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Teng WuView profile

Professor

• Wind Engineering
• Hurricane Engineering
• Structural Engineering
• +8 more

Teng Wu is a Professor and Director of Graduate Studies in the Department of Civil, Structural and Environmental Engineering at the School of Engineering and Applied Sciences, University at Buffalo . His research focuses on wind engineering, hurricane risk assessment, and climate change adaptation in infrastructure systems. PhD, Civil Engineering, University of Notre Dame (2013) MS, Civil Engineering, University of Notre Dame (2012) MS, Bridge Engineering, Tongji University (2010) BS, Civil Engineering, Tongji University (2007) Minor, Financial Engineering, Fudan University (2006) Teng Wu's research spans multiple domains including Wind Engineering , Hurricane Engineering , Structural Engineering , and Climate Change Adaptation . He specializes in nonlinear aerodynamics, performance-based wind design, and computational fluid dynamics applications to infrastructure resilience. His recent publications focus on machine learning applications for storm surge prediction, wind-induced structural response analysis, and climate change-informed infrastructure recovery frameworks. Key methodologies include hierarchical deep neural networks, knowledge-enhanced models, and reinforcement learning-based control systems. Contact: tengwu@buffalo.edu | Office: 226 Ketter Hall, Buffalo, NY 14260

• Numerical mathematics
• Technical mathematics
• Analysis
• +6 more

Barbara Kaltenbacher is a Professor at the Institute of Mathematics, University of Klagenfurt. She serves as Deputy Head of the Institute and is actively involved in academic governance through roles in curricular commissions for Information Technology and Mathematics. Her research focuses on nonlinear acoustics , inverse problems , and partial differential equations , particularly in modeling wave propagation and parameter identification. Her work spans theoretical and applied domains, including Acoustic nonlinearity parameter tomography Fractional regularization techniques Optimization of imaging and ultrasound models Well-posedness of nonlinear PDEs Her recent publications address advanced mathematical challenges in nonlinear acoustics and inverse problems, with applications in medical imaging and materials science. She contributes to academic leadership through committee memberships and maintains active research collaborations across disciplines.

• Concrete Materials Science
• Structural Engineering
• Computational Modeling
• +4 more

Dr. Tathagata Bhaduri is a Lecturer in the Department of Civil and Environmental Engineering at Rensselaer Polytechnic Institute (RPI). He holds a Ph.D. in Civil Engineering from RPI (2021), an M.Tech. from the Indian Institute of Technology, Kanpur (2016), and a B.E. from the Indian Institute of Engineering, Science and Technology, Shibpur (2014). His research focuses on computational modeling of ultra-high-performance concrete (UHPC), particularly multi-scale techniques, fiber orientation effects, and failure modes in UHPC structures. He also explores smart systems for structural vibration control using bracing and fluid dampers. Dr. Bhaduri teaches core courses including Strength of Materials and Engineering Economics, and served as the course coordinator for Strength of Materials in Fall 2023. His professional involvement includes contributions to the American Concrete Institute (ACI) 239C committee, aiding in UHPC design guide development. His publications emphasize UHPC's mechanical properties, multi-scale modeling, and fiber-reinforced material behavior. Research trends highlight interdisciplinary approaches combining experimental and computational methods to address structural challenges in advanced materials.

Muhammad R. HajjView profile

Professor

• Nonlinear Dynamics
• Fluid Mechanics
• Structural Dynamics
• +12 more

Muhammad R. Hajj is the George Meade Bond Professor, Chair of the Department of Civil, Environmental and Ocean Engineering, and Director of the Davidson Laboratory at Stevens Institute of Technology. With a distinguished career spanning over three decades, his expertise lies in nonlinear dynamics , fluid-structure interactions , and energy harvesting , applying these to ship hydrodynamics , biomimetic flight/underwater vehicles , and coastal resilience . He has mentored 32 PhD students and authored over 170 journal publications. Education PhD (1990), MS (1985), Civil Engineering , University of Texas at Austin BE (1983), Civil Engineering (with Distinction) , American University of Beirut Research Interests Dr. Hajj’s work bridges nonlinear dynamics and fluid mechanics to address challenges in structural/aeroelastic stability , bio-inspired design , and renewable energy systems . His fluid-structure interaction studies focus on ship hydrodynamics , transonic flutter , and storm surge prediction , while his energy harvesting research explores piezoelectric systems , self-powered sensors , and biomimetic energy conversion . Recent Trends in Publications His 2022-2021 publications emphasize coastal extreme weather resilience via AI-driven storm surge modeling , bio-inspired robotic fish for underwater energy harvesting , and nonlinear aeroelastic systems to enhance renewable energy extraction . Themes include high-efficiency piezoelectric designs , vortex-induced vibration control , and ultrasonic contactless power transfer , reflecting his commitment to integrating nonlinear dynamics with practical engineering applications . Scientific Honors Fellow, Engineering Mechanics Institute, ASCE Distinguished Civil Engineering Alumni (AUB, 2019) Dean’s Award for Excellence in Research (VT, 2016) Distinguished Leader in Research (VT, 2016) Excellence in Research Award (VT, 2015) Advising and Grants Dr. Hajj has supervised 32 PhD students , many of whom hold prestigious roles in academia and industry. He has secured major grants, including $1.8M from the Department of Energy for floating wave energy converters , $4.94M from the Port Authority for storm surge forecasting , and $200K from the NSF for bio-inspired telemetry energy harvesting .

Gaofeng JiaView profile

Associate Professor

• Structural Engineering
• Risk Assessment
• Infrastructure Resilience
• +4 more

Gaofeng Jia is Associate Professor in Civil and Environmental Engineering at Colorado State University's Walter Scott, Jr. College of Engineering. He specializes in natural hazard risk assessment, infrastructure resilience, and uncertainty quantification. Research integrates simulation-based approaches with high-performance computing for assessing complex engineering systems. Core areas include infrastructure deterioration modeling, wave energy converter optimization, tsunami evacuation planning, and surrogate modeling techniques. Recent work emphasizes physics-informed machine learning for engineering applications. Publications demonstrate consistent methodological innovation in uncertainty quantification across domains including seismic engineering, coastal hazards, renewable energy systems, and structural reliability. Articles employ advanced computational statistics, Bayesian methods, and multi-fidelity modeling. Young Researcher Best Paper Award Best Student Paper Award

Rebecca DziedzicView profile

Assistant Professor

• Asset Management
• Water System Sustainability
• Infrastructure Resilience
• +5 more

Rebecca Dziedzic is an Assistant Professor in the Department of Building, Civil, and Environmental Engineering at Concordia University. Her research focuses on asset management, water system sustainability, and infrastructure resilience. She holds a PhD in Civil and Environmental Engineering from the University of Toronto. Dr. Dziedzic's work integrates machine learning, data science, and policy analysis to address challenges in urban infrastructure systems. Her research explores topics such as water distribution network optimization, climate change adaptation in infrastructure, and circular economy strategies for construction. Recent projects include predicting water main breaks using multivariate models, developing frameworks for energy-efficient pump operation, and assessing carbon footprints in industrial facilities. Dr. Dziedzic supervises graduate students in Civil Engineering (MASc/PhD) and maintains an active research group through the UrbanLinks initiative. Her work has been published in over 30 peer-reviewed articles, with a strong focus on smart city technologies, disaster risk reduction, and sustainable infrastructure design.

Hao LouView profile

Assistant Professor

• Pharmaceutical Formulations
• Computational Modeling
• Molecular Dynamics
• +5 more

Dr. Hao Lou is an Assistant Professor in the Department of Pharmaceutical Chemistry at the University of Kansas School of Pharmacy. His research integrates computational modeling and experimental approaches to advance drug formulation design, with emphasis on subcutaneous delivery systems for biologics and oral formulations. Research focuses on: Computational pharmaceutics using machine learning and molecular dynamics Subcutaneous delivery of monoclonal antibodies and protein therapeutics In vitro emulation of subcutaneous absorption (ESCAR system) Formulation optimization for cyclic peptides and biologics Protein stabilization and drying techniques Recent publications demonstrate novel applications of molecular dynamics simulations in predicting peptide properties, optimizing high-concentration protein formulations, and developing biorelevant dissolution methods. Work appears in pharmaceutical sciences journals covering formulation design and computational modeling. Developed innovative platforms including an Emulator of Subcutaneous Absorption and Release (ESCAR) and protein-hyaluronic acid precipitation techniques. Contributes to advancing dry powder processing for inhaled formulations.

• Optimization and Optimal Control
• Partial Differential Equations
• Inverse Problems
• +9 more

Prof. Karl Kunisch is the Scientific Director at RICAM (Johann Radon Institute for Computational and Applied Mathematics) and a Full Professor of Mathematics at the University of Graz, Austria. He has held academic positions worldwide, including visiting roles at Brown University, INRIA, and Technical University Berlin. His research focuses on Optimization and Optimal Control, Partial Differential Equations (PDEs), Inverse Problems, and their applications in mathematical imaging, medicine, and computational science. Education: 1975: Diploma Degree, Technical University of Graz, Austria 1975: Master Degree, Northwestern University, Evanston, Illinois, USA 1978: Ph.D. Degree, Technical University of Graz 1980: Habilitation, Technical University of Graz Research Interests: Prof. Kunisch’s work spans theoretical and applied aspects of optimal control, including stabilization of PDEs, infinite horizon control problems, and feedback design. He explores numerical methods for PDE-constrained optimization and their applications in medical imaging, cardiac electrophysiology, and machine learning. His projects also address shape optimization and mathematical models for fluid dynamics and quantum systems. Publications Trends: His recent articles emphasize feedback stabilization for nonlinear systems, sparse control approaches, and the intersection of optimal control with machine learning. Key themes include robust algorithms for uncertainty handling, efficient numerical methods for high-dimensional problems, and applications in biomedical engineering. Awards: Pro Scientia-Scholarship (1974–1977) Research Award of Theodor-Körner-Fonds (1979) Fulbright Travel Scholarship (1979/80, 1985) Max Kade Scholarship (1982–83) Japanese Society for the Promotion of Science Fellowship (1990) Christian Doppler Laboratory Fellowship (1992) Advising & Grants: Prof. Kunisch leads the Optimization and Optimal Control research group at RICAM and has directed projects on mathematical data science and inverse problems. His work involves collaborations with institutions globally and has been supported by grants from NASA, the European Union, and national funding bodies. He has advised numerous researchers, though specific student names are not listed here. Labs/Teams: Group Leader of the Group "Optimization and Optimal Control" at RICAM since 2004, contributing to interdisciplinary research in computational mathematics and its applications.

• Scientific Computing
• GPU Computing
• Signal Processing
• +5 more

Professor Wes Armour is a Professor of Scientific Computing at the University of Oxford and serves as the Associate Head of Department for Research in the Department of Engineering Science. He previously directed the Oxford e-Research Centre, an interdisciplinary research center within the Engineering Science Department. With over £31 million secured as PI or Co-I, his work spans supercomputing, signal processing, machine learning, computational fluid dynamics, and protein crystallography. Professor Armour's research focuses on extracting science from data through fundamental challenges in modeling, simulation, and data processing. His work draws from numerical analysis, signal processing, and machine learning to develop technologies enabling future scientific discoveries, particularly for the Square Kilometre Array (SKA) telescope. Key interests include GPU computing, high performance computing, and machine learning applications across diverse domains from radio astronomy to finance. As Director and Principal Investigator of JADE and JADE2, a 700-GPU machine, he established the UK's first national High Performance Computer facility dedicated to advancing Artificial Intelligence and Machine Learning. His research group has pioneered GPU applications across multiple fields, including Square Kilometre Array data processing, protein crystallography, and graphene simulations. Current projects span energy-efficient machine learning, stock price prediction in finance, prime number prediction in cryptography, and multi-modal CCTV data analysis. Professor Armour has been instrumental in developing real-time signal processing techniques for astronomical observations, including the ARTEMIS system for millisecond radio transient detection. His publications demonstrate consistent innovation in GPU-accelerated computing dating back to early work in 2008 on accelerating conjugate gradient routines for electron transport in graphene.

• Thermal Propulsion Systems
• Air Quality
• Emission Reduction Technologies
• +3 more

Felix Leach is an Associate Professor of Engineering Science and Shell-Pocock Fellow at Keble College, University of Oxford. He holds a DPhil from Oxford (Oxon), is a Chartered Engineer (MIMechE), and a Fellow of the Higher Education Academy. His research focuses on thermal propulsion systems and air quality, with projects like Ammospray (green-ammonia propulsion) and OxAria (air pollution monitoring in Oxford). He collaborates with Jaguar Land Rover, Siemens, and local governments on emissions reduction and policy. Education: DPhil (Oxford), MEng. Awards include the 2021 ASME ICED Most Valuable Technical Paper Award and multiple SAE recognitions. He co-authored the prize-winning book *Racing Toward Zero: The Untold Story of Driving Green*. Research interests span engine efficiency, alternative fuels (hydrogen, ammonia), and public health impacts of emissions. He leads interdisciplinary projects combining experimental and computational methods, such as machine learning for flow field analysis and sensor networks for urban pollution monitoring. Grants include EPSRC, NIHR, and NERC funding. He advises on policy for Oxford City Council and serves as associate editor for the ASME Journal of Engineering for Gas Turbines and Power. His work bridges academic research, industry collaboration, and public engagement to address climate and air quality challenges.

Fikile R. BrushettView profile

Professor

• Electrochemical Energy Storage
• Redox Flow Batteries
• Electrochemistry
• +3 more

Fikile R. Brushett is a Professor and holds the Chevron Chair of Chemical Engineering at the Massachusetts Institute of Technology (MIT), within the Department of Chemical Engineering under the School of Engineering. His research focuses on electrochemical energy storage systems, particularly redox flow batteries, catalyst synthesis, and environmental applications like CO₂ capture. Brushett has received numerous accolades, including the Allan P. Colburn Award and Charles W. Tobias Young Investigator Award for his impactful publications. His work integrates experimental and computational methods to advance energy technologies, emphasizing sustainable materials and system optimization. He leads projects addressing global energy challenges through innovations in battery design, electrolyte development, and process modeling. Education: Ph.D. in Chemical Engineering, University of Illinois at Urbana-Champaign (2010) M.S.E. in Chemical Engineering, University of Illinois at Urbana-Champaign (2009) B.S.E. in Chemical Engineering, University of Pennsylvania (2006) Research: Brushett’s lab explores electrochemical energy conversion/storage, microfluidics, and interfacial phenomena. Key areas include redox flow battery optimization, scalable carbon materials for energy storage, and CO₂ capture via electrochemical methods. His team develops novel diagnostic tools (e.g., microelectrode sensors) and models for system efficiency. Awards: Over two dozen honors, including named chairs and fellowships from organizations like ACS and GEM. Notable recognitions include the 2024 Chevron Chair and 2022 AIChE Colburn Award. Grants & Labs: Active in MIT’s Electrochemical Energy Lab, focusing on grid-scale storage solutions. Collaborates on projects funded by DOE, industry partners (e.g., Chevron), and foundations. Leads initiatives in material sustainability and battery lifecycle analysis. Publications: Over 140 peer-reviewed articles (2022–2025) emphasize interdisciplinary innovations in energy storage, electrolyte design, and environmental electrochemistry. Recent work highlights advancements in non-aqueous systems, CO₂ capture, and Bayesian-optimized material synthesis.

Peter L. OlsonView profile

Research Professor

• Geophysics
• Core Dynamics
• Geomagnetism
• +2 more

Dr. Peter L. Olson is a Research Professor at Johns Hopkins University's Department of Earth and Planetary Sciences. His research examines Earth's deep interior dynamics including core-mantle interactions, geomagnetic field generation, and planetary evolution processes. Research Focus: Combines theoretical models, numerical simulations, and laboratory experiments to study core dynamics, geodynamo processes, and mantle convection. Current investigations include the slow carbon cycle and polar ice shelf dynamics through interdisciplinary collaborations. Publications: Recent work explores geomagnetic reversal mechanisms, core-mantle boundary interactions, and planetary dynamo diversity using advanced computational models and fluid dynamics experiments. Education: Ph.D. from University of California, Berkeley

Alan KaptanogluView profile

Professor

• Applied Mathematics
• Scientific Machine Learning
• Nuclear Fusion
• +3 more

Alan Kaptanoglu is a Professor leading the Plasma Physics Group at New York University's Courant Institute. His research focuses on the intersection of applied mathematics, scientific machine learning, and nuclear fusion. He develops theoretical and computational tools for plasma modeling, control, and optimization in complex dynamical systems. Key areas include stellarator design optimization, machine learning-driven MHD simulations, and reactor-scale plasma confinement solutions. His work advances fusion energy research through innovations in magnetic field shaping, coil optimization, and data-driven modeling of plasma behavior. Research interests span plasma turbulence analysis, magnetohydrodynamics, and the application of physics-informed neural networks (PINNs). He explores how magnetic geometry influences plasma stability and turbulence using machine learning techniques. His team addresses challenges in fusion reactor design, such as minimizing Lorentz forces in electromagnetic coils and optimizing permanent magnet configurations for stellarators. Recent work emphasizes data-driven methods for discovering interpretable models in fluids and plasmas, including reduced-order modeling and sparse regression approaches. He collaborates on projects like the DIII-D tokamak and ITER, advancing fusion energy solutions through interdisciplinary computational and experimental efforts. His group's contributions bridge plasma physics with advanced mathematics and machine learning to tackle grand challenges in energy and astrophysical systems. Advising and grants are not explicitly detailed in the provided texts, but his leadership role suggests active mentorship in graduate research. The NYU Plasma Physics Group serves as a hub for cutting-edge research, hosting internships, summer schools, and collaborations with industry/academic partners.

Philbert TsaiView profile

Academic

• Quantitative Biology
• Neurovascular Imaging
• Biomedical Engineering
• +3 more

Philbert Tsai is an Associate Teaching Professor in the Department of Physics at the University of California, San Diego (UCSD). He has held roles as QBio Lab Coordinator/Project Scientist (2015–Present) and Associate Project Scientist (2011–2015), overseeing advanced laboratory setups and bio-imaging research projects. His work focuses on neurovascular systems, microscopy techniques, and cortical blood flow dynamics. Education: Ph.D., Physics, UC San Diego, 2004 Research Interests: Quantitative analysis of cortical microvascular networks Development of ultra-high-resolution imaging systems (e.g., STED, two-photon microscopy) Neurovascular coupling mechanisms and their impact on brain oxygen supply Biomedical engineering applications in neuroscience research Lab & Projects: QBio Lab: Advanced instrumentation including confocal microscopes, 3D printers, and wet-lab equipment Developed vectorized models of mouse brain vasculature and ultra-wide-field multiphoton imaging systems Grants & Awards: No specific awards listed in provided text Collaborations: Worked extensively with colleagues like Dr. David Kleinfeld and Dr. Berislav Zlokovic on neurovascular projects.

Joachim ReuderView profile

Professor

• Meteorology
• Atmospheric Boundary Layer
• Wind Energy Meteorology
• +3 more

Joachim Reuder is a Professor and Research Group Leader at the Geophysical Institute, University of Bergen, affiliated with the Bjerknes Centre for Climate Research. His research focuses on turbulence in the atmospheric boundary layer, wind energy meteorology, and the application of drones for atmospheric measurements. He leads the Meteorology research group and collaborates with the Bergen Offshore Wind Centre. His work combines field observations with advanced simulations, emphasizing stable boundary layers, wind turbine wake dynamics, and lidar technology validation. Key projects include the ISOBAR Arctic field campaigns, COTUR offshore turbulence studies, and the SAMURAI-S drone-based turbulence investigation. Publications highlight contributions to lidar data analysis, boundary layer modeling, and wind energy applications. He has organized conferences and contributed to editorial roles, underscoring his influence in atmospheric science and meteorology.