Find Academic Supervisors

Sean P. CarneyView profile

Assistant Professor

• Applied Mathematics
• Computational Mathematics
• Numerical Analysis
• +6 more

Sean P. Carney is an Assistant Professor of Mathematics at Union College , focusing on applied and computational mathematics. His roles include teaching courses in differential equations, partial differential equations, and numerical methods, while leading research in multiscale and stochastic modeling, PDE-constrained optimization, and turbulent flow dynamics. Education: Ph.D. in Mathematics from the University of Texas at Austin (2020), advised by Björn Engquist; B.S. in Mathematics from the University of Michigan, Ann Arbor (2014). Prior Experience: Postdoctoral Research Fellow at George Mason University’s Center for Mathematics and Artificial Intelligence; Hedrick Assistant Adjunct Professor at UCLA (2020–2023); Graduate Research Assistant at Lawrence Berkeley National Lab (2018–2019). Carney’s research centers on multiscale and stochastic modeling , with applications in transport and mixing in turbulent flows , homogenization theory , and optimization under uncertainty . His work combines numerical analysis with physical modeling to solve complex problems in PDE-constrained optimization and fluid dynamics. Recent publications highlight his contributions to numerical homogenization , chaotic electroconvective flow modeling , and liquid-liquid phase separation in confined systems . These studies span fluid dynamics, computational physics, and applied mathematics, often involving collaborations with institutions like UCLA, UT Austin, and Lawrence Berkeley National Lab. Scientific Awards: Editors' Suggestion, Physical Review Fluids (2020) Carney has mentored students in the UCLA Computational and Applied Mathematics REU program and the Cal-State/UCLA Summer Bridge Program. His teaching spans differential equations, numerical methods, and advanced PDE courses at Union College and UCLA.

Spencer BryngelsonView profile

Assistant Professor

• Computational Physics
• Fluid Dynamics
• High Performance Computing
• +3 more

Spencer Bryngelson is an Assistant Professor in the School of Computational Science and Engineering at Georgia Institute of Technology's College of Computing. He specializes in computational physics, numerical methods, fluid dynamics, and high-performance computing. His research focuses on developing scalable simulation tools for multiphase flows, quantum algorithms for fluid dynamics, and Bayesian optimization techniques. Education: Ph.D. and M.S. in Theoretical and Applied Mechanics from University of Illinois at Urbana-Champaign (2015–2017); B.S. in Mechanical Engineering and Mathematics from University of Michigan–Dearborn (2013) Affiliations: Previously senior postdoc at Caltech, visiting researcher at MIT, and postdoc at XPACC Labs/Teams: Leads the Comp-Physics Group (https://comp-physics.group) His research interests include quantum computing applications to fluid mechanics, high-performance computing methodologies, and multiphysics simulation frameworks like the MFC flow solver. Recent work explores hybrid quantum-classical algorithms for PDEs and GPU-accelerated combustion kinetics modeling through tools like Pyrometheus. Notable trends in his articles include: Quantum computing integration in fluid dynamics simulations Development of exascale-ready multiphysics solvers Bayesian optimization for material characterization Advances in WENO schemes and turbulence modeling His work emphasizes portability and scalability in HPC environments, with contributions to libraries like QBMMlib and RoseNNa for neural network integration in CFD.

Saeed BidiView profile

Researcher

• Data Engineering
• Machine Learning
• Deep Neural Networks
• +3 more

Dr. Saeed Bidi is a Senior Researcher at the Wolfson Institute of Population Health, Queen Mary University of London. He holds a PhD in STEM and specializes in developing data-driven systems for healthcare and engineering applications, particularly within the Genes and Health initiative. His expertise spans Data Engineering, Machine Learning, Deep Neural Networks, and Natural Language Processing. He integrates computational methods with quantitative research to address complex problems in population health and engineering systems. Research Interests: Machine Learning Applications in Healthcare and Infrastructure Data Pipeline Development Time Series Analysis Computational Fluid Dynamics Genetic Epidemiology Recent work focuses on predicting bridge deck temperatures using machine learning, numerical modeling of bubble collapse phenomena, and genetic studies in South Asian populations. Publications highlight interdisciplinary approaches to thermal analysis, ultrasound physics, and public health genetics. No scientific awards or grants are explicitly mentioned in the provided texts.

George FacasView profile

Professor

• Heat Transfer
• Fluid Mechanics
• Thermodynamics
• +4 more

George Facas is a Professor of Mechanical Engineering at The College of New Jersey's Department of Mechanical Engineering. He holds a Ph.D. (1985) and M.S. (1983) in Mechanical Engineering from Drexel University, and a B.S.M.E. (1981) from Villanova University. His academic career at TCNJ spans over three decades, including leadership roles as Department Chairman (1995-2000) and Dean of Engineering (2000-2007). His research focuses on thermal-fluid sciences, with expertise in: Heat transfer mechanisms (convection, conduction, radiation) Fluid dynamics in porous media and confined spaces Thermodynamic system optimization HVAC and energy efficiency applications Computational fluid dynamics (CFD) modeling He maintains active research in engineering education pedagogy, particularly CFD integration in curricula. His publications demonstrate consistent focus on experimental and numerical analysis of thermal systems, with recent work exploring educational applications of simulation tools. Earlier research emphasized industrial and environmental applications of heat transfer in complex geometries. Scientific Awards: Villanova University Professional Achievement Award (2007) No information is available regarding student advising, research grants, or laboratory leadership in the provided text.

Travis W WalkerView profile

Associate Professor

• Transport Phenomena
• Complex Fluids
• Soft Solids
• +4 more

Travis W Walker serves as Associate Professor in the Karen M. Swindler Department of Chemical and Biological Engineering at South Dakota School of Mines and Technology, having returned to his alma mater in 2017 after four years as Assistant Professor at Oregon State University. His academic credentials include: B.S. in Chemical Engineering, South Dakota School of Mines & Technology B.S. in Applied and Computational Mathematics, South Dakota School of Mines & Technology M.S. in Chemical Engineering, Stanford University Ph.D. in Chemical Engineering, Stanford University Dr. Walker's research centers on transport phenomena with specialized focus on theoretical and experimental analysis of complex fluids , soft solids , and biological systems . His work investigates multiphase interactions and material mechanics through molecular-level physics to explain macroscopic behaviors, covering critical areas like rheology, capillarity, and low-Reynolds number flow. This fundamental approach bridges engineering principles with biological applications. He actively teaches core chemical engineering curriculum spanning programming, numerical methods, mass/energy balances, and advanced transport phenomena, while championing undergraduate research initiatives and developing specialized courses in complex fluid dynamics and applied mathematics.

Blaise BourdinView profile

Professor

• Solid Mechanics
• Applied Mathematics
• Numerical Analysis
• +4 more

Blaise Bourdin is a Professor of Mathematics and the Canada Research Chair in Mathematical and Computational Aspects of Solid Mechanics (Tier 1) at McMaster University's Department of Mathematics & Statistics. He holds a Doctorate from Université Paris 13 (1998) and has held academic positions at Louisiana State University (2002-2021) and postdoctoral roles at Technical University of Denmark, Caltech, and NYU. His research focuses on interdisciplinary modeling, numerical simulation, and analysis in solid mechanics, with emphasis on defect mechanics and optimal design. He pioneered the phase-field approach to brittle fracture, a widely influential method in fracture mechanics. His work has been supported by over $6M in grants from NSF, NSERC, and industry. Key research areas include solid mechanics, applied mathematics, numerical analysis, and theoretical mechanics. Recent publications (2018-2025) explore fracture toughness measurement, phase-field discretization, hydraulic fracturing, and topology optimization. Education: Doctorate in Mathematics, Université Paris 13 (1995-1998) Advanced Studies Diploma (DEA), Université Paris 13 (1994-1995) Master's and Undergraduate degrees, Université Paris 13 (1990-1993) Teaching includes advanced courses like Fourier Analysis, Optimal Design, and Engineering Mathematics. His software contributions include the open-source 'mef90/vDef' project. Awards include the prestigious Tier 1 Canada Research Chair (2022).

Sami AmmarView profile

Lecturer

• Fluid mechanics
• Aircraft performance analysis
• Aerodynamics design
• +5 more

Sami Ammar is a Lecturer in the Department of Mechanical Engineering at Polytechnique Montréal. He holds a Doctorate in Engineering (D. Ing) from ESTACA in France, along with a Master of Applied Science (M.Sc.A.) and a Ph.D. from Polytechnique Montréal. His primary academic affiliation is with the Department of Mechanical Engineering where he teaches courses related to aircraft performance, vibrations, heat transfer, and structural mechanics. Dr. Ammar's educational background includes: D. Ing (Doctorate in Engineering) from ESTACA, France M.Sc.A. (Master of Applied Science) from Polytechnique Montréal Ph.D. from Polytechnique Montréal His research focuses on fluid mechanics, aircraft performance analysis, aerodynamics design, and heat transfer. Dr. Ammar's work spans both theoretical computational fluid dynamics using Lattice Boltzmann methods and practical applications in aerospace engineering and energy systems. His primary sphere of excellence is in Energy, Water and Resources, with secondary expertise in Sustainable Transport and Infrastructures, and Modeling and Artificial Intelligence. His research has significant applications in areas such as rarefied gas flows in porous media, droplet impact and phase change phenomena, wind turbine performance analysis, and innovative aircraft design including Blended Wing Body concepts. Dr. Ammar has received recognition for his teaching excellence, including the Excellence in Teaching Award from Polytechnique Montréal in 2021. His educational publications reveal a strong interest in pedagogical innovation, particularly in developing tools and services to address student learning difficulties and improve engagement in practical work settings. He has also contributed insights on public transportation systems, as evidenced by his commentary in the Journal de Montréal regarding the Réseau express métropolitain (REM) and its challenges with winter conditions. His scientific contributions have been recognized through his publications in reputable journals such as Fluids, International Journal of Modern Physics C, Communications in Computational Physics, and Aerospace Science and Technology. Notable awards include: Excellence in Teaching Award - Polytechnique Montreal (2021) Dr. Ammar has been actively involved in advising and mentoring students, as evidenced by his thesis supervision. His research has attracted attention from media outlets, demonstrating the practical relevance of his work to contemporary engineering challenges. While specific grant information isn't detailed in the provided text, his diverse publication record spanning computational fluid dynamics, aerospace engineering, and educational innovation suggests successful research funding. Dr. Ammar's work bridges theoretical computational methods with practical engineering applications, particularly in the areas of fluid dynamics and aerospace systems. His research group appears to focus on applying advanced computational techniques like the Lattice Boltzmann method to solve complex fluid flow problems in various contexts including energy systems, transportation infrastructure, and aircraft design.

Robert TiltonView profile

Professor

• Complex Fluids
• Colloid Chemistry
• Nanotechnology
• +3 more

Robert Tilton is the Chevron Professor of Chemical Engineering at Carnegie Mellon University (CMU), a position he has held since 1992. He holds dual fellowships from the American Chemical Society (ACS) and the American Institute for Medical and Biological Engineering. His research focuses on complex fluid interfacial phenomena, including colloid chemistry, surfactant systems, and nanotechnology applications in environmental and biomedical contexts. Tilton earned a B.Ch.E. from the University of Delaware (1986) and an M.S. (1987) and Ph.D. (1991) in Chemical Engineering from Stanford University, under advisors Alice Gast and Channing Robertson. His work has been recognized with the Victor K. LaMer Award and recent ACS honors for contributions to colloid chemistry. Professor Tilton’s research integrates fundamental studies of macromolecule and nanoparticle behavior at fluid interfaces with applied projects in drug delivery, sustainable emulsifiers, and environmental nanotechnology. He has led the ACS Division of Colloid and Surface Chemistry, co-chaired major symposia at CMU, and advised over 30 Ph.D. students. His group collaborates on aerosolized drug carriers, responsive polymer brushes, and interfacial engineering for industrial and medical applications. Current projects include optimizing surfactant systems for enhanced Marangoni flow control and developing pH-responsive nanocarriers for agrochemical delivery. Leadership Roles: NIH Nanotechnology Study Section member, Scientific Council at CODIRECT, ACS symposium organizer Labs/Teams: Tilton Group (research team includes postdocs and students focused on colloids, interfaces, and nanotechnology) His scientific contributions span over 200 publications, with recent emphasis on diffusiophoretic transport, surfactant synergism, and emulsion stability. Awards include the 2023 ACS Colloid Chemistry Award, reflecting his enduring impact on the field.

• Thermodynamics
• Molecular Simulation
• Adsorption Phenomena
• +7 more

Erich A. Müller is a Professor of Thermodynamics at Imperial College London's Department of Chemical Engineering (Faculty of Engineering). He holds a PhD from Cornell University and previously worked at Universidad Simón Bolívar in Venezuela, serving as Department Head and Director of Undergraduate/Graduate studies. His research focuses on molecular simulation of complex fluids, adsorption phenomena, phase equilibria, and interfacial properties, leveraging high-performance computing and machine learning. Key projects include carbon membrane water purification, self-assembly of liquid crystals, and polymer-calcite interactions. Educational background: PhD in Chemical Engineering (Cornell University). Affiliations include the Centre for Process Systems Engineering, Industrial Biotechnology Hub, and Institute for Molecular Science and Engineering. His work spans interdisciplinary topics like nanomaterials for environmental applications, graphene rippling dynamics, and fluid-solid phase transitions. Notable contributions include pioneering studies on SAFT equations of state and molecular dynamics of confined fluids. Research interests emphasize thermodynamic modeling, with applications in energy, materials science, and environmental engineering. His group uses cutting-edge computational methods to explore fluid behavior in extreme conditions, such as nanoporous carbon membranes and subsurface fluid dynamics. Media highlights include coverage in Chem. Eng. Education and appearances in science festivals for nanoscience visualization. Key technical contributions include developing coarse-grained force fields (SAFT-γ Mie), interfacial tension modeling, and machine learning-enhanced thermodynamic predictions. Current projects address challenges in carbon capture, polymer-enhanced oil recovery, and sustainable materials design.

Eric JohnsenView profile

Professor

• Fluid Mechanics
• Multiphase Flow
• Cavitation and Bubble Dynamics
• +10 more

Eric Johnsen is a Professor of Mechanical Engineering at the University of Michigan, where he has served since 2010, progressing from Assistant Professor to Associate Professor and currently Professor since 2023. He also serves as Director of the PhD Program in Scientific Computing (2024-present) and holds affiliate positions with the Michigan Institute for Computational Discovery and Engineering, Michigan Institute for Plasma Science and Engineering, and the Applied Physics Program. Dr. Johnsen earned his PhD in Mechanical Engineering from Caltech in 2008, following an MS from Caltech in 2002 and a BS in Mechanical and Environmental Engineering from UCSB in 2001. Prior to joining Michigan, he was a Post-doctoral Fellow at Stanford's Center for Turbulence Research from 2007-2009. His research focuses on fluid mechanics with emphasis on multiphase flows, cavitation and bubble dynamics, computational fluid dynamics, high-speed flows and shock waves, turbulence and mixing, interfacial instabilities, plasmas, and high-performance computing. His work has significant applications in biomedical engineering (particularly ultrasound and cavitation therapy), energy sciences, aeronautics, hypersonics, naval engineering, and high-energy density physics. He has developed novel numerical methods for simulating complex fluid phenomena, including high-order accurate finite difference, finite volume, and discontinuous finite element methods. Analysis of his recent publications reveals a strong focus on multiphase flows, shock-interface interactions, bubble dynamics, and advanced numerical methods. His work spans applications from astrophysics (supernova remnants) to biomedical engineering (microbubble dynamics in blood-like fluids) to high-energy density physics (laser-plasma interactions). Dr. Johnsen has received numerous honors including the NSF CAREER Award (2013), ONR Young Investigator Award (2012), and Ralph E. Powe Junior Faculty Enhancement Award (2010). He was named an Associate Fellow of AIAA in 2021 and received the Mechanical Engineering Department Achievement Award at Michigan in 2017. He has advised over 25 PhD students to completion, with many going on to prominent positions in academia and industry. His research has been supported by numerous grants from DOE, NNSA, ONR, NIH, NSF, national laboratories, and industry partners. His laboratory, the Scientific Computing and Flow Physics Laboratory, maintains strong collaborations with multiple departments and institutions. The laboratory conducts cutting-edge research using high-performance computing to investigate fundamental fluid phenomena with applications across multiple disciplines. Current projects include algorithm/software/hardware co-design for high energy density applications, research in high-energy-density laboratory astrophysics, and developing physics-based approaches for blast and traumatic brain injury prediction.

Anchal SareenView profile

Assistant Professor

• Experimental Hydrodynamics
• Renewable Energy Harvesting
• Marine Propulsion
• +3 more

Anchal Sareen serves as an Assistant Professor with a joint appointment in the Department of Mechanical Engineering and the Department of Naval Architecture & Marine Engineering at the University of Michigan's College of Engineering. Her academic role centers on experimental hydrodynamics research, and she directs the Experimental Fluid Dynamics Lab, which operates under the mission statement '#Driven to create an impact!' in advancing fluid mechanics knowledge. Her primary research focuses on Experimental Hydrodynamics with specific emphasis on renewable energy harvesting systems and energy-efficient marine propulsion technologies. Through rigorous experimental methodologies, she investigates complex fluid phenomena including vortex interactions, flow control mechanisms, and surface modification effects on hydrodynamic forces. Her work frequently examines bluff bodies such as spheres and airfoils across various Reynolds number regimes, bridging fundamental fluid mechanics principles with practical engineering applications in sustainable energy and maritime transportation. Analysis of her publication record from 2022-2025 reveals a concentrated research trajectory in manipulating fluid flows around spheres and airfoils through passive and active control strategies. Key thematic threads include surface roughness effects, smart morphable skins for adaptive drag reduction, lift generation via asymmetric modifications, and vortex-induced vibration control. These studies demonstrate consistent innovation in hydrodynamic force modulation with direct implications for renewable energy device optimization and next-generation marine propulsion systems. The Experimental Fluid Dynamics Lab, under her leadership, maintains experimental facilities dedicated to cutting-edge hydrodynamics research. The lab's work integrates advanced flow visualization, force measurement techniques, and adaptive surface technologies to address critical challenges in energy sustainability and marine engineering, emphasizing practical impact through experimental validation.

Hongkai ZhaoView profile

Professor

• Applied and Computational Mathematics
• Inverse Problems
• Imaging
• +4 more

Professor Hongkai Zhao is a Chancellor’s Professor and Chair of the Mathematics Department at the University of California, Irvine (UCI), with a joint appointment in Computer Science. He holds a B.S. from Peking University, M.S. from USC, and Ph.D. from UCLA, all in Mathematics. His research focuses on computational and applied mathematics, including moving interface problems, inverse problems, imaging, and Hamilton-Jacobi equations. He is renowned for developing the Fast Sweeping Method (FSM) for solving hyperbolic PDEs efficiently. Education: B.S. (Peking University, 1990), M.S. (USC, 1992), Ph.D. (UCLA, 1996). Employment: UCI faculty since 1999, Szego Assistant Professor at Stanford (1996–1999). Research interests span computational fluid dynamics (moving interfaces), inverse problems (tomography, wave propagation), and imaging (medical imaging, computer vision). Key contributions include numerical methods for surfactant-laden drops, fast algorithms for Hamilton-Jacobi equations, and 4D cone-beam CT reconstruction. Awards include the Feng Kang Prize (2007), Sloan Fellowship (2002–2004), and Chancellor’s Professorship (2016–present). He has authored over 100 publications on numerical methods, inverse problems, and PDEs. Lab affiliations: Active in interdisciplinary research at UCI, leading projects in computational mathematics and imaging science. Current work includes high-frequency wave propagation, multi-scale modeling, and geometric data analysis.

Dieter BotheView profile

Professor

• Mathematical Analysis
• Multiphase Flow Modeling
• Computational Fluid Dynamics
• +4 more

Prof. Dr. Dieter Bothe is a full Professor at TU Darmstadt and head of the Mathematical Modeling and Analysis (MMA) lab. He holds a habilitation in mathematics from Universität Paderborn (2000) and previously served as Chair for Mathematics/CCES at RWTH Aachen (2005–2009). His research focuses on multiphase flow modeling, mathematical analysis, and computational fluid dynamics, with emphasis on interfacial phenomena, surfactant dynamics, and thermodynamic frameworks. Key roles include coordinating the DFG-Priority Programme SPP 1506 (2010–2017) and currently co-coordinating the DFG-CRC 1194. He serves on editorial boards for Nonlinear Analysis: Real World Applications and International Journal of Multiphase Flow . His work bridges theoretical rigor with industrial applications, particularly in viscoelastic two-phase flows and subgrid-scale modeling. Education : - Study in Mathematics/Computer Science/Physics at Universität Paderborn (graduated 1993) - Habilitation in Mathematics (2000) Research Themes : - Multiphase flow dynamics - Thermodynamic modeling of interfacial systems - Numerical methods (VOF, Level Set) - Surfactant and mass transfer processes His computational frameworks, such as the plicRDF-isoAdvector VOF method and OpenFOAM modules, advance wetting simulations and industrial flow modeling. Recent work emphasizes scale-bridging model hierarchies and data-driven subgrid-scale approaches for convection-dominated flows. Grants & Leadership : - Coordinator of the research profile 'Thermo-Fluids & Interfacial Phenomena' (since 2021) - Member of TU Darmstadt's Scientific Advisory Council Bothe's lab (MMA) develops innovative methods for complex fluid systems, addressing challenges in capillary phenomena, viscoelastic flows, and reactive bubbly flows. His contributions span fundamental analysis and applied engineering solutions.

Jeanette HussongView profile

Professor

• Fluid Mechanics
• Aerodynamics
• Multiphase Flows
• +7 more

Prof. Dr.-Ing. Jeanette Hussong is a Professor and Head of the Fluid Mechanics and Aerodynamics department at TU Darmstadt's College of Mechanical Engineering. Her research focuses on particle flow dynamics, multiphase systems, and advanced flow measurement techniques. Key areas include supercooled droplet impact mechanisms, icephobic coatings, and thermal management in electric machines. She employs computational models like Reynolds stress modeling and advanced experimental methods such as A-PTV and phosphorescence decay analysis. Recent work explores microfluidic oxygen distribution, snowflake melting behavior, and nanoscale dewetting instabilities. Hussong leads studies on aerodynamic ice accretion, blood analog fluid dynamics, and supersonic atomization processes for metal powder production. Her interdisciplinary contributions bridge fluid mechanics, materials science, and biomedical engineering. Education details are not explicitly listed in the provided texts, but her academic title (Dr.-Ing) indicates a Doctorate in Engineering. Research highlights include over 50 peer-reviewed articles on droplet impact dynamics, phase change phenomena, and multiphase flow simulation. She collaborates on projects involving industrial applications such as turbine cooling systems and additive manufacturing. Laboratory facilities support high-speed imaging, particle tracking velocimetry, and thermal field analysis. Current initiatives focus on optimizing spray cooling systems and developing predictive models for particle fractionation in microfluidic channels. Awards and grants are not explicitly mentioned in the available data, but her extensive publication record suggests sustained research excellence. Advising activities and student supervision details are not provided in the scraped content. Her work has direct applications in aerospace engineering, renewable energy systems, and biomedical device development.

Douglas DurianView profile

Professor

• Soft Condensed Matter Physics
• Metamaterials
• Machine Learning
• +4 more

Douglas Durian is the Mary Amanda Wood Professor of Physics and Astronomy at the University of Pennsylvania, where he also serves as Founder and Associate Director of the Center for Soft and Living Matter. He holds a secondary appointment in Mechanical Engineering and Applied Mechanics. His research focuses on soft condensed matter physics, particularly the dynamics of foams, granular systems, and emergent learning in physical networks. Durian earned his Ph.D. from Cornell University (1989) and A.B. from the University of Chicago (1984). Positions: Mary Amanda Wood Professor (2021–), Professor of Physics (2004–2021), UCLA Professor (1991–2004). Education: Ph.D. Cornell, A.B. University of Chicago. Research explores novel systems like Contrastive Local Learning Networks (CLLNs) – metamaterials capable of autonomous learning akin to neural networks. His work bridges physics, data science, and neuroscience, addressing energy-efficient AI and fundamental material behavior. Key contributions include studies on granular flow dynamics, foam coarsening, and clogging mechanisms in hoppers. Durian’s team employs interdisciplinary methods, combining experiments with machine learning analysis. Scientific Achievements: Developed physics-driven learning networks for energy-efficient computation. Advanced understanding of granular matter dynamics and clogging transitions. Received AAAS and APS Fellowships, and Sigma Xi Lecturer honor. Labs/Teams: Leads the Durian Research Group at the Laboratory for Research on the Structure of Matter (LRSM), focusing on foams, fluids, and granular systems. Collaborates with interdisciplinary teams in physics, engineering, and data science.