Find Academic Supervisors

• Petroleum Refining
• Process Modeling
• Catalytic Cracking
• +5 more

Yakubu John is an Associate Professor in Chemical and Petroleum Engineering at the University of Bradford's School of Engineering, where he serves as Programme Leader for Chemical Engineering programmes. His research focuses on process modeling, optimization, and control of petroleum refining and chemical processes. John specializes in fluid catalytic cracking (FCC), catalytic reforming, crude-to-chemicals conversion, carbon capture, and biomass-to-fuel technologies. His work integrates computational modeling with process optimization to enhance efficiency in refinery operations and sustainable energy systems. Recent publications reflect John's expertise in FCC reactor modeling, hybrid desalination systems, energy optimization in industrial furnaces, and reliability analysis of complex systems. His research consistently bridges fundamental process engineering with industrial applications.

Burt S. TilleyView profile

Professor

• Mathematical Modeling
• Fluid Mechanics
• Heat Transfer
• +3 more

Burt S. Tilley is a Professor of Mathematical Sciences at Worcester Polytechnic Institute (WPI), with affiliations in Mechanical Engineering and the Center for Industrial Mathematics and Statistics (CIMS). He holds a BS, BA from University of Massachusetts Lowell (1988), and a PhD from Northwestern University (1994), followed by a postdoc at Laboratoire d'Hydrodynamique, École Polytechnique (1995-1996). His research focuses on mathematical modeling of scientific and engineering problems, particularly in fluid mechanics, heat transfer, and free boundary problems. Key areas include interfacial pattern formation in fluid films, geothermal energy applications, and computational approaches to nonlinear PDEs. He emphasizes multidisciplinary collaboration through initiatives like the Mathematical Problems in Industry (MPI) workshops and WPI's REU program. Tilley's scholarly work spans fluid dynamics, stability analysis, and industrial applications. His teaching integrates mathematical rigor with real-world relevance, reflecting his belief in mathematics' practical value. He has advised numerous MQP projects and collaborates with industry partners to solve complex technical challenges. He is affiliated with WPI's CIMS, which bridges academic research with industrial needs. His contributions have been recognized through promotions and tenure awards, including those announced in 2021 and 2013.

• Mathematical Biology
• Data-Driven Modeling
• Multiscale Numerical Methods
• +3 more

Thomas G. Fai is an Associate Professor in the Mathematics Department at Brandeis University. His research focuses on mathematical biology, data-driven modeling, and multiscale numerical methods, with applications to biological systems such as intracellular transport, fluid-structure interactions, and nuclear size scaling. He develops computational frameworks like the Lubricated Immersed Boundary Method to simulate complex biological phenomena. Key research areas include modeling red blood cell mechanics using cryo-EM data, studying nuclear size homeostasis through osmotic forces, and exploring prebiotic vesicle dynamics. His work bridges applied mathematics and experimental biology, emphasizing numerical methods for systems where hand calculations are intractable. Publications highlight contributions to fluid dynamics modeling, stochastic systems in cellular processes, and computational tools for biomedical applications. Fai is active in mentoring and teaching, employing inquiry-based learning approaches in classrooms.

Timothy ReisView profile

Associate Professor

• Computational Fluid Dynamics
• Lattice Boltzmann Method
• Applied Mathematics
• +4 more

Dr. Timothy Reis is an Associate Professor in Mathematics at the School of Computing and Mathematical Sciences, University of Greenwich. He joined the university in 2016, previously serving as a Lecturer in Applied Mathematics at Plymouth University (2013-2016) and holding postdoctoral positions at UCL, University of Oxford, and Cardiff University. Education: BSc in Mathematics (University of Wales Aberystwyth, 2002), MSc with Distinction in Mathematical Theory of Complex Fluids (University of Wales Aberystwyth, 2003), PhD in Mathematics (Cardiff University, 2007). Research Focus: Lattice Boltzmann method (LBM), computational fluid dynamics, rheology, multiphase flows, and microfluidics. Professional Affiliations: Fellow of the Institute of Mathematics and its Applications (IMA), member of UK Consortium on Mesoscopic Engineering Science (UKCOMES), British Society of Rheology (BSR), and UK Carbon Capture and Storage Research Centre (UKCCSRC). His work contributes to industrial applications like additive manufacturing through LBM modeling. He has supervised multiple PhD students and participated in outreach activities such as the IMA Festival of Mathematics and Channel 4's Food Unwrapped. Honors: Vernon Harrison Prize (2007), UPSU SSTAR Award (2016). He serves as an Associate Editor for the International Journal of Computer Mathematics and has conducted workshops on LBM boundary conditions at institutions like Tsinghua University.

Ying SunView profile

Professor

• Multiphase Flows
• Machine Learning
• Thermal Management
• +5 more

Dr. Ying Sun is the Herman Schneider Professor and Director of Research and Strategic Initiatives at the University of Cincinnati's College of Engineering and Applied Science (CEAS). She previously served as Head of the Mechanical & Materials Engineering Department, leading initiatives such as the Industrial & Systems Engineering programs and faculty recruitment. Her research focuses on multiphase flows, machine learning, additive manufacturing, and thermal systems. She holds awards including the NSF CAREER Award and is a Fellow of APS and ASME. Education: Ph.D., Mechanical Engineering, University of Iowa, 2006 M.S., Mechanical Engineering, University of Iowa, 2001 B.Eng., Thermal Engineering, Tsinghua University, 1998 Research Interests: Multiphase transport phenomena, machine learning applications in thermal systems, additive manufacturing, and sustainable energy solutions. Her work bridges fundamental science and engineering applications, such as decarbonization and thermal management. She leads the Complex Fluids & Multiphase Transport Laboratory and co-chairs the Gordon Conference on Micro and Nanoscale Phase Change Phenomena. Grants & Funding: Over $8M in grants from NSF, NASA, and others, including projects on 3D-printed titanium radiators, acoustic signatures in boiling, and decarbonization REU programs. She also leads the NSF-funded REALIZE-2050 REU Site. Awards: NSF CAREER Award AFOSR Summer Faculty Fellowship CNRS Visiting Professorship ASME/APS Fellowships Labs/Teams: Directs the Complex Fluids & Multiphase Transport Lab, focusing on interfacial phenomena, energy systems, and data science. Collaborates internationally with institutions like Princeton University and Tsinghua University.

H. DumasView profile

Professor

• Dynamical Systems
• Applied Mathematics
• Ergodic Theory
• +6 more

H. Dumas is a Professor in the Department of Mathematical Sciences at the University of Cincinnati, with academic affiliations spanning over four decades. His research focuses on dynamical systems, applied mathematics, and their applications in physics, particularly in particle accelerators, celestial mechanics, and condensed matter physics. He has held visiting positions at institutions worldwide, including universities in France, Germany, Brazil, and China. Dumas is renowned for his work on averaging methods, KAM theory, and Hamiltonian systems, with over 60 peer-reviewed articles and multiple authored/translated books. Education & Academic Career : Awarded the Tom L. Popejoy Dissertation Prize (1991) for his PhD thesis. He held postdoctoral roles at the University of Minnesota (1989–1990) and received NSF grants (1992–2000). His academic leadership includes organizing international conferences, such as the 1992 Hamiltonian Dynamical Systems conference at UC. Research Contributions : His work bridges pure and applied mathematics, emphasizing long-term stability in Hamiltonian systems. Notable contributions include the 'KAM Story' book (2014), a foundational text on KAM theory, and pioneering studies on particle channeling in crystals and spin-orbit interactions in accelerators. Awards & Honors : Recipient of the Mrs. A.B. (Dolly) Cohen Award (2008) and the Edith C. Alexander Award (2009) for teaching excellence. His research has been supported by grants from the NSF, Ohio Supercomputer Center, and UC.

Shaaban AbdallahView profile

Professor

• Fluid Mechanics
• Computational Fluid Dynamics
• Turbomachinery Flow
• +5 more

Shaaban Abdallah is a Professor in the Department of Aerospace Engineering & Engineering Mechanics at the University of Cincinnati. His research focuses on fluid mechanics, computational fluid dynamics, and turbomachinery flow analysis. He has led or collaborated on federal and industry-funded projects, including studies on hybrid propulsion systems and wind turbine optimization. Notable grants include the $27.5M Ohio Department of Development project on propulsion systems (2008–2013) and multiple General Electric Aviation collaborations. His work spans aerodynamic design, turbulence modeling, and fluid-structure interaction, with over 50 peer-reviewed publications. Recent research includes advancements in Savonius wind turbine efficiency, supersonic flow simulation, and UAV propulsion systems. Education details are not explicitly stated, but his career spans over four decades, with contributions to both academia and industry. He has advised on projects involving heart valve fluid dynamics, morphing UAVs, and micro-pump design. Key collaborations include partnerships with the Department of the Air Force and General Electric. His lab work includes developing novel compressor designs and exploring hybrid propulsion technologies. Research interests are anchored in solving complex fluid dynamics problems with applications in aerospace and energy sectors. His recent publications (2019–2025) highlight innovations in numerical methods for incompressible flows, wind energy systems, and supersonic turbulence modeling.

Erwin GeorgeView profile

Senior Lecturer

• Computational Applied Mathematics
• Fluid Dynamics
• Numerical Analysis
• +3 more

Dr. Erwin George is a Senior Lecturer in the School of Computing and Mathematical Sciences at the University of Greenwich. He holds a PhD in Computational Applied Mathematics from Stony Brook University (2003) and has held academic positions at SUNY Stony Brook, the University of Warwick, and the University of Edinburgh. His research focuses on fluid dynamics, numerical analysis, and mathematical biology, with notable contributions to turbulent mixing, inverse problems in biomedical imaging, and computational methods for complex systems. He is a Fellow of the Institute of Mathematics and its Applications (FIMA) and serves on the Mathematics Today Editorial Board. Education includes: B.A. Honours in Mathematics, University of Guelph (Canada) M.A. in Mathematics, Pennsylvania State University (USA) PhD in Computational Applied Mathematics, SUNY Stony Brook (USA) Research interests span fluid dynamics phenomena, mathematical modeling of biological systems, and numerical methods for solving transport and reaction equations. His work bridges theoretical analysis and practical applications in engineering and life sciences. Recent publications emphasize stochastic processes in structural dynamics and biomedical modeling. Awards include Fellowship of the IMA, reflecting his contributions to advancing mathematical sciences. Teaching expertise spans pure/applied mathematics, scientific computing, and statistics at undergraduate and postgraduate levels.

• Fluid Dynamics
• Computational Fluid Dynamics
• Chemical Engineering
• +3 more

Orest Shardt is an Associate Professor in the Department of Chemical Sciences at the University of Limerick. His research focuses on computational fluid dynamics, numerical methods for engineering problems, and colloidal science. He specializes in developing advanced numerical techniques for solving complex fluid flow and particle dynamics challenges. His work addresses areas such as electric double layer effects on suspension viscosity, particle-wall collision mechanics, and rennet-induced coagulation processes in food systems. Shardt's methods include lattice-Boltzmann approaches, meshfree collocation schemes, and finite volume techniques. His recent publications explore topics ranging from non-Newtonian fluid behavior to population balance modeling in chemical engineering processes. While no specific awards are listed, his contributions reflect impactful work in computational methodologies for multiphase flow systems.

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.

İlyas Bedii ÖzdemirView profile

Professor

• Computational Fluid Dynamics
• Turbulent Flow
• Combustion Engineering
• +4 more

İlyas Bedii Özdemir is a Professor in the Department of Mechanical Engineering at Istanbul Technical University with an h-index of 8. His research spans computational fluid dynamics, turbulent flow, combustion engineering, and biomedical fluid mechanics. Current projects include electric aircraft propeller design and hydrodynamic stability analysis. His research interests focus on Computational Fluid Dynamics for complex systems including aneurysm hemodynamics, bubble dynamics in boiling, and vortex phenomena in combustion. Key application areas include biomedical diagnostics, heat transfer optimization, and multiphase flow modeling. Recent work integrates machine learning with CFD for predictive healthcare applications. Analysis of his 37 research outputs (1992-2025) reveals dominant trends in Blood flow modeling for medical diagnostics (2022-2025) Boiling heat transfer and bubble dynamics (2022-2023) Combustion instability in burners (2015-2017) His work consistently bridges fundamental fluid mechanics with practical engineering applications. Scientific awards include: 11. TÜRKİYE TEKNOLOJİ ÖDÜLÜ (2014) Three AvH Fellowships (1998, 2002, 2005) Three Overseas Research Student Awards (1989-1991) Supervised 36 research works including four major projects on turbulent flow analysis (2022-2023), electric aircraft propellers (2021-2022), spiral jet simulations (2018-2021), and combustion instability (2015-2017). His team develops advanced CFD models for biomedical and industrial applications with strong international collaboration networks. Current research focuses on neural network predictive models for patient-specific aneurysm risk assessment and next-generation propeller design for electric aircraft, leveraging his expertise in multiphase flow and vortex dynamics.

James ChenView profile

Associate Professor

• Multiscale Simulation
• Turbulence
• Triboelectricity
• +7 more

James Chen is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University at Buffalo, State University of New York. His research focuses on fundamental theories and numerical simulations of multiscale physical phenomena, including turbulence, triboelectricity, and atomistic-to-continuum mechanics. He leads the Multiscale Computational Physics Lab and maintains active collaborations in computational mechanics and energy materials. Education: PhD, Mechanical and Aerospace Engineering, The George Washington University (2011) MS, Applied Mechanics, National Taiwan University (2007) BS, Mechanical Engineering, National Chung-Hsing University (2005) Research interests span multiscale modeling, high-speed flow dynamics, and energy materials. Key areas include turbulence physics, fracture mechanics of ceramics, and triboelectric energy harvesting. His work bridges atomistic simulations with continuum models to explain phenomena across scales. Recent publications (2022–2024) emphasize uncertainty quantification in ceramic materials, turbulent boundary layer scaling with mass transfer, and hybrid rocket flow simulations. This reflects a focus on both foundational fluid mechanics and applied materials science challenges. Notable honors include the U.S. Air Force Young Investigator Award (2017) and Steve Hsu Research Scholar (2017–2020). His lab develops advanced numerical tools for complex multiscale systems, with applications in aerospace thermal protection and energy conversion technologies.

James GlimmView profile

Professor

• Mathematical Physics
• Fluid Dynamics
• Computational Science
• +5 more

James Glimm is a Distinguished Professor and Chair of the Department of Applied Mathematics and Statistics at Stony Brook University. He holds a PhD in Mathematics from Columbia University (1959) and has held positions at MIT, the Rockefeller University, and the Courant Institute. His research spans mathematical physics, fluid dynamics, computational science, and turbulence modeling. Glimm is renowned for contributions to nonlinear analysis (earning the AMS Steele Prize), quantum field theory (Heineman Prize), and computational fluid dynamics (DOE adoption of his front-tracking method). A member of the National Academy of Sciences and recipient of the National Medal of Science, he has also served as AMS President (2007–2009). His work integrates theoretical analysis with high-performance computing, addressing challenges in multiphase flows, shock dynamics, and turbulent mixing. Key collaborations include projects with Brookhaven National Lab, ARO, and DOE, focusing on multiscale physics and inertial confinement fusion. Glimm’s methodologies, such as front-tracking algorithms, are foundational in simulating complex fluid interfaces and instabilities. His research also extends to biomedical applications, including cardiac electrophysiology simulations. Awarded over 20 grants, his funding includes DOE support for multiphase flow simulations in nuclear reactors and hypersonic systems. Mentoring over 30 PhD students, Glimm has shaped interdisciplinary research in applied mathematics, with notable contributions to both academic and industrial challenges.

• Multiphase Turbulent Mixing
• Computational Fluid Dynamics
• Numerical Methods
• +3 more

Hyunkyung Lim is a Research Assistant Professor at Stony Brook University's Department of Computational Applied Mathematics. Her research focuses on multiphase turbulent mixing of compressible and incompressible fluids, numerical methods for fluid interface tracking, and computational fluid dynamics analysis. She holds a Ph.D. from 2009. Expertise: Turbulent mixing simulations, front-tracking methods, quantum computing algorithms, and biomedical modeling Key Projects: Cybercardia cardiac device modeling, inertial confinement fusion studies, and quantum amplitude estimation Her work bridges computational mathematics with applications in energy systems (fusion research), medical devices, and quantum computing. Recent studies include quantum algorithm implementations on NISQ devices and MRI-based finite element modeling. Publications highlight contributions to both classical fluid dynamics and emerging quantum computing fields. No specific grants or awards are listed in the provided materials.

Alice KonigesView profile

Researcher

• High Performance Computing
• Computational Science
• Parallel Algorithms
• +5 more

Dr. Alice Koniges is a computational scientist at the University of Hawaii at Manoa and Maui High Performance Computing Center. She specializes in modeling complex physical phenomena using advanced HPC techniques, with research interests spanning emerging architectures, machine learning, and partial differential equations. Her career includes roles at Berkeley Lab and joint appointments with UC Berkeley and UCLA. She leads the ALE-AMR code development team, which models plasma, fluid, and solid interactions in high-energy density regimes. As a DOE Exascale project PI, she achieved milestones in supercomputing performance and secured multimillion-dollar contracts. Previously, she served as Head of Institutional Computing at Livermore Lab, fostering industry partnerships through CRADAs. Education: PhD in Applied and Computational Mathematics from Princeton University (first woman recipient), MSE and MA from Princeton, and BA from UC San Diego. Over 100 technical papers, two HPC books, and 1,500+ citations highlight her scholarly impact. She has mentored >15 postdocs and graduate students, emphasizing HPC's practical applications. Recent work includes coastal aquifer management models and epidemiological studies of pandemic spread in island environments. Her research bridges computational methods with real-world challenges, such as fusion energy simulations and climate modeling. The PISALE codebase and ALE-AMR framework are core to her contributions. Current focus areas include exascale computing readiness, multiphysics integration, and optimizing HPC for next-gen architectures.