Find Academic Supervisors

Amanda DiegelView profile

Assistant Professor

• Numerical Analysis
• Scientific Computing
• Partial Differential Equations
• +3 more

Dr. Amanda Diegel is an Assistant Professor of Mathematics at Mississippi State University's Department of Mathematics and Statistics. Her research develops numerical methods for partial differential equations with applications in physics, biology, and materials science. Specializes in finite element methods, multigrid solvers, and stability analysis for complex systems including Cahn-Hilliard models, fluid-structure interactions, and liquid crystal dynamics. Published in leading computational mathematics journals on convergence analysis and numerical schemes for multiphysics problems. Holds PhD in Mathematics from University of Tennessee (2015) and completed postdoctoral research at Louisiana State University. Current work advances computational techniques for phase field models and coupled physical systems.

Ilya AvdeevView profile

Professor

• Real-Time Modeling
• Energy Storage Systems
• Design Thinking in Education
• +3 more

Ilya Avdeev is a Professor of Mechanical Engineering at the University of Wisconsin-Milwaukee (UWM) and holds multiple leadership roles including Director of the Lubar Entrepreneurship Center (LEC), Co-Founder/Executive Director of the UWM Student Startup Challenge, and Director of the Advanced Manufacturing and Design Laboratory. He is on sabbatical during the Spring 2025 semester. His research focuses on real-time modeling (Digital Twin), energy storage systems, and design thinking in engineering education. Education: PhD in Mechanical Engineering from the University of Pittsburgh (2003), MS and BS in Mechanical Engineering from St. Petersburg State Technical University, Russia (1999 and 1997). Research interests span advanced manufacturing, battery safety, and interdisciplinary education innovation. His work integrates computational modeling with practical applications in energy systems and biomedical devices. Over 20 years of academic and industry collaboration have resulted in impactful contributions to both technical and entrepreneurial ecosystems. Notable initiatives include the Milwaukee Regional Energy Education Initiative (as PI) and the UWM Student Startup Challenge, which fosters student entrepreneurship. His publications address topics like battery impact analysis, MEMS simulation, and educational pedagogy.

Nadine AubryView profile

Professor

• Fluid Dynamics
• Turbulence Modeling
• Microfluidics
• +5 more

Nadine Aubry is Professor and Senior Advisor to the Dean of Engineering at Tufts University School of Engineering, Department of Mechanical Engineering. An internationally recognized scholar, she previously served as Provost and Senior Vice President at Tufts (2019-2021), Dean of Engineering at Northeastern University (2012-2019), and Department Head at Carnegie Mellon University. Her leadership spans multiple institutions with global impact. Education: Ph.D. in Mechanical and Aerospace Engineering, Cornell University (1987) M.S. in Mechanical Engineering, Université Scientifique et Médicale de Grenoble B.S., Grenoble - Institut National Polytechnique Research Focus: Dr. Aubry pioneers computational approaches in fluid dynamics, specializing in turbulence modeling, microfluidics, and electrohydrodynamics. Her work integrates dynamical systems theory with nanoparticle manipulation and biofluid applications. Recent research emphasizes machine learning-enhanced methods for flow prediction, convective heat transfer optimization, and aerodynamic design using physics-informed neural networks. Publication Trends: Recent articles demonstrate strong emphasis on machine learning applications in fluid mechanics and thermal systems. Dominant themes include physics-informed neural networks for flow prediction, deep reinforcement learning for active flow control, convolutional networks for aerodynamic optimization, and hybrid AI methods for multiphysics problems across aerospace, electronics cooling, and biomedical domains. Awards & Honors: Elected Member: U.S. National Academy of Engineering Fellow: American Academy of Arts & Sciences, American Physical Society, ASME, AAAS, AIAA G.I. Taylor Medal for Fluid Mechanics Research National Academy of Inventors Fellow C.C. Mei Distinguished Lecturer Grants & Leadership: Secured USDA funding for aging research (2014-2019). Chaired International Union of Theoretical and Applied Mechanics assemblies globally. Serves on National Academy of Engineering prize committees and governance boards.

Ming HuView profile

Professor

• Thermal Transport
• Nanotechnology
• Energy Systems
• +8 more

Ming Hu is a Professor in the Department of Mechanical Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina. His research focuses on computational thermal science with applications in energy systems, nanotechnology, and advanced materials engineering. Education: Dr.-Ing. in Mechanics from the Institute of Mechanics, Chinese Academy of Sciences (2006) B.S. in Mechanical Engineering from the University of Science and Technology of China (2001) Dr. Hu's research centers on micro-/nano-scale thermal transport phenomena, particularly in low-dimensional materials and nanostructures. His work spans energy nanotechnology, interfacial heat transfer for thermal management, and multi-scale multiphysics modeling of complex energy systems. He employs advanced computational techniques including molecular dynamics, first-principles simulations, and machine learning to investigate phonon transport mechanisms and thermal properties of novel materials. Analysis of recent publications reveals a strategic shift toward artificial intelligence applications in thermal science. Dr. Hu has pioneered graph neural network architectures for high-throughput prediction of phonon properties and thermal conductivity, with significant focus on materials for wide bandgap semiconductor cooling, thermoelectrics, and battery systems. His current work emphasizes accelerating materials discovery through machine learning while maintaining physical interpretability. Scientific Awards: No major awards were documented in the provided information. Dr. Hu leads the Nano Energy and Engineering Laboratory, which specializes in computational thermal science research. The laboratory develops and applies advanced simulation methodologies to investigate heat transfer in next-generation materials. No information regarding graduate students or research grants was available in the provided materials.

Yue LingView profile

Associate Professor

• Multiphase Flows
• Atomization & Sprays
• Computational Fluid Dynamics
• +3 more

Yue (Stanley) Ling is an Associate Professor in Mechanical and Aerospace Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing. He directs the Computational Thermo-Fluids Laboratory, where his team develops advanced numerical methods for high-fidelity simulation of multiphase flows and heat transfer processes. Research focuses on atomization mechanisms, droplet/bubble dynamics with phase change, and fluid-surface interactions, using adaptive mesh refinement and massively parallel computing. Applications span fuel injection, thermal management, aerodynamics, and environmental processes. Dr. Ling's NSF CAREER Award (2020) supports fundamental research on spray formation mechanisms under turbulent conditions. Education: Ph.D. in Mechanical Engineering, University of Florida (2010) B.S. in Engineering Mechanics, Beihang University (2004) Recent publications develop conservative volume-of-fluid methods for compressible multiphase flows and characterize turbulence-interface interactions. Awards include ASME's Best Video Presentation (2021) and AIAA's Flow Visualization honor (2019). Dr. Ling leads multiple NSF-funded projects and collaborates internationally through the PARIS (Parallel, Robust, Interface Simulator) consortium.

Yi WangView profile

Professor

• Computational Fluid Dynamics
• Microfluidics
• Reduced Order Modeling
• +2 more

Dr. Yi Wang is Professor of Mechanical Engineering at University of South Carolina's College of Engineering and Computing. He directs the Integrated Multiphysics & Systems Engineering Laboratory (iMSEL) researching computational and data-enabled science for multiphysics systems including microfluidics, additive manufacturing, and autonomous systems. Research develops physics-based adaptive modeling, reduced order techniques, and data-driven surrogate models for complex engineering systems. Applications include microfluidic device optimization, aeroelastic reduced order models, robotic path planning, and structural health monitoring. Honors include Breakthrough Stars Award, Research Progress Award, and multiple Dean's Excellence Awards. He serves as Associate Editor for Discover Applied Sciences and editorial board member for multiple journals. Current NSF and DoD-funded projects focus on aeroservoelastic reduced order modeling and autonomous systems perception. Research group includes 5 postdoctoral fellows and 12 graduate students working on multidisciplinary computational projects. Teaching includes Flight Dynamics and Control, Engineering Analysis, and senior design courses.

Xingjian XueView profile

Associate Professor

• Solid Oxide Fuel Cells
• Electrochemical Energy Conversion
• Gas Separation Membranes
• +4 more

Dr. Xingjian Xue is an Associate Professor in Mechanical Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing. He holds a Ph.D. in Mechanical Engineering from the University of Connecticut with complementary training in mechatronics and materials science. His research advances electrochemical energy technologies, focusing on solid oxide fuel cells (SOFCs), gas separation membranes, and materials for energy conversion. Recent innovations include nanostructured cathodes, functionally graded membranes for oxygen separation, and thin-film SOFCs. His work integrates computational modeling with experimental validation to enhance performance and durability. Recent publications demonstrate progress in scaling membrane technologies for industrial oxygen separation and developing methane-fueled SOFC systems. Research consistently addresses fundamental transport phenomena and kinetic processes in electrochemical systems. Laboratory Leadership: Directs the High Temperature Electrochemical Materials and Energy Lab, focusing on materials synthesis, electrochemical characterization, and system prototyping.

Adam RobertsView profile

Research Fellow

• Offshore Renewable Energy
• Digital Engineering
• Operations & Maintenance
• +2 more

Adam Roberts is a Research Fellow in Operations & Maintenance at the University of Plymouth's School of Engineering, Computing and Mathematics within the Faculty of Science and Engineering. His expertise bridges renewable energy systems and digital engineering for offshore renewable applications. Research applies analytical, numerical, and practical methods across technology readiness levels. Specializes in developing simulation tools for floating offshore wind farms, including the Ec Simulator for the Cornwall FLOW Accelerator project. Focuses on packaging complex models into user-friendly applications for decision support. Prior to Plymouth, worked at University of Exeter's Offshore Renewable Energy Group and High Value Manufacturing Catapult. At Exeter, supported Marine-I project providing modeling expertise to marine technology companies. At MTC, conducted multiphysics analysis and developed simulation apps for commercial R&D. Research interests include operations optimization for floating offshore wind, digital twin development, and marine renewable energy technology advancement.

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.