Parviz Moin holds the Franklin P. and Caroline M. Johnson Professorship in Stanford University's School of Engineering. As founding director of the Center for Turbulence Research (CTR)—a NASA-Stanford consortium established in 1987—he has pioneered computational methods for turbulence physics, including direct numerical simulation and Large Eddy Simulation (LES) techniques. CTR serves as an international hub for turbulence studies across engineering, mathematics, and physics disciplines. Moin's research encompasses computational physics of turbulent flows, with emphasis on boundary layer control, hypersonic aerodynamics, propulsion systems, and aircraft icing. His recent work advances high-fidelity simulations for aerospace applications, particularly developing wall models for LES that accurately capture separation phenomena under complex pressure gradients and Reynolds number effects. Recent publications demonstrate extensive applications of LES to aircraft design challenges, including transonic buffet prediction, high-lift configuration analysis, and icing aerodynamics. Investigations consistently address fundamental turbulence physics while developing practical computational tools for aerospace engineering, with particular focus on hypersonic boundary layers, flow separation mechanisms, and conjugate heat transfer in iced environments.
Themistoklis Sapsis is a Professor in the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT), where he also holds an affiliation with the MIT Institute for Data, Systems, and Society. He earned his Ph.D. in Mechanical Engineering from MIT in 2011 and previously served as an Assistant Research Scientist at NYU’s Courant Institute of Mathematical Sciences. His research focuses on developing analytical, computational, and data-driven methods to predict and quantify extreme events in high-dimensional nonlinear systems, such as turbulent fluid flows and mechanical systems. Key areas include probabilistic modeling of climate extremes, machine learning for climate simulation corrections, and uncertainty quantification in complex dynamical systems. Recent work emphasizes applications in ocean engineering (e.g., vortex-induced vibrations, wave energy systems) and environmental science (e.g., spatially resolved climate extremes, bias correction in Earth system models). His methodologies combine stochastic emulators, Bayesian experimental design, and neural networks to address challenges in data sparsity and model fidelity. Notable contributions include frameworks for correcting coarse-scale climate simulations using machine learning, real-time ocean temperature reconstruction from satellite data, and data-driven modeling of hydrodynamic interactions in marine risers. His research bridges theoretical developments with practical applications in energy systems, structural monitoring, and autonomous systems. Prof. Sapsis collaborates with interdisciplinary teams and has contributed to initiatives such as FIRSTLING-DIGIMAR (a marine riser digital twin) and multi-fidelity frameworks for autonomous seakeeping. His work is supported by grants focused on advancing machine learning in scientific modeling and extreme event prediction.
Gustav Amberg is a Professor at KTH Royal Institute of Technology, affiliated with the Flow Mechanics research group within the School of Engineering Sciences. His primary appointment is in the Department of Mechanics, where he focuses on fluid dynamics, multiphase flow, and interfacial phenomena. He holds a permanent full professorship with no indication of兼职 roles. Research interests center on dynamic wetting mechanisms, phase-field modeling, and computational fluid dynamics applied to complex fluid systems. His work explores contact line behavior, microstructured surface interactions, and material phase transformations. Notable areas include rapid droplet spreading, viscoelastic fluid dynamics, and boiling heat transfer on engineered surfaces. Recent studies investigate the interplay between surface topography and wetting dynamics, oscillatory contact line phenomena, and numerical benchmarking across molecular and continuum models. He has pioneered methods for simulating surfactant effects in multiphase flows and developed novel approaches for analyzing weld pool behavior during sintering processes. No academic awards or grants are explicitly listed in the provided materials. His advising record remains undisclosed, though his prolific publication history suggests active research supervision. Laboratory affiliations are not detailed, but his work aligns with KTH's broader initiatives in computational mechanics and materials science.
Professor Manolis Gavaises is a leading academic in the field of mechanical engineering and computational fluid dynamics at City St George's, University of London, where he holds the position of Professor in the School of Engineering and Mathematical Sciences. He earned his PhD from Imperial College London and has been a faculty member since 2001, progressing to full Professor in 2009. His research is centered on advanced modeling of multi-phase flows, cavitation, and fuel injection systems, with extensive collaborations across Europe and industry partners such as Delphi, Caterpillar, and BP. Education: DIC, Mechanical Engineering, Computational Fluid Dynamics, Imperial College London, 1997 PhD, Mechanical Engineering, Computational Fluid Dynamics, Imperial College London, 1997 Diploma (5 years), Mechanical Engineering, National Technical University of Athens, 1992 His research interests span computational fluid dynamics, cavitation, fuel injection, atomization, high-pressure and supercritical flows, and alternative fuels . He has developed advanced numerical models and experimental techniques, including X-ray phase contrast imaging and high-pressure test rigs. His work integrates fundamental DNS and LES simulations with industrial applications in automotive, marine, aerospace, and medical devices such as heart valves. The recent publications reflect a strong trend toward real-fluid thermodynamic modeling (e.g., PC-SAFT), multi-component fuel behavior, cavitation erosion, and advanced diagnostics . His research increasingly incorporates machine learning and high-fidelity imaging to understand complex flow phenomena across energy, transportation, and biomedical domains. Scientific Awards and Recognitions: Richard Way Prize (1998) Arch T. Collwell Merit Award (1998) Best Oral Paper, SAE World Congress (2006) PE Publication Award, IMechE (2007) Best Presentation Award, Engine Combustion Processes (2009) Fellow, IMechE (2013) Fellow, IMA (2015) As a dedicated mentor, Professor Gavaises has supervised 13 PhDs to completion and currently guides 23 doctoral students. He has secured over €16 million in EU and UK funding, including multiple Horizon 2020 Marie Skłodowska-Curie ITN projects (CAFÉ, HAOS, IPPAD), which support 46 early-career researchers globally. He has created academic opportunities for post-docs and junior faculty, significantly advancing the research profile of his institution. He leads the International Institute of Cavitation Research (IICR), co-founded in 2011 with partners from Loughborough University, TU Delft, and Imperial College, supported by The Lloyd’s Register Foundation. His lab maintains strong experimental capabilities, including a 2000bar pressure flow rig with micro-transparent nozzles and collaborations with Argonne National Laboratory for X-ray imaging.
Dr. Peter Fokker is a Researcher at Utrecht University's Faculty of Geosciences, specifically within the Department of Earth Sciences and the Experimental Rock Deformation/HPT group. He is affiliated with the Research Programme in Earth Sciences Utrecht (DES/IVAU) and has been actively publishing in geomechanics, subsidence modeling, and induced seismicity for over three decades. His work primarily focuses on the application of geomechanical principles to understand and model subsurface processes related to resource extraction and geothermal energy. Dr. Fokker's research interests span several interconnected domains in geomechanics and subsurface engineering. His primary focus is on experimental rock deformation , studying how rocks behave under various stress conditions. He has made significant contributions to subsidence modeling , particularly in the context of gas field depletion in the Netherlands. His work on induced seismicity has helped understand the relationship between subsurface operations and seismic events. Additional interests include geothermal energy systems , reservoir engineering , and the application of data assimilation techniques to improve subsurface characterization. His research often bridges theoretical models with practical applications in energy resource management. An analysis of Dr. Fokker's recent publications (2020-2025) reveals a strong focus on practical applications of geomechanics to real-world challenges. His work increasingly integrates InSAR technology and data assimilation methods to monitor and model subsidence processes. There's a clear emphasis on geothermal energy applications , reflecting growing interest in sustainable energy solutions. His research also demonstrates a sophisticated approach to modeling complex reservoir behaviors across multiple scales, from laboratory experiments to field-scale operations. The interdisciplinary nature of his work is evident in collaborations spanning geology, engineering, and environmental science. Dr. Fokker has supervised multiple research projects and students throughout his career, as indicated by the "Supervised Work (4)" reference in his profile. His research has been supported by various grants focused on subsidence modeling, geomechanics of energy resources, and induced seismicity. He has been involved in significant collaborative efforts, including the Dutch National Scientific Research Program on Land Subsidence. Dr. Fokker is part of the Experimental Rock Deformation/HPT group at Utrecht University, which conducts laboratory experiments and develops theoretical models to understand rock behavior under various conditions. His work contributes to the broader research ecosystem focused on sustainable resource management and understanding subsurface processes, with particular relevance to the Dutch context of gas extraction and land subsidence.
Nadia Shardt is an Associate Professor in the Department of Chemical Engineering at the Norwegian University of Science and Technology (NTNU). Her research focuses on interfacial thermodynamics, particularly in systems with nanoscale curvature, with applications spanning atmospheric science, biomedical cryopreservation, and industrial process optimization. She contributes to teaching courses such as TKP4580 - Chemical Engineering Specialization Project and KP3100 - Chemical Engineering . PhD in Chemical Engineering (University of Alberta, 2019) BSc in Chemical Engineering (University of Alberta, 2015) Postdoctoral researcher at ETH Zurich (2020-2022) Her work addresses fundamental challenges in phase behavior under curvature constraints, combining microfluidic experimentation , Gibbsian thermodynamic modeling , and machine learning techniques to study systems like CO 2 storage media, cloud microphysics, and food emulsions. Recent publications emphasize surface tension modeling for complex multi-component systems and cryoprotectant loading efficiency. Scientific awards include the ETH Postdoctoral Fellowship Natural Sciences and Engineering Research Council of Canada (NSERC) Postdoctoral Fellowship Outstanding Academic Fellows Programme 2024-2028
Lande Liu is a Senior Lecturer in Chemical Engineering at the University of Huddersfield's School of Applied Sciences. Previously, he held a Lectureship at the University of Manchester (2010-2014), and earlier worked as an industrial consultant and research fellow at Leeds and Sheffield Universities. His academic journey began with a MEng in Chemical Engineering and a PhD in kinetic theory of aggregation from Sheffield (2004), preceded by a visiting PhD at Twente University (2002). Education: PhD in Chemical Engineering (University of Sheffield, 2004) Visiting PhD (Twente University, 2002) MEng in Chemical Engineering (Tsinghua University, 1999) BSc in Applied Mathematics (Tsinghua University, 1996) Liu's research focuses on multi-scale particle interactions (molecular to granular) using kinetic theory of aggregation, with applications spanning nanotechnology, pharmaceutical engineering, and sustainable chemical processes. His work aligns with UN Sustainable Development Goals for environmental protection and industrial innovation. Recent publications examine particle deposition in turbulent flows, enhanced heat exchanger designs, and nanofluid stabilization techniques. He teaches core chemical engineering topics including transport phenomena, unit operations, and process design. Active in collaborative research, Liu has partnered with institutions across Europe on projects involving spectroscopy, ultrasonics, and dynamic modeling. His technical expertise includes particle size analysis, tomography, and computational simulation of complex systems.
Ronald G. Larson serves as the George Granger Brown Professor of Chemical Engineering and A. H. White Distinguished University Professor at the University of Michigan's College of Engineering, with additional appointments in Mechanical Engineering and Macromolecular Science & Engineering. His research leadership spans multiple departments within the Chemical Engineering Division, where he directs the Larson Lab focused on fundamental and applied soft matter physics. His research program investigates complex fluids through computational and theoretical frameworks, emphasizing polymer physics, rheology, and molecular simulations. Key thrusts include polymer melt processing, biomembrane dynamics, colloidal systems, and polyelectrolyte coacervation. The group employs advanced techniques like Brownian dynamics, coarse-grained modeling, and multiscale simulation to address challenges ranging from industrial polymer processing to biomedical applications. Recent publications (2023-2025) reveal strong momentum in rheological modeling of complex fluids, with particular emphasis on self-healing materials, wax deposition in pipelines, and crystallization mechanisms. The work bridges fundamental molecular insights with industrial applications, demonstrating consistent high-impact output across polymer science, soft matter physics, and chemical engineering domains. The Larson Lab operates as a collaborative hub within the Chemical Engineering Department, leveraging computational resources to advance understanding of fluid mechanics and material properties. Current projects integrate machine learning with traditional modeling approaches, reflecting the group's commitment to methodological innovation while maintaining strong connections to experimental validation and real-world engineering problems.
Andrea Prosperetti is a Full Professor at the University of Twente's Physics of Fluids department within the MESA+ Institute. His research focuses on multiphase flow dynamics, bubble behavior, and fluid mechanics, contributing to Sustainable Development Goals through advanced fluid dynamics studies. He has published extensively with over 224 research outputs and 70 h-index. Key research areas include fluid instabilities, interfacial phenomena, and numerical simulations of complex flows. Recent work explores particle-resolved convection, Taylor bubble dynamics, and hydraulic jumps on conical surfaces. His collaborations span international institutions, emphasizing computational modeling and experimental validation. Prosperetti has delivered numerous presentations on particulate flow simulations and fluid dynamics, and has held editorial and committee roles in organizations like the Institute for Cavitation Research and Deutsche Forschungsgemeinschaft (DFG).
Philipp Schlatter is a Professor in the Department of Mechanics at KTH Royal Institute of Technology. His research focuses on fluid mechanics, turbulence, and computational fluid dynamics (CFD), with expertise in high-performance computing and direct numerical simulations (DNS). He leads projects involving scalable CFD frameworks like Neko and Nek5000, and investigates turbulent boundary layers, flow control, and coherent flow structures. His work includes experimental and numerical studies of wing profiles, rotating systems, and transition dynamics. Schlatter teaches courses on computational fluid dynamics and turbulence, emphasizing both theoretical and practical aspects of fluid mechanics. Key research interests include developing numerical methods for high-fidelity simulations, understanding turbulence mechanisms, and optimizing flow control strategies. His contributions span aerodynamics, heat transfer, and the application of machine learning to fluid dynamics problems. Schlatter collaborates extensively on interdisciplinary projects, leveraging advanced computing resources to address complex fluid flow phenomena. Publications highlight advancements in DNS frameworks, Bayesian optimization for flow control, and analysis of turbulent structures in pipe and boundary layer flows. His research also addresses challenges in measurement techniques and uncertainty quantification in CFD simulations.
Kurt Maute is a Professor and Palmer Engineering Chair at the University of Colorado Boulder’s College of Engineering and Applied Science (CEAS). He currently serves as Associate Dean for Undergraduate Education. His academic journey includes a PhD in Civil Engineering (University of Stuttgart, 1998) and a Dipl.-Ing. in Aerospace Engineering (University of Stuttgart, 1992). He has held progressively senior roles at CU Boulder, including Associate Dean for Research (2012–2014), Associate Professor (2006–2012), and Assistant Professor (2000–2006). Maute’s research focuses on structural topology optimization, multi-disciplinary optimization, and aeroelastic systems. He has pioneered methods integrating XFEM, level-set techniques, and isogeometric analysis for complex engineering problems. His work spans fluid-structure interaction, hypersonic vehicle design, and additive manufacturing. His notable contributions include advancements in immersed boundary methods, multi-material optimization, and uncertainty quantification. Awards include the NSF Career Award (2004) and Palmer Endowed Chair (2016–present). Maute’s lab (Aerospace Mechanics Research Center, AMREC) addresses challenges in computational mechanics and multi-physics systems. He has advised numerous students and led grants in battery modeling, topology optimization, and aerospace systems. His research bridges theory and application, emphasizing industrial relevance and computational innovation.
Daria Camilla Boffito is a Full Professor in the Department of Chemical Engineering at Polytechnique Montréal , holding the Tier-2 Canada Research Chair in Intensified Mechano-chemical Processes for Sustainable Biomass Conversion. Her research spans process intensification , catalysis , sonochemistry , photocatalysis , and metal extraction , with a focus on sustainability. Education: B.Sc. and Ph.D. in Industrial Chemistry from the University of Milan, M.Sc. in Industrial Chemistry and Management Current Research: Developing ultrasound-assisted extraction , CO2 conversion , and floating photocatalysts for wastewater treatment Collaborations: Works with Canadian and international companies on sustainable chemical processes Scientific Awards include the Canada Research Chair Tier-2 (2016-2021), NSERC Banting Postdoctoral Fellowship (2013-2016), and FRQNT PBEEE Postdoctoral Fellowship (2013-2016). Advising has seen 5 Ph.D. and 9 Master's students graduate. She leads the Engineering Process Intensification and Catalysis (EPIC) Laboratory and is a member of the Institut de génie biomédical .
Professor Michael Manhart is affiliated with the Technical University of Munich (TUM) as an Extraordinary Professor in the Department of Hydromechanics . His research focuses on fluid mechanics, turbulent flow dynamics, and computational fluid dynamics (CFD) simulations, particularly in porous media and environmental fluid mechanics. Education: Not explicitly stated in the provided text. His recent publications investigate turbulent flow over random sphere packs, scalar transport at porous-turbulent interfaces, acoustic resonances in HVAC systems, and nonlinear oscillatory flow modeling. He employs advanced numerical techniques like direct numerical simulations (DNS) and large-eddy simulations (LES) to study flow structures, energy budgets, and particle transport mechanisms. Professor Manhart collaborates with researchers such as Yoshiyuki Sakai, Simon Wenczowski, and Daniel Quosdorf. His work addresses applications in environmental engineering, hydraulic modeling, and industrial fluid dynamics, with a strong emphasis on validating computational models against experimental data (e.g., PIV measurements). He leads the Professorship for Hydromechanics at TUM, conducting high-fidelity simulations and experimental studies on topics like wall shear stress estimation, sediment erosion around cylinders, and turbulence decomposition in complex flows.
Sadaf Sobhani is an Assistant Professor in the Sibley School of Mechanical and Aerospace Engineering at Cornell University. Her research focuses on thermal management and energy conversion with applications in high-efficiency, low-emission energy systems, spacecraft thermal control, and electrochemical reactors for carbon dioxide conversion. Dr. Sobhani's educational background includes a B.S. (2014), M.S. (2015), and Ph.D. (2019) in Mechanical Engineering from Stanford University. During her doctoral studies, she worked as a research associate at the NASA Ames Research Center and later joined the Lawrence Livermore National Laboratory as a postdoctoral researcher. Her research program integrates computational modeling, experimental techniques, and advanced manufacturing to investigate flow, heat transfer, and chemical reactions in porous media. She leverages the connection between micro-scale features and macro-scale transport properties to develop innovative solutions for energy systems. Her work spans multiple disciplines including combustion engineering, electrochemical systems, and thermal management for spacecraft. Dr. Sobhani's publications demonstrate a consistent focus on porous media combustion, heat transfer optimization, and advanced diagnostic techniques. Her recent work has increasingly incorporated additive manufacturing and machine learning approaches to solve complex thermal management challenges, particularly for space applications and carbon dioxide conversion systems. Gallery of Fluid Motion Award, American Physical Society (2018) Accel Innovation Scholarship, Stanford Technology Ventures Program (2017) Graduate Public Service Fellowship, Haas Center for Public Service (2016) Schneider/MAP Sustainable Energy Fellowship, Haas Center for Public Service (2016) Graduate Research Fellowship, National Science Foundation (2015) AIAA Niagara Frontier Section 2025 Young Professional of the Year Award NASA Early Career Faculty Award (2023) NASA Early Stage Innovations Award (2023) Dr. Sobhani leads an active research group and has secured significant funding including a NASA Early Career Faculty Award and a FuzeHub grant with industry partners Lithoz America and Dimensional Energy. She has developed a new spacecraft thermal management course at Cornell and is actively mentoring students in her laboratory research. The Sobhani Lab, located at 182 Grumman Hall, spans approximately 850 sq. ft. and focuses on spacecraft thermal control, combustion research, non-intrusive diagnostic methods, and ceramic additive manufacturing. The lab utilizes advanced facilities including the Cornell NanoScale Science and Technology Facility and the Cornell High Energy Synchrotron Source.
Sean Andersson is a Professor in Mechanical Engineering and Systems Engineering at the College of Engineering, Boston University, and serves as Director of the BU Robotics Lab. His research bridges systems and control theory with applications in nanotechnology , atomic force microscopy , and robotics . His work in nanobioscience focuses on single molecule tracking and high-speed imaging in atomic force and fluorescence microscopy, leveraging control theory to enhance imaging capabilities. In robotics, he develops stochastic control methods for autonomous systems operating in complex environments, emphasizing multi-agent systems , sparsely sampled data , and symbolic control frameworks . Recent publications highlight trends in receding horizon control , persistent monitoring , neural style transfer for imaging , and stochastic policy optimization . The Andersson Lab also explores compressive sensing and optimal control for sensor networks and nanoscale fluid dynamics.
