Find Academic Supervisors

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.

• Computational Multiphysics
• Experimental Characterization of Soft Tissues
• Brain Mechanics
• +1 more

Johannes Weickenmeier is an Adjunct Research Assistant Professor in the Department of Mechanical Engineering at Stevens Institute of Technology's Charles V. Schaefer, Jr. School of Engineering and Science. He holds a PhD, MS, and BS in Mechanical Engineering from ETH Zurich. His research focuses on computational and experimental biomechanics, particularly in brain tissue characterization and neurodegenerative diseases. His research interests include: Computational Multiphysics Experimental tissue characterization Brain mechanics and aging Neurodegenerative disease modeling He has been recognized with the 2022 Stevens Employee Recognition Award for Student-Centricity. Dr. Weickenmeier serves as Director of the Center for Neuromechanics and participates in institutional committees, including graduate program oversight and computational research initiatives.

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.

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

Xiangmin JiaoView profile

Associate Professor

• High-performance computing
• Numerical methods
• Geometric computing
• +5 more

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. SpiteriView profile

Professor

• Scientific Computing
• Numerical Analysis
• Applied Mathematics
• +5 more

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 BaratiView profile

Professor

• Enhanced Oil Recovery
• Hydraulic Fracturing
• Nanoparticle Applications
• +2 more

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 PrasadView profile

Professor

• Geophysics
• Rock Physics
• CO2 Sequestration
• +4 more

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 WiseView profile

Professor

• Computational and Applied Mathematics
• Multi-phase Flows
• Moving Boundary Problems
• +5 more

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 NathanailView profile

Research Fellow

• Machine Learning
• Artificial Intelligence
• Geology
• +3 more

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).

Huang ChenView profile

Associate Professor

• Computational Materials Science
• Scientific Computing
• Density Functional Theory
• +3 more

Chen Huang is an Associate Professor in the Department of Scientific Computing at Florida State University (FSU), within the College of Arts and Sciences. His research focuses on computational materials science, particularly developing multiphysics methods for high-accuracy materials simulations and advancing orbital-free density functional theory (DFT) for large-scale studies of functional materials like metal alloys and lithium battery components. Huang holds a Ph.D. in Physics from Princeton University and a B.Sc. in Physics from Tsinghua University, China. Education Ph.D. in Physics, Princeton University B.Sc. in Physics, Tsinghua University Research Interests Development of novel multiphysics methods for oxide interface electronic structure predictions Orbital-free DFT advancements for scalable simulations of functional materials Electronic structure calculations and materials modeling Publications Trends Huang's recent work emphasizes improving DFT methodologies through embedded cluster approximations, analytical force calculations, and optimized effective potentials. His studies address challenges in simulating metals, catalytic reactions, and optoelectronic materials, with applications in energy storage and quantum chemistry. Key contributions include enhancing computational efficiency for large-scale systems and refining electron correlation treatments. Awards No specific scientific awards mentioned in the provided materials. Advising & Grants Current advisee: Mani Tyagi (Ph.D. student since 2023) Past students: Yu-Chieh Chi (UCSB Research Computing Consultant), Maliheh Shaban Tameh (ASU Postdoc) Former postdoc: Albert Dearden (2014-2015) Labs & Teams Huang leads the HUANG GROUP , focusing on computational materials science and DFT methodological advancements through the PROFESS software suite. The group collaborates on embedded cluster density approximations and high-accuracy electronic structure simulations.