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Pedro DiezView profile

Professor

• Reduced Order Models
• Error Estimation and Adaptivity
• Extended Finite Element Methods (X-FEM)
• +4 more

Professor Pedro Diez is a faculty member at the Universitat Politècnica de Catalunya (UPC), specializing in computational mechanics and reduced-order modeling. He holds a BSc and PhD in Civil Engineering from UPC (1989 and 1996, respectively). His research focuses on error estimation, extended finite element methods (X-FEM), and applications in automotive and geophysical systems. He co-organizes the International Conference in Adaptive Modeling and Simulation since 2003, with the next edition in 2023. Key research areas include: Reduced Order Models (POD/PGD) Goal-oriented adaptivity in numerical simulations Geophysical inversion and thermal modeling Automotive structural dynamics and NVH analysis Sustainable materials for civil engineering His work bridges computational methods with real-world applications, such as earth dam monitoring, hydraulic fracturing modeling, and biomechanical simulations. Recent publications emphasize real-time risk analysis, parametric solutions for nonlinear systems, and inverse problem techniques in geophysics. His contributions to numerical methods have advanced high-fidelity, low-cost simulations across engineering disciplines. Publications span over 30 years, with a focus on: Efficient parametric solutions for transient coupled systems Nonintrusive reduced basis methods Thermo-hydro-mechanical modeling of geological media Advances in error estimation and model order reduction underpin much of his work, enabling applications in structural health monitoring, crashworthiness analysis, and geophysical exploration.

Sourabh ApteView profile

Professor

• Multiphase Turbulent Flows
• Numerical Simulations
• Predictive Numerical Techniques
• +4 more

Sourabh Apte is a Professor in the Department of Mechanical, Industrial, and Manufacturing Engineering at Oregon State University (OSU), part of the College of Engineering. He has been affiliated with OSU since 2005. His research focuses on computational fluid dynamics, particularly numerical simulations and modeling of multiphase turbulent flows, with applications in porous media, environmental flows, and spray systems. He leads the Computational Flow Physics Laboratory. Dr. Apte holds a Ph.D. in Mechanical and Nuclear Engineering from Pennsylvania State University (2000), an M.E. from the Indian Institute of Science (1996), and a B.E. from COEP, University of Pune (1994). He has held adjunct roles in OSU’s Water Resources Engineering and Water Resources Science Graduate Programs and served as a Visiting Professor at Aix-Marseille Université (2019). He was an ASEE Summer Faculty Fellow at Wright Patterson Air Force Base (2010–2011) and an ORISE researcher at NETL Albany (2006–2010). His research group develops advanced algorithms for multiphysics turbulent flow simulations using direct and large-eddy simulation techniques. Notable work includes studies on sediment transport and spray systems. He received the Engelbrecht Young Faculty Award from OSU and serves as an Associate Editor for the International Journal of Rotating Machinery.

Shun UchidaView profile

Associate Professor

• Geotechnical Numerical Analysis
• Energy Geotechnics
• Computational Geomechanics
• +5 more

Shun Uchida is an Associate Professor in the Department of Civil and Environmental Engineering at Rensselaer Polytechnic Institute (RPI), joining the Geotechnical Group in 2014. He specializes in multiphysics problems at the intersection of Energy Geotechnics , Computational Geomechanics , and Environmental Geotechnics . Education: BSc in Civil Engineering, Waseda University (2006) MPhil and PhD in Geotechnical Engineering, University of Cambridge (2008, 2013) Postdoctoral Research, Technion – Israel Institute of Technology His research focuses on coupled thermo-hydro-mechanical (THM) processes in geomaterials, particularly methane hydrate-bearing sediments , seismic-induced submarine landslides , and contaminant barrier systems . His work integrates advanced numerical modeling with experimental validation. Key trends in his publications include gas hydrate extraction optimization , sand migration dynamics , and AI-driven geotechnical analysis . Recent studies emphasize CO2 injection for methane recovery and environmental impacts of hydrate dissociation.

• Hypersonic Flows
• Computational Physics
• Rarefied Gas Dynamics
• +4 more

Dr. Ozgur Tumuklu is an Assistant Professor in the Department of Mechanical, Aerospace and Nuclear Engineering at Rensselaer Polytechnic Institute (RPI). His research focuses on computational physics, rarefied non-equilibrium flows, and stability analysis of hypersonic reacting flows. He holds a Ph.D. in Aerospace Engineering from the University of Illinois at Urbana-Champaign (2018), and prior roles include postdoctoral research with NASA's Artemis project at the Jet Propulsion Laboratory and software development at the University of Arizona. Education : Ph.D., Aerospace Engineering, University of Illinois at Urbana-Champaign (2018) M.S., Aerospace Engineering, Middle East Technical University (2013) B.S., Aerospace Engineering & Physics, Middle East Technical University (2010/2009) Research : Dr. Tumuklu's work integrates high-performance computing, open-source software development, and data-driven techniques to study hypersonic flows across continuum-to-rarefied regimes. Key areas include shock-boundary layer interactions, nonequilibrium aero-optics, and adaptive combustion control in ramjets. His lab (Havalab) develops tools like SU2-NEMO for multiphysics simulations. Publications : His recent work emphasizes hypersonic instability dynamics, magnetohydrodynamic effects, and hybrid particle-continuum modeling. Themes include predictive signature analysis, wake turbulence, and flow control strategies in extreme regimes. Advising & Grants : While specific grants are not listed, his research aligns with NASA-funded initiatives and hypersonic propulsion projects. He mentors students in advanced CFD and plasma-kinetic simulations. Labs/Teams : Director of Havalab, a research group advancing computational tools for hypersonic aerothermodynamics and rarefied flows.

Gregor KovačičView profile

Professor

• Mathematical Neuroscience
• Wave Dynamics
• Integrable Systems
• +4 more

Gregor Kovačič serves as Professor and Undergraduate Program Director in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute (RPI), holding a Ph.D. in Applied Mathematics from Caltech and prior postdoctoral experience at Los Alamos National Laboratory. His academic journey began with dual bachelor's degrees in Physics and Mathematics from the University of Ljubljana, Slovenia. His educational background includes: Ph.D. in Applied Mathematics, California Institute of Technology, 1990 Bachelor's degrees in Physics and Mathematics, University of Ljubljana, Slovenia Kovačič's research integrates mathematical theory with physical applications across neuroscience and wave phenomena. Primary interests include mathematical neuroscience for modeling neuronal networks, wave dynamics in dispersive systems, integrable partial differential equations, and nonlinear optics in metamaterials. Recent work examines light propagation through exotic refractive media and neural mechanisms underlying insect olfaction. Analysis of his 2020-2025 publications reveals dominant themes in quantum optics (Maxwell-Bloch systems), computational wave dynamics (high-order numerical schemes), and cortical neuroscience (pyramidal cell coupling). His work consistently bridges abstract mathematical frameworks with experimental physics and biological applications, particularly in nonlinear resonant systems and wave turbulence phenomena. Major recognitions include: Prešeren's Student Prize (Slovenia) Director's Funded Postdoctoral Fellowship (Los Alamos) NSF Career Award Sloan Research Fellowship As Undergraduate Program Director, Kovačič oversees curriculum development while maintaining active research leadership. His NSF Career Award and Sloan Fellowship indicate substantial grant funding, though specific projects aren't detailed. The absence of listed advisees suggests primary focus on undergraduate mentorship rather than graduate supervision. Kovačič collaborates through RPI's Center for Modeling, Optimization and Computational Analysis (MOCA), advancing interdisciplinary work in computational mathematics with applications spanning optical physics and neural modeling.

Katharine LongView profile

Professor

• Scientific Computing
• Mathematical Modeling
• Applied Mathematics
• +9 more

Katharine Long is a Professor in the Department of Mathematics and Statistics at Texas Tech University (TTU), where she has served since 2007. Her research focuses on scientific computing, mathematical modeling, numerical methods for partial differential equations, uncertainty quantification, and high-performance computing. She has held prior positions at Sandia National Laboratories, SUNY Brockport, and UMass Amherst. Her work spans interdisciplinary applications in physics, biology, engineering, and medicine, with recent emphases on inverse problems in medical imaging and implicit Runge-Kutta methods for PDEs. Education: PhD in Astrophysics from Princeton University (1990), BS in Astronomy from the University of Maryland (1986), and coursework at Prince George's Community College (1981-82). Research interests include numerical optimization, structural sensitivity analysis, astrophysical dynamics (e.g., barred galaxies), and ecological modeling. She collaborates extensively, including with Victoria Howle on computational methods for PDEs and interdisciplinary teams on biomedical applications of infrared thermography. Teaching spans graduate and undergraduate courses in numerical analysis, mathematical methods for physics, and computational mathematics. She has advised numerous PhD, MS, and undergraduate students, many of whom now hold academic or industry positions. Labs/Teams: Co-developer of the Sundance and Trilinos software frameworks for PDE-constrained optimization. Active in professional organizations such as SIAM and AWM.

Yifan WangView profile

Assistant Professor

• Computational Fluid Dynamics
• Fluid-Structure Interaction
• Artificial Intelligence
• +3 more

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 MohebujjamanView profile

Assistant Professor

• Uncertainty Quantification
• Stochastic Partial Differential Equations
• Reduced Order Modeling
• +3 more

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 ShiriView profile

Associate Professor

• Offshore Engineering
• Subsea Pipelines
• Arctic Geotechnics
• +2 more

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 TilfordView profile

Senior Lecturer

• Computational Mechanics
• Thermal Management
• Power Electronics
• +3 more

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 SundaramView profile

Professor

• Additive Manufacturing
• Nano-Manufacturing
• Nontraditional Machining
• +8 more

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 TabieiView profile

Associate Professor

• Impact dynamics
• Material modeling (isotropic/anisotropic)
• Extreme deformation analysis
• +3 more

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 LiuView profile

Adjunct Associate Professor

• Geotechnical Engineering
• Intelligent Infrastructure
• Multiphysics Modeling
• +3 more

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. MoellerView profile

Professor

• Plasma Physics
• Electromagnetics
• Rocket Propulsion
• +3 more

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 OsborneView profile

Assistant Professor

• Nuclear Engineering
• High Performance Computing
• Reactor Design
• +6 more

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