جست‌وجوی استاد و پژوهشگر

Dan Jiaoمشاهده پروفایل

استاد

• Computational Electromagnetics
• Computational Multiphysics
• Electronic Design Automation (EDA)
• +۸ مورد دیگر

Prof. Dan Jiao is the Synopsys Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School. She leads the Rapid-Heterogeneous Integration (Rapid-HI) Design Institute and serves as Editor-in-Chief of the IEEE Journal on Multiscale and Multiphysics Computational Techniques. Her research focuses on computational electromagnetics, multiphysics modeling, and AI-driven design automation for advanced integrated circuits and quantum systems. She has held academic positions since 2005, progressing from Assistant to Full Professor, and has extensive industry experience at Intel Corporation (2001–2005). Education: PhD in Electrical Engineering, University of Illinois at Urbana-Champaign (2001) Senior Staff Engineer at Intel Corporation (2001–2005) Research Interests: Fast numerical methods for large-scale electromagnetic analysis AI/ML integration in design automation (EDA/MDA) Quantum circuits and spin qubit systems Heterogeneous integration and advanced packaging Multiphysics co-simulation for nano-scale devices Signal/power integrity in high-speed systems Key Projects: Leads the NSTC AIDRFIC program (first NSTC R&D Jump Start project), the DARPA NGMM Rapid-HI Design Institute, and the GENIE-RFIC generative design tool initiative. Also directs the Consortium for Electromagnetic Science and Technology. Awards & Honors: 2022 ACES Computational Electromagnetics Award IEEE Fellow (2016) Intel Outstanding Researcher Award (2019) MTT-S Distinguished Microwave Lecturer (2020–2023) 2013 Schelkunoff Prize Paper Award Advising & Grants: Advised over 30 PhD/master's students and led projects funded by NSF, DARPA, Intel, SRC, and industry partnerships. Key grants include NSF CAREER (2008), ONR Young Investigator (2006), and multiple industry-sponsored initiatives. Labs & Teams: Rapid-HI Design Institute (DARPA NGMM) Quantum device co-design group Multiphysics modeling team

Yuri Bazilevsمشاهده پروفایل

استاد

• Computational Mechanics
• Isogeometric Analysis
• Fluid-Structure Interaction
• +۵ مورد دیگر

Yuri Bazilevs is the E. Paul Sorensen Professor of Engineering at Brown University's School of Engineering and Co-Director of the Mechanics of Undersea Science and Engineering Center. His research focuses on computational mechanics, isogeometric analysis, fluid-structure interaction, and high-performance computing. Prior to Brown, he held positions at UC San Diego, where he advanced to Full Professor in 2014 after a rapid tenure. He earned his PhD in 2006 and postdoc training in computational engineering at UT Austin's ICES. Research interests span computational fluid dynamics, solid mechanics, and advanced discretization methods like isogeometric analysis (IGA) and meshfree approaches. He has developed novel formulations for complex phenomena such as underwater explosions, composite material failure, and hypersonic flow dynamics. His work integrates cutting-edge numerical methods with practical engineering applications in aerospace, energy, and biomedical systems. Recent publications highlight advancements in IGA for architected materials, RKPM-based crack modeling, and stabilized formulations for compressible flows. His contributions bridge theoretical mechanics with computational innovation, addressing challenges in multiphysics coupling and large-scale simulations. Collaborations span academia and industry, emphasizing practical validation and real-world impact. Bazilevs' expertise includes variational multiscale methods, peridynamics for fracture mechanics, and immersive particle methods for fluid-structure interaction. His work has been applied to wind turbine aerodynamics, gas turbine optimization, and cardiovascular flow analysis. He actively contributes to computational infrastructure development, such as the tIGAr software framework for IGA automation.

• Parallel Computing
• Scientific Computing
• Adaptive Mesh Refinement
• +۳ مورد دیگر

Martin Berzins is a Professor of Computer Science at the University of Utah, affiliated with the School of Computing and the Scientific Computing and Imaging (SCI) Institute. His research focuses on parallel scientific computing, numerical methods for partial differential equations, and high-performance computing frameworks. He is a leading developer of the Uintah framework, a scalable simulation tool used for large-scale engineering and scientific problems. Research Interests : Parallel algorithms, adaptive mesh refinement, material point method (MPM), exascale computing, computational fluid dynamics, and performance portability. His work emphasizes scalable software solutions for complex multiscale and multiphysics simulations, with applications in environmental modeling, explosive detonation analysis, and computational mechanics. Recent articles highlight advancements in Uintah's portability to exascale systems, error estimation in MPM, and high-order numerical methods. Berzins has contributed significantly to the development of task-based parallelism strategies and heterogeneous computing optimizations. His research bridges theoretical numerical analysis with practical large-scale computational challenges. Collaborations include DOE projects on hazard analysis and exascale computing. He has pioneered the integration of runtime systems like Hedgehog with Uintah to enhance scalability on modern architectures. His work ensures computational frameworks remain viable for emerging hardware trends, emphasizing both algorithmic innovation and software engineering rigor.

Jacob Fishمشاهده پروفایل

استاد

• Multiscale Computational Science
• Computational Mechanics
• Homogenization Methods
• +۵ مورد دیگر

Jacob Fish is the Robert A.W. and Christine S. Carleton Professor and Chair of the Department of Civil Engineering and Engineering Mechanics at Columbia University. He directs the Multiscale Science and Engineering Center and leads Columbia's Computational Science and Engineering initiative (iCSE), coordinating 65+ faculty. With 35 years of pioneering research, he specializes in multiscale computational methods bridging aerospace, automotive, and healthcare industries. His research integrates multiscale computational science with applications in: Homogenization and reduced-order methods for complex materials Stochastic modeling of heterogeneous systems Coupled thermo-chemo-electro-mechanical processes Data-physics driven frameworks for industrial processes Recent work emphasizes AI-enhanced modeling for composites, porous media, and environmental systems. His 15 most recent publications (2023-2025) demonstrate strong trends toward: Data-physics integration in manufacturing (e.g., resin transfer molding) Multiscale environmental applications (canopy flows, CO2 mineralization) Advanced numerical methods (discontinuous Galerkin, solver-free homogenization) Digital twin development for composite lifecycle management Scientific Awards & Honors: 2018 JSCES Grand Prize 2010 IACM Computational Mechanics Award 2005 USACM Computational Structural Mechanics Award 2003 Rensselaer Research Award Fellowships: AAM, USACM, IACM Two Best Paper awards He founded the commercial Multiscale Designer software suite (250+ global clients) and secured major grants including an NSF-DFG collaboration on thermoplastic interfaces. His textbooks are used in 200+ universities worldwide. Leads the Multiscale Science and Engineering Center focusing on industrial-scale computational challenges and mentors researchers through Columbia's iCSE initiative. Former President of USACM and current IACM Vice-President for the Americas.

Ahmed F. Ghoniemمشاهده پروفایل

استاد

• Energy Conversion
• Computational Fluid Dynamics
• Combustion Dynamics
• +۵ مورد دیگر

Professor Ahmed F. Ghoniem is the Ronald C. Crane (1972) Professor of Mechanical Engineering at MIT, directing the Center for Energy and Propulsion Research and the Reacting Gas Dynamics Laboratory. He holds a B.Sc. and M.Sc. from Cairo University and a Ph.D. from the University of California, Berkeley. His research focuses on computational methods in fluid-thermal sciences, turbulent combustion, energy conversion systems, and CO2 capture technologies. He has authored over 500 publications and mentored over 100 students, many of whom are leaders in academia and industry. His research interests include multiscale simulations of turbulent reactive flows, clean energy systems, and advanced combustion technologies. He has pioneered work on oxy-fuel combustion, gasification processes, and ion transport membrane reactors. Ghoniem’s contributions span fundamental science and applied engineering, addressing challenges in sustainable energy and environmental sustainability. Honors: ASME James Harry Potter Gold Medal (2015), AIAA Propellant and Combustion Award (2016), Fellowships from ASME, APS, and The Combustion Institute. Service: Leadership roles in MIT’s Energy initiatives, KAUST collaborations, and advisory boards for energy research centers. Extensive contributions to curriculum development and graduate education in mechanical engineering. Labs/Teams: Directs the Reacting Gas Dynamics Lab and leads the Center for Energy and Propulsion Research, focusing on integrated energy systems and CO2 capture innovations.

C. Armando Duarteمشاهده پروفایل

استاد

• Computational Mechanics
• Structural Engineering
• Multiscale Modeling
• +۵ مورد دیگر

Prof. C. Armando Duarte is the Nathan M. Newmark Distinguished Professor in Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign (UIUC). He holds affiliations with the Computational Science and Engineering Program and the National Center for Supercomputing Applications (NCSA). His research focuses on computational mechanics, particularly the Generalized Finite Element Method (GFEM/XFEM), multiscale modeling, fracture mechanics, and hydraulic fracturing. He has authored/co-authored over 100 publications and co-edited books on computational methods. Education: Ph.D. (1996) and M.Sc. (1991) in Engineering Mechanics from the University of Texas at Austin and Federal University of Santa Catarina, respectively, and a B.Sc. in Mechanical Engineering from the Federal University of Pernambuco. Professional Roles: Professor at UIUC since 2015, previously at the University of Alberta and visiting roles at institutions in Brazil, Portugal, and the Netherlands. He serves on editorial boards of computational mechanics journals and chairs committees for professional societies like USACM and ASCE. Research Interests: Multiscale problems, computational fracture mechanics, meshfree methods, and multiphysics simulations. His work emphasizes 3D fracture analysis, hydraulic fracturing, and thermal gradient modeling. Awards: Recognitions include the Nathan Newmark Professorship, USACM Fellowship, and multiple best paper awards. His work has been cited over 9,000 times, with papers featured in top journals like Computer Methods in Applied Mechanics and Engineering . Teaching: Renowned for excellence in courses like Finite Element Methods, Structural Analysis, and Computational Plates and Shells. His teaching accolades span over two decades at UIUC.

Kevin Clarnoمشاهده پروفایل

دانشیار

• Nuclear and Radiation Engineering
• Computational Nuclear Energy
• Multiphysics Simulation
• +۵ مورد دیگر

Kevin Clarno is a tenured Associate Professor in the Department of Nuclear and Radiation Engineering at the University of Texas at Austin, holding the Charlotte Maer Patton Centennial Fellowship in Engineering. His research focuses on computational nuclear energy, multiphysics reactor simulation, and high-performance computing (HPC). Previously, he spent 15 years at Oak Ridge National Laboratory (ORNL), where he led major initiatives such as the Consortium for Advanced Simulation of Light Water Reactors (CASL) and contributed to the development of software tools like SCALE, CTF, and VERA. Education and Career: Assistant Professor at University of Tennessee-Knoxville (2010–2016) Senior Research Scientist at ORNL (2006–2021) Research Interests: Multiphysics coupling methods for reactor simulation Multiscale neutronics and thermal-hydraulics modeling Advanced reactor design (e.g., molten salt reactors) HPC-driven software integration for nuclear analysis Uncertainty quantification in coupled simulations Grants and Projects: Lead of CASL’s Physics Integration Focus Area Development of the Advanced Multi-Physics (AMP) fuel code ORNL-led strategic research projects in reactor simulation Labs and Tools: VERA: Virtual Environment for Reactor Applications CTF: Thermal-hydraulic solver for PWR analysis MPACT: Neutronics simulation tool within SCALE

Gregory J. Wagnerمشاهده پروفایل

دانشیار

• Additive Manufacturing
• Multi-scale Simulations
• Multi-physics Modeling
• +۴ مورد دیگر

Gregory J. Wagner is an Associate Professor of Mechanical Engineering and Director of Graduate Studies at Northwestern University's McCormick School of Engineering. His research focuses on developing computational methods for multi-scale and multi-physics problems in additive manufacturing, fluid dynamics, and heat transfer. He leads the Wagner Research Group, which specializes in high-performance computing tools for complex engineering simulations. Education includes a Ph.D., M.S., and B.S. in Mechanical Engineering from Northwestern University and Boston University. His work integrates machine learning with traditional computational methods to model material behavior, microstructure evolution, and process-structure-property relationships in advanced manufacturing. Notable contributions include the GO-MELT framework for thermal simulations and the C-HiDeNN neural network approach for large-scale systems. Research interests span additive manufacturing process modeling, multiphysics coupling, and data-driven approaches for material design. Awards include the Bette and Neison Harris Chair in Teaching Excellence. Publications emphasize thermal modeling, phase change phenomena, and computational fluid dynamics innovations. His lab's work bridges mesoscopic and multiscale modeling, with applications in energy systems, biomedical devices, and environmental engineering. Collaborations focus on experimental validation and industrial-scale simulation challenges.

Travis B. Thompsonمشاهده پروفایل

استادیار

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

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

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.

Caglar Oskayمشاهده پروفایل

استاد

• Multiscale Computational Modeling
• Composite Materials
• Structural Failure Analysis
• +۷ مورد دیگر

Caglar Oskay is the Cornelius Vanderbilt Professor of Engineering and Chair of the Department of Civil and Environmental Engineering at Vanderbilt University. He is also a Professor of Mechanical Engineering. His research focuses on multiscale computational modeling of material and structural systems under extreme conditions, with expertise in composite materials, failure mechanisms, and computational mechanics. Dr. Oskay earned his Ph.D. in Civil Engineering from Rensselaer Polytechnic Institute (2003) and has held academic roles there before joining Vanderbilt in 2006. He was honored as an ASME Fellow in 2017 and as a Chancellor Faculty Fellow in 2016. Key research areas include multiscale failure modeling, life prediction of heterogeneous materials, and computational methods for composites and multiphysics systems. His work integrates advanced simulation techniques with experimental validation, addressing challenges in infrastructure resilience, additive manufacturing defects, and quantum computing applications in engineering. Education: Ph.D., Civil Engineering, Rensselaer Polytechnic Institute (2003) M.S., Civil Engineering, Rensselaer Polytechnic Institute M.S., Applied Mathematics, Rensselaer Polytechnic Institute B.S., Civil Engineering, Middle East Technical University Recent research highlights include stochastic modeling of geotechnical infrastructure failures, quantum-enhanced finite element methods, and predictive analytics for additive manufacturing defects. He leads interdisciplinary efforts on backward erosion piping in flood protection systems and has secured grants for multiscale modeling of titanium alloys and composites. Awards: ASME Fellow (2017) Chancellor Faculty Fellow (2016) Advising and grants: Dr. Oskay’s grants include NSF-funded studies on backward erosion piping and quantum computing applications. His research group collaborates with industry partners on materials for aerospace and energy sectors, emphasizing computational tools for failure prediction and material design. His work bridges computational mechanics with practical engineering challenges, advancing methods for infrastructure resilience, advanced materials, and sustainable design through multiscale modeling innovations.

Zhen Liمشاهده پروفایل

استادیار

• Multiscale/Multiphysics modeling
• Soft matter and Smart materials
• Biophysics and Collective dynamics
• +۷ مورد دیگر

Dr. Zhen Li is an Assistant Professor in the Department of Mechanical Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. He joined Clemson in August 2019 after serving as a research associate professor at Brown University and a postdoctoral research associate at University of California, Merced. Education: Ph.D. in Fluid Mechanics, Shanghai University, 2012 MS in Fluid Mechanics, Shanghai University, 2008 BS in Engineering Mechanics, Wuhan University, 2005 Dr. Li's research focuses on multiscale modeling of soft matter, complex fluids, biophysics, and collective dynamics using both bottom-up (coarse-grained molecular modeling) and top-down (from continuum descriptions to fluctuating hydrodynamics) approaches, along with high-performance computing. His work spans mathematical theory for coarse-graining and model reduction, statistical methods and machine-learning approaches applied to multiscale modeling, memory effects in complex fluids, and concurrent coupling of heterogeneous solvers for scale-bridging. Analysis of Dr. Li's recent publications reveals a strong trend toward integrating machine learning with traditional computational methods, particularly neural operators for multiscale problems. His work spans diverse applications from bubble dynamics and blood flow to materials science and bioprinting, demonstrating the versatility of his computational approaches across multiple disciplines in engineering and physics. Awards and Recognition: CECAS Dean's Professor Award (2024) Award of Excellence - Junior Faculty (2021-2022) Best Research Poster Award at SC19 (2019) 2nd Place Award of Best Poster Presentation at DOE/EFRC AIM for Composites meeting (2024) Dr. Li actively mentors PhD students including Miles Lu, Ryan Wan, Haizhou Wen, and Ali Mohammadi, who have published significant research in computational mechanics. His research is supported by multiple grants including an NSF Elements grant as PI for 'SciMem: Enabling High Performance Multi-Scale Simulation on Big Memory Platforms', an NSF CDS&E grant as co-PI for 'HAM3R: Heterogeneous Automated Management of Multiscale Methods and Resources', a DOE/EFRC grant as Thrust lead co-PI for 'AIM for Composites', and a NASA EPSCoR grant as Science-PI. Dr. Li leads the MuthComp (Multiscale theory and Computation) research group, which focuses on developing interfaces between Engineering, Applied Mathematics, Physics-based Machine Learning, and High Performance Scientific Computing. The group has active collaborations with institutions including Idaho National Laboratory, University of Tokyo, and Brown University, and has developed open-source software including USERMESO for GPU-accelerated DPD simulations.

Sorin Mitranمشاهده پروفایل

استاد

• Computational simulation of multiscale, multiphysics systems
• Data-driven constitutive relations for hyperelastic materials
• Nonlinear mode reduction
• +۴ مورد دیگر

Sorin Mitran is a Professor in the Department of Mathematics at the University of North Carolina at Chapel Hill. His research focuses on computational simulation of multiscale and multiphysics systems, data-driven constitutive relations for hyperelastic materials, and information geometry for reduced stochastic models. PhD in Aerospace Engineering from Politehnica University Bucharest (1995) Professional background includes fellowships at University of Tokyo (1993), Karlsruhe Institute of Technology (1998-1999), and University of Washington (1999-2002) His research develops numerical tools to predict macro-scale behavior from micro-scale interactions, such as plastic deformation of metals from lattice defect dynamics, microtubule mechanics from molecular dynamics, and protein folding from atomic-level simulations. Mathematical approaches include adaptive computation, machine learning for constitutive law prediction, and information geometry for stochastic process analysis. Recent publications (2023-2018) span computational biology, multiscale fluid dynamics, and medical applications of continuum mechanics. Articles frequently explore data-driven modeling, wave propagation in biological systems, and GPU-accelerated numerical methods like Lattice Boltzmann and Lattice Fokker-Planck formulations.

John Mooreمشاهده پروفایل

استادیار

• Computational mechanics
• Materials science
• Engineering design
• +۴ مورد دیگر

Dr. John Moore is an Assistant Professor in the Department of Mechanical Engineering at Marquette University . He leads the Computational Mechanics of Materials Laboratory, focusing on computational mechanics, materials science, and high-performance computing. His research spans alloys, polymers, and biomedical devices. Education Ph.D., Mechanical Engineering, Northwestern University (2015) M.S.E., Civil Engineering, University of Washington (2007) B.S., Aeronautical and Astronautical Engineering, University of Washington (2005) Research Focus Dr. Moore's work combines computational modeling with experimental validation to understand material behavior under extreme conditions. Key areas include: Crystal plasticity modeling of metallic alloys Nonlocal damage mechanics for fatigue prediction Dynamic spallation and porosity evolution High-throughput X-ray imaging of additively manufactured materials UV-sensitive resin material modeling Recent Publications His recent work focuses on advanced computational techniques for material failure analysis, including: Nonlocal approaches for statistical fatigue prediction Microinertia effects in spall modeling Betatron X-ray tomography applications UV resin optimization studies Phase transformation fatigue mechanisms Contact Email: john.a.moore@marquette.edu | Phone: (414) 288-6641 Location: Haggerty Hall, 225, Marquette University, Milwaukee, WI 53201

Pu Zhangمشاهده پروفایل

دانشیار

• Composite Materials
• Architected Materials
• Liquid Metal Technologies
• +۵ مورد دیگر

Pu Zhang is an Associate Professor and Undergraduate Studies Director in the Department of Mechanical Engineering at Binghamton University (State University of New York at Binghamton). He leads the Composite and Architected Materials Group, focusing on advanced materials research. Previously, he held postdoctoral positions at the University of Manchester and earned his PhD from the University of Pittsburgh, with earlier degrees from Hunan University. Education background includes a B.S. and M.S. in Mechanics from Hunan University (China), followed by a Ph.D. in Mechanical Engineering from the University of Pittsburgh (USA). His research interests revolve around the mechanics, design, and manufacturing of composite and architected materials, particularly those incorporating liquid metals. Key areas include: Development of soft conductive composites and metamaterials Advanced manufacturing techniques like additive manufacturing and hybrid methods Multiscale and multiphysics modeling for material behavior prediction Recent publications highlight advancements in liquid metal-based composites, additive manufacturing innovations, and multiscale modeling approaches. These works address challenges in material fabrication, characterization, and application in fields like soft robotics and wearable electronics. He has received notable recognitions, including the NSF CAREER Award (2022) and the Watson Early-Stage Distinguished Research Award (2024). Zhang has taught courses such as Intro to Solid Mechanics and Mechanics of Composites. He has mentored over seven PhD and MS students and holds multiple patents. His research is supported by NSF, IEEC, and industry partnerships. His lab, the Composite and Architected Materials Group, fosters interdisciplinary research in soft functional materials and advanced manufacturing technologies.