جست‌وجوی استاد و پژوهشگر

Armin Wehrfritzمشاهده پروفایل

استادیار

• Computational Fluid Dynamics (CFD)
• Fluid Mechanics
• Turbulence
• +۵ مورد دیگر

Armin Wehrfritz is an Assistant Professor in the Department of Mechanical Engineering at the University of Turku. His research focuses on high-fidelity numerical simulations of multiphase and chemically reacting flows, particularly involving low-carbon fuels like hydrogen. He holds a Master's degree from the University of Kaiserslautern and a Ph.D. from Aalto University. During his doctoral studies, he was a visiting researcher at Eindhoven University of Technology, and later worked as a Research Associate at the University of New South Wales in Sydney. His expertise spans computational fluid dynamics (CFD), turbulence modeling, combustion physics, and high-performance computing (HPC), with a growing interest in machine learning applications for numerical methods. Education: Doctoral Degree: Aalto University, Finland Master's Degree: University of Kaiserslautern, Germany Research interests include: Direct numerical simulation (DNS) and large-eddy simulation (LES) of combustion processes Hydrogen integration in compression-ignition engines Development of advanced micro-mixing models for transported PDF methods Data-driven approaches for optimizing combustion systems His recent work emphasizes reducing carbon emissions through hydrogen-diesel dual-fuel systems and improving simulation accuracy for engine-relevant conditions. Collaborations have involved institutions in the Netherlands, Australia, and the U.S., focusing on topics like plasma-ignited hydrogen jets and NOx reduction mechanisms.

John H. Reifمشاهده پروفایل

استاد

• DNA Computing
• Molecular Assembly
• Robot Motion Planning
• +۴ مورد دیگر

John H. Reif is a Professor of Computer Science at Duke University, with secondary appointments in the Thomas Lord Department of Mechanical Engineering and Materials Science (since 2024) and the Department of Electrical and Computer Engineering (since 2016). His research spans DNA computing , molecular assembly , robot motion planning , and quantum computation , focusing on programmable biomolecular systems and parallel algorithms. Education: Ph.D. in Applied Mathematics, Harvard University (1977) M.S. in Applied Mathematics, Harvard University (1975) B.S. in Applied Mathematics and Computer Science, Tufts University (1973) His recent publications emphasize DNA strand displacement , molecular-scale learning systems , and 3D DNA nanostructures , with applications in diagnostics, data storage, and nanofabrication. Articles highlight innovations in error-resilient DNA circuits, programmable self-assembly, and algorithmic modeling of molecular processes. Scientific Awards: Fellow of the American Association for the Advancement of Science (AAAS) Fellow of the Association for Computing Machinery (ACM) Fellow of the Institute of Electrical and Electronics Engineers (IEEE) He has supervised numerous Ph.D. students and postdoctoral researchers , including Rajiv Nagipogu (adaptive molecular systems) and Xin Song ( Daniel Fu , DNA computation on cell membranes). His work is funded by grants from the Defense Advanced Research Projects Agency (DARPA) and the National Science Foundation (NSF) , with recent focus on molecular-scale AI and DNA polymerase reaction networks. Reif co-founded Domus Diagnostics , developing affordable infectious disease testing solutions. He served as General CoChairman of FNANO24 and contributes to teaching courses like Computational Complexity and Molecular Assembly and Computation .

Maxx Q. Arguillaمشاهده پروفایل

استادیار

• Materials Chemistry
• Inorganic Chemistry
• Solid-State Chemistry
• +۷ مورد دیگر

Maxx Q. Arguilla is an Assistant Professor in the Department of Chemistry at the University of California, Irvine (UCI), with affiliations in the Department of Chemical and Biomolecular Engineering and the Solutions that Scale Initiative. He is also a member of the advisory boards for the Eddleman Quantum Institute and Chemical & Engineering News . Education: B.S. in Chemistry from University of the Philippines Diliman (2011, cum laude); Ph.D. in Inorganic Chemistry from The Ohio State University (2017); Postdoctoral Fellow at MIT under Prof. Mircea Dincă. Dr. Arguilla's research focuses on the discovery and chemical understanding of one-dimensional and quasi-1D crystalline materials with sub-nanometer thickness, often described as 'molecular pasta' due to their topological resemblance to spaghetti, rigatoni, and fusilli. His work bridges gaps between 0D, 1D, and 2D materials, aiming to uncover novel electronic, optical, and quantum phenomena. He employs advanced synthesis and characterization techniques to grow high-quality crystals and study their anisotropic properties. Although no specific publications are listed in the provided texts, his research themes consistently center on low-dimensional inorganic solids , particularly van der Waals materials and metal-organic frameworks, with implications for quantum computing, optoelectronics, and nanoscale devices. His work emphasizes curiosity-driven exploration and hacking crystallization design rules across the periodic table. His scientific achievements have been recognized with several prestigious awards: Runner-up, Journal of Materials Chemistry Lectureship (2024) NSF CAREER Award C&EN Talented Twelve (2023) Matter's 35 PIs under 35 (2024) Finalist, Dream Chemistry Award (2024) Dr. Arguilla advises graduate students and leads an independent research group at UCI. He has received major research grants including the NSF CAREER Award, supporting his long-term vision in materials discovery. He is actively involved in scientific outreach, particularly with middle school students, and promotes diversity in STEM. He is also a photographer and food enthusiast, drawing creative parallels between cooking and materials synthesis. He is affiliated with key research entities at UCI: Eddleman Quantum Institute (Advisory Board) Solutions that Scale Initiative (Affiliate Faculty) Department of Chemical and Biomolecular Engineering (Affiliate Faculty)

• Fluid Dynamics
• Computational Fluid Dynamics
• Hypersonic Flows
• +۵ مورد دیگر

Giuseppe Pascazio serves as a Full Professor in the Department of Mechanics, Mathematics & Management at the Polytechnic University of Bari, Italy. His research spans computational fluid dynamics with dual focus on aerospace applications and biomedical engineering, particularly in hypersonic flow phenomena and microwave ablation technologies for cancer therapy. His primary research interests include fluid dynamics, computational methods for high-enthalpy flows, thermochemical non-equilibrium modeling, turbulent boundary layer analysis, and biomedical device optimization. Pascazio develops advanced numerical techniques including high-order schemes, state-to-state kinetics implementations, and GPU-accelerated solvers to address complex flow physics in atmospheric entry and medical applications. His work bridges fundamental gas dynamics with practical engineering solutions for spacecraft thermal protection and minimally invasive cancer treatments. Analysis of his recent publications (2021-2025) reveals three dominant research thrusts: (1) High-fidelity simulation of hypersonic flows with detailed chemistry using state-to-state kinetics, (2) Development of robust numerical methods for shock-capturing in thermochemically non-equilibrium flows, and (3) Biomedical applications focusing on microwave ablation probe design and microcapsule transport in vascular systems. His aerospace work emphasizes atmospheric reentry physics while biomedical research targets cancer therapy optimization. Pascazio has participated in significant research projects including "PrInCE" (Innovative Processes for Energy Conversion) and "INNOVHEAD" (Advanced technologies for reduction of polluting emissions in Heavy Duty engines). His collaborative work involves industrial partnerships in aerospace and medical device sectors, though specific grant details beyond project names aren't provided. He maintains active research output with over 50 publications demonstrating consistent contributions to high-speed aerodynamics and biomedical fluid dynamics.

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.

• Industrial and Applied Mathematics
• Computational Science
• Reservoir Simulation
• +۶ مورد دیگر

Zhangxing (John) Chen is a Professor in the Schulich School of Engineering at the University of Calgary , specializing in the Department of Chemical and Petroleum Engineering . His research focuses on industrial and applied mathematics, computational science, reservoir simulation, and modeling of multi-phase flow in porous media, with applications to the oil and energy sectors, CO2 sequestration, and semiconductor device simulations. He holds the NSERC Industrial Research Chair in Reservoir Simulation and the AITF (iCORE) Industrial Chair in Reservoir Modeling , leading collaborative projects with industry giants like Suncor, Nexen Energy, and China National Petroleum Corp. His work has resulted in 15 patents and a stellar publication record of 16 books and over 500 refereed journal papers. Key Research Areas: Industrial and Applied Mathematics, Reservoir Simulation, Porous Media Flow, CO2 Sequestration, Semiconductor Device Simulations Scientific Awards: CAIMS-Fields Industrial Mathematics Prize (2017) Dr. Chen has trained over 100 graduate students and postdocs, emphasizing advanced computing algorithms and practical applications in energy recovery. His contributions bridge theoretical mathematics with real-world industrial challenges.

• Quantum Physics
• Quantum Computing
• Quantum Control
• +۳ مورد دیگر

Michael Biercuk is a Professor and Director of the Quantum Control Laboratory at the University of Sydney. He holds a dual role as founder and CEO of Q-CTRL, a quantum technology company. His academic work focuses on quantum control, quantum firmware, and trapped ion systems, with applications in quantum computing, quantum metrology, and quantum simulation. Biercuk earned his undergraduate degree from the University of Pennsylvania and his Master's and PhD from Harvard University. He has held research fellowships at NIST Boulder and advised agencies like DARPA. Education: BA (University of Pennsylvania), MSc/PhD (Harvard University) Research interests include developing quantum control techniques to suppress errors in qubits, engineering quantum firmware for scalable systems, and exploring trapped ion-based quantum sensors. His lab combines theory and experiment, leveraging ultra-high-vacuum systems and precision lasers to study quantum coherence. Awards include the 2021 Australian Financial Review 'Most Innovative Companies' recognition, 2015 Eureka Prize for Outstanding Early Career Researcher, and multiple innovation accolades. His work bridges academia and industry, with collaborations spanning Tsinghua University, MIT, and NIST. Key Projects: Quantum Control & Firmware, Quantum Simulation of Many-Body Systems, Quantum Metrology with Ions

Sherief Redaمشاهده پروفایل

استاد

• Machine Learning
• Computer Architecture
• Energy Efficiency
• +۵ مورد دیگر

Sherief Reda is a Professor of Engineering and Computer Science at Brown University's School of Engineering. He leads the SCALE lab, focusing on energy-efficient computing, digital chip design, embedded systems, and machine learning applications. His research bridges hardware design and combinatorial optimization, with over 130 publications and five US patents. Reda has secured $21M+ in research funding from NSF, DoD, DARPA, and industry partners like Samsung and Intel. Education: PhD in Computer Science & Engineering, UC San Diego (2006) MS in Computer Science, Ain Shams University (2000) BS in Computer Science, Ain Shams University (1998) Research Interests: His work spans resource-efficient AI, approximate computing, and interdisciplinary applications in social sciences. Recent projects include chemical-based computing and thermal management for high-performance chips. Reda's lab explores machine learning for combinatorial optimization, with practical applications in logistics and hardware design. Publications & Awards: Over 130 articles in top venues like IEEE Transactions and Nature Communications. Recipient of the NSF CAREER Award (2021) and IEEE Fellow (2020). His work on energy-efficient computing earned him the AAIA Fellowship. Grants & Collaborations: Principal Investigator on NSF, DoD, and industry-funded projects. Collaborators include Prof. Kim (Chemistry) and Prof. Rose (Engineering). Reda also serves as an Amazon Scholar and expert witness in patent litigation. Labs & Teams: Directs the SCALE lab, which develops open-source EDA tools and novel thermal simulation frameworks like PACT. His team pioneered techniques like ABACUS for approximate circuit synthesis and LoCool for energy efficiency in servers.

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.

H. Metin Aktulgaمشاهده پروفایل

دانشیار

• High Performance Computing
• Parallel Computing
• Big Data Analytics
• +۳ مورد دیگر

H. Metin Aktulga is an Associate Professor in the Department of Computer Science and Engineering at Michigan State University's College of Engineering. His research focuses on high-performance computing, parallel algorithms, and numerical methods for large-scale scientific applications. He leads interdisciplinary projects involving collaborations with computational physicists and materials scientists to develop scalable software systems. His work includes the development of PuReMD, a reactive molecular dynamics code, and DOoC+LAF, a task-based middleware for data analytics. He explores parallel computing on emerging architectures, emphasizing energy efficiency and performance optimization. His research spans applications in molecular modeling, nuclear physics, and computational biology. Awards and grants are not explicitly listed, but his contributions are highlighted through collaborations with projects like MFDn (nuclear structure) and SHINES (electronic structure computations). He advises students in computational methods and leads efforts to automate force field optimization using machine learning and big data analytics. Labs and teams include the High-Performance Computing group at MSU, with active participation in interdisciplinary initiatives to bridge simulation and data-driven discovery in materials science and quantum systems.

Neil Sandhamمشاهده پروفایل

استاد

• Transonic Aerodynamics
• Hypersonic Flows
• Turbulent Flows
• +۳ مورد دیگر

Professor Neil Sandham is a Professor of Aerospace Engineering at the University of Southampton, leading research in transonic and hypersonic aerodynamics. He has held this position since 1999, following roles at Stanford University (PhD 1989), DLR Göttingen, and Queen Mary, University of London. His expertise spans numerical simulations of transitional and turbulent flows, with a focus on high-performance computing and aerodynamic design. Research interests include transonic buffet phenomena, hypersonic flows with thermo-chemical non-equilibrium, and flow over rough surfaces. Current projects involve Exascale computing using OpenSBLI, shocklet dynamics, and space entry systems like MEESST. Teaching includes modules on aerothermodynamics and computational aerodynamics. He supervises PhD students in engineering and environmental systems. External roles include Fellow of the Royal Aeronautical Society (since 2004) and editorial board membership for AIAA Journal (2019–2021). Key collaborations involve EPSRC-funded projects such as the UK Turbulence Consortium and EU initiatives like EXAFLOW. His work bridges academic research and industrial applications in aerospace engineering.

Jennifer Sinclair Curtisمشاهده پروفایل

استاد

• Particle Technology
• Granular Flow Dynamics
• Fluidization
• +۴ مورد دیگر

Jennifer Sinclair Curtis is the Dean of the UC Davis College of Engineering since 2015. Previously, she served as the Associate Dean for Research and Facilities at the University of Florida’s College of Engineering, Chair of Chemical Engineering, and Head of Freshman Engineering at Purdue University. She holds a B.S. in Chemical Engineering from Purdue University and M.S./Ph.D. from Princeton University. Her research focuses on particle flow behavior, with applications in food processing, pharmaceuticals, aerospace, energy, mining, and natural processes like debris flows. She employs computational methods (DEM, CFD) to study granular flows, fluid-particle interactions, and material dynamics. Her work bridges engineering education and industry, emphasizing diversity and inclusion initiatives. Key awards include Fellowships from AAAS, AIChE, and ASEE. Her articles span particle dynamics, fluidization, and educational strategies, reflecting her dual expertise in technical and pedagogical innovation. She has advised numerous students and contributed to national advisory boards, including co-chairing the National Academies Chemical Science Roundtable.

Fayssal Benkhaldounمشاهده پروفایل

استاد

• Finite Volume Methods
• Computational Fluid Dynamics
• Shallow Water Equations
• +۳ مورد دیگر

Fayssal Benkhaldoun is a Professor at Université Paris 13, affiliated with the LAGA laboratory (UMR7539). He has held leadership roles including former Head of the MCS team (Modeling and Scientific Computing) at LAGA and former President of the Scientific Council at IUT Villetaneuse. He is also the Project Leader of the International Office at IUT Villetaneuse. His research focuses on numerical methods for partial differential equations, particularly finite volume schemes for hyperbolic and elliptic problems. Key areas include shallow water equations, flow in porous media, mesh adaptation, and combustion front propagation. He has organized major conferences such as the International Symposium on Finite Volumes for Complex Applications (FVCA), initiating its first edition in 1996. Recent work emphasizes advanced numerical techniques like stabilized meshless methods, GPU acceleration, and parallel computing for CFD applications. His contributions span environmental modeling (flood simulation, sediment transport) and industrial applications (phosphate slurry rheology). Students advised include Jan Karel (2014, streamer propagation) and Saida Sari (2013, multilayer shallow water equations). He co-organized conferences since 1996 and has been an invited speaker at numerous institutions globally.

• Computational Materials Science
• Molecular Dynamics
• Machine Learning
• +۲ مورد دیگر

Dr. Priya Vashishta is a Professor with joint appointments in Computer Science, Materials Science, and Physics at the University of Southern California. His research integrates computational methods across multiple scales to address fundamental challenges in materials science and nanotechnology. Research focuses on multiscale simulation frameworks combining quantum molecular dynamics, machine learning interatomic potentials, and high-performance computing. Key areas include: energy storage materials, 2D materials characterization, catalytic processes, and AI-driven materials discovery. Publication trends reveal consistent development of computational methodologies: machine learning potentials for materials simulation, neural network quantum dynamics, GPU-accelerated computing, and multiscale modeling techniques. Recent work emphasizes practical applications in energy storage, nanoscale electronics, and advanced manufacturing. Research group activities include development of open-source simulation packages (PND, Allegro) and leadership in the MAGICS computational materials center. Contributions span fundamental theoretical frameworks to applied materials engineering.

Anuraag Shrivastavمشاهده پروفایل

استاد

• Cancer Biology
• Cellular Signaling Pathways
• Oncology
• +۴ مورد دیگر

Dr. Anuraag Shrivastav is a Professor at the University of Winnipeg's Department of Biology, affiliated with the Richardson College for the Environment and Science Complex. His research focuses on cellular signaling mechanisms linked to cancer development, particularly studying biomarkers and therapeutic targets for colorectal, breast, and prostate cancers. His lab also explores renewable energy materials, including perovskite solar cells and nanomaterials like zinc oxide buckyballs. Research Interests: Dr. Shrivastav investigates oncogenic pathways involving N-myristoyltransferase (NMT), mTOR signaling, and STAT3/AP1 interactions. His work bridges basic science and clinical applications, developing diagnostic tools such as blood-based biomarker tests. Simultaneously, he pioneers advancements in energy materials through computational modeling and experimental synthesis of novel photovoltaic systems. Publications Trends: Over 2023-2025, his articles emphasize translational cancer research—identifying prognostic markers and therapeutic strategies—while maintaining a parallel focus on materials innovation for sustainable energy solutions. This dual focus reflects his interdisciplinary approach to scientific inquiry. Awards: No specific awards mentioned, though his extensive publication record indicates sustained research impact. He currently supervises graduate students and postdocs in both cancer biology and materials science domains. Lab/Team: His laboratory integrates molecular biology, computational modeling, and materials engineering, fostering cross-disciplinary collaboration. Active projects include NMT-based cancer diagnostics and next-generation solar cell development.