topics ranging from numerical methods for PDEs to operator learning and uncertainty quantification. His former students have gone on to positions at institutions including General Motors, Amazon, Intel Corporation, and various academic institutions. He has also secured research funding supporting his work in computational mathematics and scientific computing, though specific grant details are not provided in the available text. He is actively involved with the Scientific Computing and Imaging (SCI) Institute at the University of Utah, where he collaborates with researchers across disciplines. His work through the UncertainSCI project focuses on uncertainty quantification for computational models in biomedicine and bioengineering, particularly in cardiac applications. He frequently collaborates with researchers in the Department of Mathematics, School of Computing, and the SCI Institute on interdisciplinary projects that combine mathematical theory with practical computational applications.

Parisa Khodabakhshiمشاهده پروفایل

استادیار

• Nonlocal Mechanics
• Fracture Mechanics
• Model Reduction
• +۲ مورد دیگر

Parisa Khodabakhshi is an Assistant Professor in the Department of Mechanical Engineering and Mechanics at Lehigh University's P.C. Rossin College of Engineering and Applied Science. Her work focuses on nonlocal continuum mechanics, fracture mechanics, and computational methods. Ph.D. in Civil Engineering (Structural Engineering), Texas A&M University, 2018 M.S. in Civil Engineering (Earthquake Engineering), Sharif University of Technology, 2009 B.S. in Civil Engineering, Sharif University of Technology, 2007 Her research interests include nonlocal mechanics , fracture mechanics , model order reduction , computational mechanics , and multifidelity methods , targeting advanced predictive modeling for material failure and multiscale systems. Contact: pak322@lehigh.edu

Christophette Blanchet-Scallietمشاهده پروفایل

مدرس

• Applied Probability
• Statistics
• Stochastic Processes
• +۴ مورد دیگر

Christophette Blanchet-Scalliet is a Lecturer in the Department of Mathematics and Computer Science at École Centrale de Lyon, affiliated with the Camille Jordan Institute (UMR CNRS 5208). She obtained her doctorate in 2001 and her Habilitation to Supervise Research in 2016. Currently serving as Director of the Mathematics and Computer Science Department and Head of the Dual Diploma program at Centrale Lyon-ENSAE, she has been active in academia since 2002, with positions at both École Centrale de Lyon (since 2007) and the University of Nice Sophia-Antipolis (2002-2007). Her research spans applied probability , statistics , stochastic processes , stochastic control , Kriging , and robust optimization . She has developed significant expertise in Ornstein-Uhlenbeck processes, backward stochastic differential equations, Hamilton-Jacobi-Bellman equations, and applications in financial mathematics. Her work bridges theoretical probability with practical applications in risk assessment, insurance, and optimization under uncertainty. Analysis of her recent publications reveals a consistent focus on stochastic processes and their applications, with increasing emphasis on computational methods, sensitivity analysis, and optimization techniques. Her research shows strong connections between theoretical probability and practical applications in finance, insurance, and environmental risk assessment, with growing interdisciplinary collaboration across mathematical fields. Dr. Blanchet-Scalliet has supervised five doctoral students since 2015: Mélina Ribaud (2015-2018), Laura Gay (2016-2019), Thierry Gonon (2019-2022), Benoit Nieto (2021-2024), and Noé Fellmann (2021-2024), typically in co-supervision with colleagues from related research groups. She has secured significant research funding through multiple projects including the CIROQUO Consortium (2020-2028), ANR DREAMES (2021-2025), ANR Oquaido Chair (2015-2020), and others. She is actively involved in the CIROQUO research consortium as co-leader, which brings together multiple academic institutions and industry partners including École Centrale de Lyon, Mines Saint-Etienne, University of Toulouse 3, Stellantis France, BRGM, CEA, IFP Energies Nouvelles, and others to advance research in uncertainty quantification and optimization.

• Computational Methods
• Numerical Analysis
• Control Theory
• +۹ مورد دیگر

Boris Kramer is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of California San Diego , affiliated with the Jacobs School of Engineering . He leads research in computational methods for control , optimization , and uncertainty quantification of complex systems, with applications in space weather modeling , systems biology , and soft robotics . Center for Extreme Events Research (CEER) Center for Computational Mathematics (CCoM) Air Force Center of Excellence Multi-Fidelity Modeling His work focuses on reduced-order modeling (ROM) and data-driven methods that preserve physical structures (e.g., energy conservation in Hamiltonian systems). Recent projects include collaborations with Samsung Electronics for semiconductor manufacturing optimization and leadership in DOE's PSAAP IV program for radiation resilience modeling. Kramer's research has been featured in Nature Computational Science and Science News , with grants from NSF , DoD , and AFOSR . His group has advised students like Opal Issan (published in Journal of Computational Physics) and Nate Linden (Nature Communications). Scientific awards include: NSF CAREER Award (2022) DoD Newton Award for Transformative Ideas (2020) Outstanding PhD Student Award (2024-2025, UCSD MAE) MURI funding for Digital Twins (2023) Outreach efforts include participation in the Barrio Logan Science & Art Expo and the Southeast San Diego STEM Ecosystem , emphasizing science communication for K-12 audiences.