جست‌وجوی استاد و پژوهشگر

Yifan Wangمشاهده پروفایل

استادیار

• Computational Fluid Dynamics
• Fluid-Structure Interaction
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Yifan Wang is an Assistant Professor in the Department of Mathematics and Statistics at Texas Tech University. His research focuses on computational fluid dynamics, fluid-structure interaction, artificial intelligence, machine learning, and numerical solutions of PDEs, with applications in biomedical engineering and high-performance computing. His research interests include developing numerical methods such as Galerkin finite elements, spectral element methods, discontinuous Galerkin methods, and smoothed particle hydrodynamics to solve complex fluid-structure interaction problems. Specific applications include cardiovascular modeling, blood flow analysis in stented arteries, microfluidic systems for cancer detection, and bioartificial organ design. Wang's publications demonstrate a strong focus on multiscale modeling in biomedical contexts, including computational studies of drug effects in carcinogenesis, stents, and tumor cell dynamics. Recent work explores machine learning applications in biological data regression, medical diagnostics, and predictive modeling of disease outbreaks. He contributes to cardiovascular engineering through computational analysis of stent geometries in tortuous arteries and hemodynamic modeling of stenotic vessels, with emerging work in geothermal energy systems and multiphysics modeling techniques.

Muhammad Mohebujjamanمشاهده پروفایل

استادیار

• Uncertainty Quantification
• Stochastic Partial Differential Equations
• Reduced Order Modeling
• +۳ مورد دیگر

Muhammad Mohebujjaman is an Assistant Professor at the University of Alabama at Birmingham (UAB) . His research focuses on Uncertainty Quantification of stochastic PDEs (including Navier-Stokes and Magnetohydrodynamics equations), Reduced Order Modeling , Population Dynamics , and Applied Analysis , with applications to multiphysics problems and fast algorithms. He teaches courses in Numerical Analysis , Scientific Computing , and Partial Differential Equations . Education : B.S. in Mathematics, University of Dhaka M.S. in Applied Mathematics, University of Dhaka M.S. and Ph.D. in Applied and Computational Mathematics, Clemson University He previously held postdoctoral positions at Virginia Tech (joint appointment in Mathematics and Biomedical Engineering & Mechanics) and MIT's Plasma Science and Fusion Center, where he contributed to data-driven reduced order modeling and Krylov solvers for fusion reactor simulations. His office hours are Monday/Wednesday 11:10 am–12:10 pm or by appointment.

Hodjat Shiriمشاهده پروفایل

دانشیار

• Offshore Engineering
• Subsea Pipelines
• Arctic Geotechnics
• +۲ مورد دیگر

Dr. Hodjat Shiri is an Associate Professor in Civil Engineering at Memorial University of Newfoundland, holding the Wood Group Chair in Arctic and Harsh Environments. His research addresses Arctic offshore challenges, including ice-seabed interactions, subsea pipeline design, and reliability assessment. Shiri integrates machine learning with geotechnical modeling to predict iceberg impacts and pipeline performance. Recent work explores trenching techniques, layered seabed responses, and fatigue in riser systems. Publications emphasize practical solutions for energy infrastructure in climate-sensitive regions, combining numerical simulations with field data validation.

Tim Tilfordمشاهده پروفایل

مدرس ارشد

• Computational Mechanics
• Thermal Management
• Power Electronics
• +۳ مورد دیگر

Dr. Tim Tilford is a Senior Lecturer in Computational Mechanics at the University of Greenwich, serving as Associate Head of School (Research & Knowledge Exchange) in the School of Computing and Mathematical Sciences. He holds a B.Eng. in Aeronautical Engineering from Queen Mary College, University of London (1998), an M.Sc. in Computational Fluid Dynamics from the University of Greenwich (1999), and a PhD in Eulerian-Lagrangian Multidomain methods for multiphysics simulations (2013). Education: B.Eng. Aeronautical Engineering, Queen Mary College, University of London M.Sc. Computational Fluid Dynamics, University of Greenwich PhD. Computational Mechanics, University of Greenwich Research Interests: Dr. Tilford’s work focuses on computational mechanics, thermal management of power electronics, topology optimization, and machine learning applications in engineering. His research integrates numerical methods like finite element analysis, coupled with experimental validation, to address challenges in heat sink design, power component reliability, and additive manufacturing. Research Projects: He has contributed to EPSRC-funded projects such as the Power Electronics Flagship and FAMOBS initiatives, as well as EU-funded efforts like NextFactory. His recent publications emphasize machine learning-driven failure prediction, thermal-mechanical analysis of power modules, and optimization of heat transfer systems. Advising & Grants: Dr. Tilford oversees research projects in collaboration with industry partners, focusing on reliability prediction, materials processing, and sustainable manufacturing. His work bridges computational modeling and practical engineering solutions. Labs & Teams: He leads interdisciplinary teams advancing thermal management and additive manufacturing technologies, with a focus on power electronics and microelectronics packaging reliability.

Murali Sundaramمشاهده پروفایل

استاد

• Additive Manufacturing
• Nano-Manufacturing
• Nontraditional Machining
• +۸ مورد دیگر

Dr. Murali Sundaram is a Professor in the Department of Mechanical and Materials Engineering at the University of Cincinnati's College of Engineering. With over 25 years of experience in precision manufacturing and postdoctoral research in micro/nano-manufacturing, his expertise spans Additive Manufacturing, Nano-Manufacturing, and Electrochemical Machining. He leads a vibrant research program supported by grants from the National Science Foundation (NSF), including a NSF CAREER Award, and collaborates with industry partners like Northrop Grumman. His work focuses on advanced machining techniques, surface modification, and functional material synthesis. Education: Industrial experience in precision manufacturing and postdoctoral research in micro/nano-manufacturing. Research interests include Electrochemical Deposition, Hybrid Machining, and Nontraditional Manufacturing methods such as EDM and USM. His team develops innovative solutions for challenges in aerospace, biomedical, and renewable energy sectors. Notable achievements include pioneering localized electrochemical additive manufacturing and mitigating thermal damage in advanced machining processes. Recent grants include studies on high entropy alloys (NSF $491,976) and multiphysics modeling of electroplating (DoN $212,500). He serves as Associate Editor of the Journal of Manufacturing Processes and is an active member of ASME and SME. Scientific Awards: 2015 NSF CAREER Award, 2016 CEAS Research Award, 2019 Excellence in Mentoring, and 2018 AIMTDR Best Paper Award. Teaching includes courses on Advanced Manufacturing, Nontraditional Processes, and Micro/Nano Manufacturing. His lab explores cutting-edge topics like cloud-based micro additive manufacturing and functionalized high-entropy alloys.

Ala Tabieiمشاهده پروفایل

دانشیار

• Impact dynamics
• Material modeling (isotropic/anisotropic)
• Extreme deformation analysis
• +۳ مورد دیگر

Ala Tabiei is an Associate Professor at the University of Cincinnati's Department of Mechanical Engineering within the College of Engineering and Applied Science. His research focuses on impact dynamics, material modeling under extreme conditions, computational mechanics, and finite element code development. He has held multiple roles as Principal Investigator (PI) in grants funded by federal agencies like the Department of Defense and industry partners. Education : PhD in Aerospace Engineering, University of Cincinnati (1994) MS in Applied Mathematics, University of Cincinnati (1992) MS in Aeronautical Engineering, Wichita State University (1988) BS in Mechanical Engineering, D. University (1985) Research Interests : Dr. Tabiei specializes in isotropic/anisotropic material modeling for extreme deformation, high-rate loading, and harsh environments. His work includes developing advanced constitutive models, computational simulations for crashworthiness, and experimental evaluation of material strength. His recent studies focus on composite laminates, delamination mechanisms, and cohesive zone modeling for dynamic fracture propagation. Grant Highlights : Department of Army: Mine/IED Blast Injury Modeling ($70,000) Department of Air Force: Hyper Velocity Impact Analysis ($30,000–$25,000) Federal Highway Administration: Guard Rail Safety Simulation ($56,546–$50,000) Delphi Automotive: Airbag Simulation Using Fluid-Structure Interaction ($50,000) Publications : Over 50 peer-reviewed articles in Journal of Composite Materials , International Journal of Computational Methods , and Engineering Fracture Mechanics . His work emphasizes computational modeling, material behavior under extreme conditions, and aerospace safety.

Zhen Liuمشاهده پروفایل

دانشیار مدعو

• Geotechnical Engineering
• Intelligent Infrastructure
• Multiphysics Modeling
• +۳ مورد دیگر

Zhen Liu is an Adjunct Associate Professor in the Department of Civil, Environmental, and Geospatial Engineering at Michigan Technological University. His research integrates multiphysics modeling, AI, and geotechnical engineering for intelligent infrastructure and system resilience. Teaching includes soil mechanics, foundation engineering, AI applications, and numerical simulations. Recent publications focus on machine learning in geosystems, pavement management, and computational methods.

Trevor M. Moellerمشاهده پروفایل

استاد

• Plasma Physics
• Electromagnetics
• Rocket Propulsion
• +۳ مورد دیگر

Trevor M. Moeller is the Jack D. Whitfield Professor and Associate Professor in the Mechanical, Aerospace, and Biomedical Engineering (MABE) Department at the University of Tennessee Space Institute (UTSI). He holds a Ph.D. (1998), M.S. (1993), and B.S. (1991) in Mechanical Engineering from the University of Tennessee and Rose-Hulman Institute of Technology, respectively. His research focuses on plasma physics, electromagnetics, and high-speed propulsion systems, with particular emphasis on electric propulsion, high-temperature plasmas, and advanced manufacturing for aerospace applications. Moeller has secured over $8M in grants, leading projects on electrospray thrusters, hypersonic flow environments, and space environment simulations. He has authored/co-authored over 70 peer-reviewed articles and one patent, and serves on committees for AIAA and IEEE. Education: B.S., Mechanical Engineering, Rose-Hulman Institute of Technology, 1991 M.S., Mechanical Engineering, University of Tennessee, 1993 Ph.D., Mechanical Engineering, University of Tennessee, 1998 Research Interests: High-temperature plasmas, rocket propulsion, electric propulsion devices, electromagnetic acceleration, and plasma diagnostics. Current projects include developing electrospray thrusters and mitigating cryodeposition in vacuum chambers. Publications reflect contributions to computational fluid dynamics, plasma modeling, and experimental propulsion studies. His lab, the Computational and Experimental Aerospace Research (CEAR) group, utilizes advanced facilities like wind tunnels and vacuum chambers, alongside the TEMPEST simulation software. Moeller advises numerous graduate students and collaborates with agencies like AFRL, AFOSR, and DOD. His work addresses challenges in space environment simulation and propulsion system optimization.

Andrew Osborneمشاهده پروفایل

استادیار

• Nuclear Engineering
• High Performance Computing
• Reactor Design
• +۶ مورد دیگر

Andrew Osborne is an Assistant Professor in the Department of Mechanical Engineering at the Colorado School of Mines. He holds a Ph.D. in Nuclear Physics from the University of Glasgow (2006) and has a unique interdisciplinary background, including a postdoctoral research period at the University of Texas at Austin (2009-2014) and a Research Assistant Professor appointment at the Colorado School of Mines before joining the faculty in 2018. Education: Ph.D. in Nuclear Physics (2006), University of Glasgow, Scotland. Osborne's research focuses on applying high performance computing and reactor theory to model coupled multiphysics phenomena in nuclear reactor systems. His work spans advanced reactor design, hybrid energy systems , numerical methods , and software engineering for scientific applications. Recent publications highlight his innovative integration of machine learning techniques with Monte Carlo simulations for nuclear reactor analysis, exploration of space nuclear power systems , and contributions to renewable energy integration and radiation detection technologies. Osborne's scholarly output demonstrates expertise in computational modeling of fast reactors , neutron transport , and fission wave dynamics . His interdisciplinary approach bridges nuclear engineering, data science, and energy systems analysis, with applications ranging from Earth-based sustainability to Martian colonization energy solutions. He actively contributes to nuclear safety and security through work on radiation source localization in urban environments and nuclear nonproliferation technologies. His software development background informs research on computational efficiency and robust simulation methods . Contact information: osbornea@mines.edu , Office: Brown Hall W370A, Phone: 303-384-2003

Xiangmin Jiaoمشاهده پروفایل

دانشیار

• High-performance computing
• Numerical methods
• Geometric computing
• +۵ مورد دیگر

Xiangmin (Jim) Jiao is an Associate Professor in the Department of Applied Mathematics and Statistics at Stony Brook University, affiliated with the Computer Science Department and the Institute for Advanced Computational Science. He holds a B.S. from Peking University, M.S. from UC Santa Barbara, and Ph.D. in Computer Science from UIUC. His research focuses on high-performance geometric and numerical computing, including algorithms and software for dynamic surfaces, mesh optimization, and multiphysics coupling in applications like computational fluid dynamics and biomedical engineering. He has received awards such as the Outstanding Teacher Award (2010-2012) and the David J. Kuck Outstanding Ph.D. Thesis Award (2001). His teaching spans courses like AMS 527 (Numerical Methods) and AMS 561 (Computational Science). Key research contributions include work on finite element methods, preconditioning techniques, and surface reconstruction. He has led projects on adaptive mesh refinement and multilevel linear solvers, with applications in climate modeling and structural mechanics. His lab, the Numerical Geometry Group (NumGeom), develops open-source software tools for numerical simulations. Collaborations include interdisciplinary work in biomedical computing and climate science. Recent research emphasizes robust numerical methods for singular systems and high-order surface integration techniques.

Raymond J. Spiteriمشاهده پروفایل

استاد

• Scientific Computing
• Numerical Analysis
• Applied Mathematics
• +۵ مورد دیگر

Raymond J. Spiteri is a Professor in the Department of Computer Science at the University of Saskatchewan, where he also directs the Centre for High-Performance Computing. His research spans numerical analysis, scientific computing, and applied mathematics, with a focus on developing numerical methods and software for differential equations, computational fluid dynamics, and exascale computing. He has led projects funded by Mprime and Mitacs, collaborating with industries like AFCC and Ballard Power. Education: Ph.D. in Mathematics (Institute of Applied Mathematics), University of British Columbia, 1997 B.Sc. (Hons.) in Applied Mathematics (Theoretical Physics), University of Western Ontario, 1990 Postdoctoral Fellow, School of Computer Science, McGill University, 1997–1999 Research Interests: Numerical methods for ODEs/DAEs/PDEs, including Runge-Kutta and SSP methods High-performance computing and parallel programming Applications in fluidized bed simulation, cardiac electrophysiology, and climate modeling Software development for scientific computing (e.g., problem-solving environments) Recent Article Trends: Advancements in implicit-explicit methods and operator-splitting techniques Computational modeling of cardiac arrhythmias and electrochemical systems Large-scale simulations for environmental and geophysical processes High-performance computing optimizations for exascale architectures Grants & Leadership: Director of the Centre for High-Performance Computing (College of Arts and Science) Executive Committee member of WestGrid/Compute Canada Past leader of the Mprime project on fuel cell simulation and transport phenomena Labs & Teams: Research is conducted in the Numerical Simulation Laboratory, focusing on interdisciplinary projects with industrial and academic partners.

Reza Baratiمشاهده پروفایل

استاد

• Enhanced Oil Recovery
• Hydraulic Fracturing
• Nanoparticle Applications
• +۲ مورد دیگر

Reza Barati is the Don W. Green Professor of Chemical and Petroleum Engineering and Director of the Tertiary Oil Recovery Program (TORP) at the University of Kansas. His research bridges industry and academia, focusing on enhanced oil recovery (EOR), hydraulic fracturing technologies, nanoparticle applications, and unconventional reservoir characterization. Research areas include fracture conductivity improvement using nanoparticles, waterless fracturing with CO2 foam, hydrocarbon gas huff-n-puff for shale recovery, and CO2 mobility control. His lab features advanced coreflooding systems, fracture conductivity measurement equipment, and HPHT analysis capabilities supported by major industry donations. He has secured significant research funding from NSERC, DOE, and industry partners. Teaching includes petroleum engineering design, well logging, unconventional resources, and water management courses. He developed KU's SPE PetroBowl team and organized international symposia on reservoir wettability.

Manika Prasadمشاهده پروفایل

استاد

• Geophysics
• Rock Physics
• CO2 Sequestration
• +۴ مورد دیگر

Dr. Manika Prasad Summary Dr. Manika Prasad is a Professor in the Geophysics Department at the Colorado School of Mines (Mines), serving as Director of the Mines CCUS Innovation Center and Director of the Center for Rock & Fluid Multiphysics. Her expertise spans multidisciplinary geosciences, with focus areas including rock and fluid properties, multiphysics phenomena, CO2 sequestration, and advanced materials characterization. She leads major initiatives like the DOE SMART program and has contributed to projects such as lunar regolith studies and groundwater accessibility in rural communities. Education: B.S. in Geology, St. Xavier’s College, Bombay, India (1978) M.S. and Ph.D. in Geophysics, University of Kiel, Germany (1983 and 1990) Research Interests: CO2 storage mechanisms and capacity assessment Rock physics of shales, clays, and sandstones Nano/microscale characterization via NMR, SEM, and CT imaging Geophysical exploration for sustainable resource management Machine learning applications in reservoir modeling Labs & Centers: Mines CCUS Innovation Center (CO2 storage solutions) Center for Rock & Fluid Multiphysics (interdisciplinary rock-fluid interactions) Bakken Research Consortium (shale reservoir studies) Recognition: 2022 Martin Luther King, Jr. Award for leadership in advancing equity and innovation in geoscience education and research. Advising & Grants: Dr. Prasad has secured federal grants (e.g., DOE) and collaborates with industry partners on CCUS and unconventional reservoir projects. Her work emphasizes ethical geoscience practices and global water access initiatives.

Steve Wiseمشاهده پروفایل

استاد

• Computational and Applied Mathematics
• Multi-phase Flows
• Moving Boundary Problems
• +۵ مورد دیگر

Steve Wise is a Professor of Mathematics and Associate Head for Research and Development at the University of Tennessee, Knoxville. He holds a PhD from the University of Virginia (2003) and has been at UT since 2007. His research focuses on numerical analysis, partial differential equations, and applications in soft matter physics and kinetic theory, with emphasis on developing efficient numerical methods for complex fluid systems and interface dynamics. He is co-author of Classical Numerical Analysis: A Comprehensive Course (Cambridge, 2023). Education: BS in Mathematics (Clarion University, 1996), MS in Mathematics (Virginia Tech, 1998), PhD in Engineering Physics (University of Virginia, 2003). Research interests include multi-phase flows, moving boundary problems, nonlinear PDEs, and mathematical biology. He has advised numerous PhD students and has been recognized as a Web of Science Highly Cited Researcher (2020, 2022). Teaching includes courses on PDEs, numerical analysis, and multigrid methods. His YouTube channel WiseAppliedMaths features lecture series on asymptotic analysis and numerical methods. Recent work includes collaborations on phase-field crystal models, kinetic theory, and energy-stable numerical schemes. Key Contributions: Textbook on numerical analysis, development of convex-splitting schemes, energy-stable finite element methods for phase-field systems. Labs/Teams: Active in UT’s Mathematics Department research groups, contributing to interdisciplinary initiatives in applied mathematics and computational science.

Athos Nathanailمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Artificial Intelligence
• Geology
• +۳ مورد دیگر

Athos Nathanail is a Research Fellow at the Colorado School of Mines, specializing in multidisciplinary research combining machine learning and artificial intelligence with geology, focusing on rock, sediment, and fluid properties. His work bridges computational methods with geological applications, including synthetic dataset creation and reservoir engineering. Education: BS from the University of Wyoming, followed by MS and PhD from Heriot-Watt University. His research emphasizes machine learning applications in geology, such as fossil image analysis and sedimentary structure detection, alongside computational creativity in scientific domains. His publications include peer-reviewed articles on synthetic datasets for geology and machine learning methodologies. He contributes to research centers like the Mines CCUS Innovation Center (MCIC) and the Center for Rock & Fluid Multiphysics (CRFM).