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Yanlai ChenView profile

Professor

• Numerical Analysis
• Scientific Computing
• Machine Learning
• +3 more

Yanlai Chen is a Professor in the Department of Mathematics at the University of Massachusetts Dartmouth and serves as Chief Research Officer. He holds a PhD (2007) and MS (2007) from the University of Minnesota Twin Cities, and a BS (2002) from the University of Science and Technology of China. His research integrates numerical analysis, scientific computing, and machine learning. Research interests focus on numerical PDEs, model reduction, machine learning applications in scientific computing, uncertainty quantification, and high-performance computing algorithms. His work bridges theoretical mathematics with practical computational challenges. His publications demonstrate consistent focus on physics-informed neural networks (2021-2024), model reduction techniques (2019-2024), and computational methods for differential equations. Recent work shows increased emphasis on machine learning integration with traditional numerical methods. He has supervised 6 doctoral dissertations and leads the NSF-funded ACCOMPLISH program supporting STEM education through contextualized computing curricula.

Yangwen ZhangView profile

Assistant Professor

• Applied Mathematics
• Scientific Computing
• Numerical Analysis
• +7 more

Yangwen Zhang is an Assistant Professor in the Department of Mathematics at the University of Louisiana at Lafayette. He holds a Ph.D. in Mathematics from the Missouri University of Science and Technology (2018). Prior to his current position, he completed postdoctoral research at the University of Delaware (2018–2021) and Carnegie Mellon University (2021–2023). His research focuses on applied mathematics, scientific computing, numerical analysis, and data science. Core areas include computational methodologies for partial differential equations (PDEs), hybridizable discontinuous Galerkin (HDG) methods, model order reduction for PDEs, optimal control, inverse problems, and engineering applications. Key research trends in his publications emphasize HDG methods for PDEs, with applications in fluid dynamics, semiconductor devices, and electromagnetic wave propagation. Recent work highlights advancements in divergence-free approximations for Stokes equations, superconvergence analysis of HDG schemes, and spectral methods for thin surface modeling. His academic career includes postdoctoral fellowships at top institutions, reflecting expertise in numerical analysis and computational mathematics. No scientific awards are explicitly mentioned, but his extensive publication record underscores contributions to the field. Advising and grants are not detailed in the provided text, but his research collaborations span institutions like Missouri S&T, Delaware, and CMU. He currently contributes to the Mathematics Department at UL Lafayette, bridging theoretical and applied computational mathematics.

Daniel FloryanView profile

Assistant Professor

• Fluid Mechanics
• Nonlinear Dynamics
• Bio-inspired Flows
• +3 more

Dr. Daniel Floryan is the Kalsi Assistant Professor of Mechanical & Aerospace Engineering at the University of Houston, affiliated with the College of Engineering. His research focuses on fluid mechanics, nonlinear dynamics, bio-inspired flows, applied mathematics, and scientific machine learning. He holds a PhD from Princeton University (2019) and undergraduate degrees from Cornell University (BS and BA). Key research interests include turbulence dynamics, flow control, and optimization of swimming and flying systems. His work bridges experimental, computational, and theoretical approaches. Recent studies explore particle dynamics in Rayleigh-Bénard convection, energy-efficient propulsion, and data-driven modeling of fluid systems. Notable awards include the Teaching Excellence Award (2024), Thomas J. R. Hughes Fellowship (2021), and the Sibley Prize (2014). He teaches courses on fluid mechanics and machine learning applications in fluids. His lab (FLO Lab) investigates fluid-thermal interactions and bio-inspired locomotion, emphasizing practical innovations in propulsion and energy systems. Publications span over a decade, addressing topics like flexible foil propulsion, turbulence structure decomposition, and optimal swimming gaits. Collaborations with industry and academic partners drive applied fluid dynamics research with real-world impact.

Yingjie TangView profile

Research Professor

• Multi-physics modeling
• Fluid mechanics
• Heat transfer
• +7 more

Dr. Yingjie Tang is a Research Professor in the Department of Mechanical and Aerospace Engineering at the University of Houston. His research focuses on multi-physics modeling, fluid mechanics, heat transfer, and numerical simulations using CFD and FEA. He holds a Ph.D. in Mechanical Engineering from Texas A&M University. His work addresses challenges in pipeline integrity, multiphase flow dynamics, and advanced actuator design. Dr. Tang's research interests span computational fluid dynamics, thermodynamic systems, and corrosion analysis. He has contributed to studies on cavitating flows, two-phase flow modeling, and subsea engineering systems. His recent work includes electromagnetic inspection of pipelines, high-pressure flow dynamics, and analytical solutions for transient flows. His publications highlight expertise in both experimental and computational methods, with applications in offshore engineering, nanomaterials, and energy systems. Collaborations involve industries and academic partners in mechanical, aerospace, and petroleum engineering domains.

Nikolay DimitrovView profile

Researcher

• Wind Energy
• Structural Integrity
• Reliability Analysis
• +3 more

Nikolay Dimitrov is a Senior Researcher at the Department of Wind and Energy Systems , Technical University of Denmark (DTU). His work focuses on wind turbine structural integrity, reliability analysis, and data-driven optimization techniques. Active in Wind Energy and Structural Reliability research Developed open-source tools for Mann turbulence modeling and virtual load sensors Supervised multiple PhD students in projects related to floating wind turbines and uncertainty quantification His research spans surrogate modeling , fatigue load analysis , and environmental condition characterization for offshore wind turbines. Key contributions include machine learning-based load monitoring systems and frameworks for probabilistic design . Scientific recognition includes the Best Poster award (2019). Current projects involve reinforcement learning for wind farm control and federated digital twins for offshore wind energy .

Alan Wai Hou LioView profile

Associate Professor

• Wind Turbine Control
• Predictive Modeling
• Renewable Energy Systems
• +2 more

Alan Wai Hou Lio is an Associate Professor at the Technical University of Denmark's Department of Wind and Energy Systems, specializing in wind turbine control and energy system dynamics. His research develops advanced control strategies to optimize energy capture and reduce structural loads in wind farms. Research Focus: Predictive wind turbine control algorithms LiDAR-assisted flow measurement techniques Offshore floating turbine dynamics Real-time estimation methods for turbulent flows His publications demonstrate consistent focus on experimental validation of control theories, with recent work emphasizing field implementation challenges. Current projects include DigiWind (digital wind energy systems) and PowerKey (wind plant optimization). Education: PhD in Automatic Control (Sheffield, 2017), M.Eng in Electrical Engineering (Imperial College London, 2012).

Donsub RimView profile

Assistant Professor

• Numerical Analysis
• Partial Differential Equations (PDEs)
• Model Reduction
• +6 more

Donsub Rim is an Assistant Professor in the Department of Mathematics at Washington University in St. Louis. He holds a PhD from the University of Washington, advised by Randall J. LeVeque and Gunther Uhlmann. His research focuses on numerical analysis of PDEs, inverse problems, and low-rank neural network representations (LRNRs) for real-time solutions in geophysics, medical imaging, and plasma physics. Key areas include stability analysis of neural networks for tsunami early warning, fast inversion of the approximate discrete Radon transform (ADRT), and computational applications in geophysical hazard assessment. Rim is on leave during 2024-2025 as a Visiting Scholar at the University of Washington. Previously, he was a visiting scholar at Tohoku University's IRIDeS (2023) and affiliated with the Courant Institute and Columbia University. His work bridges mathematical theory with practical tools like the 'adrt' Python library for signal processing. Research Interests: Numerical methods for PDEs, model reduction techniques, neural network applications in high-consequence domains (e.g., tsunami forecasting), and ADRT-based algorithms for dimensional splitting and sparse approximations. Collaborations include projects on probabilistic tsunami hazard assessment (PTHA), earthquake kinematic effects on tsunami propagation, and meta-learning approaches for physics-informed networks. Publications highlight contributions to real-time prediction systems, stability analysis of neural networks, and manifold approximations for transport-dominated problems. His software developments, such as the ADRT Python package, emphasize open-source tools for scientific computing. Rim has advised or collaborated with researchers at institutions including Columbia University, Tohoku University, and the University of Washington. Grants and future work involve advancing LRNR frameworks for nonlinear hyperbolic problems, improving tsunami early warning systems via GNSS data, and exploring applications of sparse physics-informed backpropagation in geophysics and plasma physics.

Tina HannemannView profile

Lecturer

• Demography
• Social Statistics
• Health and Morbidity
• +4 more

Tina Hannemann is a Lecturer in Social Statistics at the University of Manchester's Department of Social Statistics. She serves as director of the MSc program in Social Research Methods and Statistics, and teaches undergraduate and postgraduate modules focusing on survey research methods, demography, and demographic forecasting. Her research explores demographic events in health, mortality, family formation, and socio-economic influences on ethnic minority populations. She has contributed to projects like the EU 'Families and Societies' initiative and NCRM studies on missing data compensation in bio-marker datasets. Education: PhD in Economics from Lund University (2012), MSc in Demography from Rostock University (2007), and advanced studies at INED (Paris). Professional experience includes roles at the Max-Planck Institute for Demographic Research and the CoDE/NCRM research groups. Research emphasizes methodological innovations in handling missing data and applied topics like immigrant fertility patterns and health disparities. She co-organized the Cathie Marsh Institute seminar series and developed training courses on STATA programming and data visualization. Her work addresses global challenges linked to UN SDGs including reduced inequalities and quality education through improved demographic literacy and data accessibility.

Alice ThompsonView profile

Associate Professor

• Fluid Mechanics
• Free-Surface Flows
• Mathematical Modelling
• +4 more

Alice Thompson is a Reader (Associate Professor) in Applied Mathematics at the University of Manchester, specializing in fluid mechanics with emphasis on free-surface phenomena. She leads research on bubble/drop dynamics, thin films, and multiphase flows using mathematical modeling, asymptotic methods, and experimental collaborations. Research Focus: Her work explores interfacial flows in confined geometries, control of liquid films, and pattern formation in deposition processes. Key applications include microfluidics, inkjet printing, and pulmonary flows. Current projects investigate viscoplastic fluid behavior, bubble interactions in microchannels, and airway deposition modeling. Education: PhD in Applied Mathematics (University of Nottingham, 2012) Part III Mathematics (University of Cambridge, 2007) BA Mathematics (University of Cambridge, 2006) Research Trends: Recent publications demonstrate evolving focus from fundamental bubble/drop dynamics (2016-2019) toward controlled flow applications in biomedical/industrial contexts (2020-present), with increasing emphasis on yield-stress fluids and functional surface interactions. Current PhD Students: Jake Harris: Multiphase flow modeling Sammy Ayoubi: Control of disordered systems James Shemilt: Lung deposition modeling

Peter Howard HaynesView profile

Professor

• Atmosphere-Ocean Dynamics
• Climate Modeling
• Fluid Mechanics
• +3 more

Professor Peter Howard Haynes is a faculty member at the University of Cambridge's Faculty of Mathematics, specializing in Atmosphere-Ocean Dynamics. His research bridges theoretical fluid mechanics, climate modeling, and atmospheric physics, with a focus on stratosphere-troposphere interactions, convective processes, and climate variability. He leads the Atmosphere-Ocean Dynamics research group, contributing to fundamental understanding of geophysical fluid systems. His work emphasizes: Dynamics of convective overshoots impacting stratospheric water vapor Climate sensitivity of Southern Ocean convection Stochastic modeling of atmospheric jets QBO influences on tropical clouds Fluid mechanical analysis of mixing processes Recent publications demonstrate consistent focus on climate change impacts, with 80% of articles addressing warming-related feedbacks, model uncertainties, and hydrological cycle interactions. Methodology combines theoretical frameworks, machine learning techniques, and observational validation.

Oliver D. StreetView profile

Research Fellow

• Geometric Mechanics
• Stochastic Modelling
• Fluid Dynamics
• +4 more

Oliver D. Street is a Research Fellow at the Grantham Institute - Climate Change and the Environment, Imperial College London, within the Faculty of Natural Sciences. His research focuses on geometric mechanics and stochastic modeling, particularly in fluid dynamics and plasma physics. He holds a PhD from Imperial College London (2022) and an MRes in Mathematics of Planet Earth from Imperial College London and the University of Reading (2019). Key research areas include stochastic differential equations, wave-current interactions, free boundary problems, and plasma dynamics in thin domains. His work bridges geometric methodologies with real-world applications in oceanography and climate science. Recent contributions address soliton behavior, thermal Green-Naghdi models, and Hall magnetohydrodynamics. Street has collaborated with institutions like the UK Met Office and co-authored over 15 peer-reviewed articles. His teaching experience includes analysis and geometric mechanics at undergraduate/postgraduate levels. Technical skills span Python, MATLAB, FORTRAN, and scientific communication.

Rayhaneh AkhavanView profile

Associate Professor

• Fluid Mechanics
• Turbulence
• Computational Fluid Dynamics
• +4 more

Rayhaneh Akhavan is an Associate Professor in the Department of Mechanical Engineering at the University of Michigan, Ann Arbor. She holds a tenured position and is actively involved in research and teaching. Her educational background includes: Ph.D. in Mechanical Engineering from the Massachusetts Institute of Technology (1987) M.S. in Mechanical Engineering from the Massachusetts Institute of Technology (1982) B.S. in Engineering and Applied Science from the California Institute of Technology (1980) Professor Akhavan's research focuses on fluid mechanics, with particular emphasis on turbulence physics, modeling, and control. She employs high-fidelity computations and reduced-order modeling for complex turbulent flows involving multi-phase systems, fluid-structure interactions, and viscoelastic effects. Her work also explores biomimetic and bio-inspired concepts for flow control and drag reduction, utilizing lattice Boltzmann, pseudo-spectral, and stochastic methods within high-performance computing frameworks. Applications span energy, environmental, aerospace, and naval disciplines. Her notable recognition includes the Robert T. Knapp Award from the American Society of Mechanical Engineers in 1995 for the best paper on "Dynamics of a Turbulent Jet Interacting with a Free Surface". As an advisor, Professor Akhavan adopts a "hands-on" mentoring style, meeting with students daily to weekly depending on their needs. She expects students to be actively engaged in literature review, coding, simulations, data analysis, and paper writing. Authorship is typically granted with the student as first author when they provide a complete draft. Students are required to publish at least one (preferably up to three) papers in top journals for graduation. Funding for conference attendance (such as APS/DFD) is provided through Rackham Travel Grants and her research funds. She maintains a small research group and does not hold regular group meetings, focusing instead on individual mentoring.

Bogdan EpureanuView profile

Professor

• Biological Systems
• Epidemiological Systems
• Aerospace Structures
• +5 more

Bogdan Epureanu is a Professor of Mechanical Engineering at the University of Michigan, Ann Arbor, holding the Roger L. McCarthy Professorship and the Arthur F. Thurnau Professorship. He serves as the Director of the Automotive Research Center, a key institution for vehicle technology development for the U.S. Army. His educational qualifications are: Ph.D. in Mechanical Engineering, Duke University, 1999 Graduate Studies, University of Valladolid, 1994 M.S. in Mechanical Engineering, Galati University, 1993 Graduate Studies, École Nationale Supérieure des Mines de Paris, 1992 Professor Epureanu's research spans biological and epidemiological systems, aerospace and automotive structures, and turbomachinery. His work involves creating novel mechano-chemical dynamic models, developing sensitive diagnosis techniques, forecasting tipping points in complex systems, and advancing reduced order models and control methodologies for multi-physics systems. His numerous accolades include: Roger L. McCarthy Professorship (2024) N.O. Myklestad Award (2022) Archie Higdon Distinguished Educator Award (2021) Arthur F. Thurnau Professorship (2019) National Science Foundation CAREER Award (2004) 1938E Award, College of Engineering, University of Michigan (2007) He actively mentors Ph.D. students, several of whom have received prestigious awards, and leads significant research initiatives funded by major grants, including a $100 million award from the U.S. Army for the Automotive Research Center through 2028. Professor Epureanu directs the Epureanu Research Group and the Automotive Research Center, fostering innovation in autonomous vehicle technologies and complex systems modeling.

Anna StefanopoulouView profile

Professor

• Control Systems
• Energy Storage
• Automotive Engineering
• +6 more

Anna Stefanopoulou is a Professor at the University of Michigan College of Engineering , holding the William Clay Ford Professor of Manufacturing and courtesy appointments in Naval Architecture & Marine Engineering and Electrical Engineering & Computer Science . Her work focuses on estimation and control of electrochemical systems including batteries, fuel cells, and internal combustion engines. Education : Ph.D. in Electrical Engineering & Computer Science (1996), M.S. in Electrical Engineering & Computer Science (1994), M.S. in Naval Architecture & Marine Engineering (1992), and Diploma in Naval Architecture & Marine Engineering from National Technical University of Athens (1991). Her research spans energy storage systems for automotive applications, emphasizing lithium-ion battery modeling , fuel cell water management , and hybrid powertrain optimization . She has pioneered adaptive observers for battery state-of-charge estimation and thermal management strategies for cold-temperature battery operation. Her 15 most recent publications (2013-2017) explore electrochemical degradation , mechanical stress in battery cells , and fuel cell dynamics , with applications in electric vehicles and microgrids . Key scientific awards include: Fellow, SAE (2018) IEEE Control Systems Technology Award (2016) Rackham Distinguished Graduate Mentor Award (2018) ASME Gustus L. Larson Memorial Award (2009) NSF CAREER Award (1997) She advises doctoral students through modeling, lab work, and authorship prioritization , supporting internships post-second year and requiring 4+ conference papers per student . Her Battery Control Group collaborates on automotive electrification and energy storage safety .

Charikleia StouraView profile

Researcher

• Structural Health Monitoring
• Railway Infrastructure
• Dynamical Systems
• +3 more

Charikleia Stoura is a Researcher at the Chair of Structural Mechanics and Monitoring within the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich . Her work focuses on leveraging data from traversing trains to monitor railway infrastructure, including tracks and bridges. Diploma : Civil Engineering, National Technical University of Athens (2015) Ph.D. : Civil and Environmental Engineering, Hong Kong University of Science and Technology (2021) Postdoctoral Fellowships : ETH Zürich (2022), Marie Skłodowska-Curie Global (2024) Her research integrates Bayesian filtering , data-driven modeling , and on-board sensor networks to develop cost-effective solutions for continuous structural health monitoring (SHM) of transport infrastructure. This includes applications for rail roughness profiling and bridge modal identification using vibration data from trains. Key publication themes include: Imitation learning from observation for input estimation Vehicle-bridge interaction analysis under seismic loads Markov-Chain Monte Carlo Bayesian parameter tuning Virtual sensing techniques for railway infrastructure Reduced-order modeling for rail roughness identification Notable scientific awards include: ETH Zürich Postdoctoral Fellowship Marie Skłodowska-Curie Global Postdoctoral Fellowship