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Huang ChenView profile

Associate Professor

• Computational Materials Science
• Scientific Computing
• Density Functional Theory
• +3 more

Chen Huang is an Associate Professor in the Department of Scientific Computing at Florida State University (FSU), within the College of Arts and Sciences. His research focuses on computational materials science, particularly developing multiphysics methods for high-accuracy materials simulations and advancing orbital-free density functional theory (DFT) for large-scale studies of functional materials like metal alloys and lithium battery components. Huang holds a Ph.D. in Physics from Princeton University and a B.Sc. in Physics from Tsinghua University, China. Education Ph.D. in Physics, Princeton University B.Sc. in Physics, Tsinghua University Research Interests Development of novel multiphysics methods for oxide interface electronic structure predictions Orbital-free DFT advancements for scalable simulations of functional materials Electronic structure calculations and materials modeling Publications Trends Huang's recent work emphasizes improving DFT methodologies through embedded cluster approximations, analytical force calculations, and optimized effective potentials. His studies address challenges in simulating metals, catalytic reactions, and optoelectronic materials, with applications in energy storage and quantum chemistry. Key contributions include enhancing computational efficiency for large-scale systems and refining electron correlation treatments. Awards No specific scientific awards mentioned in the provided materials. Advising & Grants Current advisee: Mani Tyagi (Ph.D. student since 2023) Past students: Yu-Chieh Chi (UCSB Research Computing Consultant), Maliheh Shaban Tameh (ASU Postdoc) Former postdoc: Albert Dearden (2014-2015) Labs & Teams Huang leads the HUANG GROUP , focusing on computational materials science and DFT methodological advancements through the PROFESS software suite. The group collaborates on embedded cluster density approximations and high-accuracy electronic structure simulations.

Theo SiegristView profile

Professor

• Organic Semiconductors
• Structural Analysis of Nanoscale Materials
• Structure-Property Relationships in Crystalline Materials
• +5 more

Theo Siegrist is a Professor of Chemical & Biomedical Engineering and Materials Science & Engineering at the FAMU-FSU College of Engineering. He holds a joint appointment with the National High Magnetic Field Laboratory (Condensed Matter Science) and serves on the Department Graduate Committee. His research focuses on structural analysis of nanoscale materials, crystal growth, and advanced material characterization using techniques like X-ray diffraction. He earned his Ph.D. in Solid State Physics from ETH Zurich and has held roles at Bell Laboratories and Lund University. Research Interests: Organic semiconductors Crystal growth mechanisms Structure-property relationships in crystalline materials Advanced magnetic materials Key Achievements: Fellow of the American Physical Society (2006) Humboldt Research Fellow (2008) Over 150 peer-reviewed publications Current Projects: Exploring magnetophoresis dynamics in porous media, optimizing thermoelectric properties of heavy-fermion compounds, and developing novel perovskite-based optoelectronic materials.

Alessandro RealiView profile

Professor

• Computational Mechanics
• Advanced Materials
• Isogeometric Analysis
• +7 more

Alessandro Reali is a Professor of Solid and Structural Mechanics at the Department of Civil Engineering and Architecture (DICAr), University of Pavia. He also holds roles as Dean of DICAr, Rector's Delegate for International Research, and affiliate at IAS-TUM (Technical University of Munich) and IMATI-CNR. His research focuses on computational mechanics, advanced materials, isogeometric analysis, and biomechanics, with applications in structural engineering, additive manufacturing, and biomedical modeling. Key achievements include: Elected Fellow of the European Academy of Sciences (2023) Recipient of the 2020 Euler Medal (ECCOMAS) 2010 ERC Starting Grant for ISOBIO project 2018 and 2016 Highly Cited Researcher (Clarivate) Research interests include phase-field modeling, fracture mechanics, and patient-specific forecasting of prostate cancer growth using imaging-informed biomechanical models. He leads the Computational Mechanics & Advanced Materials Group at the University of Pavia and has developed open-source tools like GeoPDEs for isogeometric analysis. Grants and collaborations span European and international institutions, emphasizing interdisciplinary applications in engineering, medicine, and environmental sustainability. His work bridges computational methods with real-world challenges in healthcare, manufacturing, and structural safety.

• Fluid Dynamics
• CFD (Computational Fluid Dynamics)
• Aerodynamics
• +7 more

Dr. Khalid Saleh is a Senior Lecturer in Mechanical Engineering at the School of Engineering, University of Southern Queensland. His research focuses on fluid dynamics, icing phenomena in aircraft engines, computational fluid dynamics (CFD), and thermal engineering. He holds a PhD from USQ (2013) and an MSc from Al-Nahrain University (2002). Dr. Saleh teaches courses in heat transfer, fluid mechanics, and computational methods. He is affiliated with the Institute for Advanced Engineering and Space Sciences (IAESS) and has conducted pioneering work on ice crystal icing wind tunnels and supersonic flow analysis using advanced diagnostics like TDLAS. His research interests span multiphase flows, nanofluids, and renewable energy systems, with applications in aerospace and industrial engineering. He has supervised over 10 doctoral students in areas including ejector performance optimization and hybrid nanomaterials. His recent publications emphasize machine learning integration in fluid dynamics and hydrogen fuel utilization in engines. Research affiliations: Institute for Advanced Engineering and Space Sciences (IAESS) Key research tools: CFD simulations, experimental wind tunnels, and optical diagnostics (TDLAS) Key projects: Variable geometry ejector design, ice accretion monitoring systems, and eco-hybrid composite materials Dr. Saleh's work bridges theoretical fluid mechanics with practical engineering solutions, contributing to aerospace safety and sustainable energy technologies.

Alice KonigesView profile

Researcher

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

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

Liam StantonView profile

Associate Professor

• Mathematical Modeling in Physics and Biology
• Multiscale and Multiphysics Methods
• Non-equilibrium Many-Body Systems
• +3 more

Liam Stanton is an Associate Professor of Applied Mathematics at San Jose State University (SJSU), part of the College of Science. He holds a Ph.D. in Applied Mathematics from Northwestern University (2009), an M.S. from the same institution (2005), and a B.S. in Physics & Mathematics from Boston College (2004). His research focuses on multiscale modeling of non-equilibrium many-body systems, with applications in physics and biology. Key areas include mathematical modeling of plasma dynamics, transport phenomena, and stochastic approaches in biological systems. Stanton’s work emphasizes analytical and computational methods, including molecular dynamics, density functional theory, and continuum mechanics. His recent studies explore plasma transport in high-energy-density systems, inertial confinement fusion, and biophysical modeling of cellular membranes. He has developed novel algorithms for hydrodynamic density functional theory and machine learning-driven surrogate models to bridge scales in complex systems. His publications span plasma physics, computational methods, and interdisciplinary applications, reflecting a commitment to advancing both fundamental theory and practical simulation techniques. Stanton collaborates widely, contributing to projects like the Cimarron Initiative for large-scale plasma simulations. He is actively involved in teaching and mentoring, though no student names are publicly listed. Office hours are held Monday-Thursday 9am–12:30pm/1:30pm–4pm and Friday 9am–12:30pm.

Feruza AmirkulovaView profile

Assistant Professor

• Mechanical Engineering
• Acoustics
• Machine Learning
• +4 more

Dr. Feruza Amirkulova is an Assistant Professor of Mechanical Engineering at San Jose State University, where she has served since 2018. She focuses on integrating hands-on learning and advanced simulations into courses like ME149: Engineering Acoustics and ME231: Machine Learning and Optimization in Mechanical Engineering through tools such as BK Connect and COMSOL MultiPhysics. B.Sc. and M.Sc. in Mathematics (with honors), Samarkand State University Ph.D. in Civil Engineering, Samarkand State University Ph.D. and M.Sc. in Mechanical and Aerospace Engineering, Rutgers University Her research spans acoustics, computational modeling, and optimization, emphasizing industrial applications. She collaborates with Silicon Valley companies like Jabil, Apple, NASA, Toyota, and Airbus, and contributes to interdisciplinary initiatives with SJSU’s College of Engineering and Audiology department. Dr. Amirkulova has chaired special sessions at the Acoustical Society of America and served on NSF grant review panels. Guidry Family Faculty Teaching Fellow

Ha BuiView profile

Professor

• Computational Mechanics
• Constitutive Modelling
• Geomechanics
• +3 more

Professor Ha Bui is the Head of the Department of Civil and Environmental Engineering at Monash University and an ARC Future Fellow. He holds a PhD in Computational Geomechanics from Ritsumeikan University, Japan, and has been with Monash since 2012. His research focuses on computational mechanics, material modeling, and geomechanics, with a particular emphasis on failure mechanisms of geomaterials and developing predictive tools for geo-disasters. He leads the development of GeoXPM, a particle-based computational software for geotechnical applications. Bui has received numerous awards, including the ALERT Research Medal (2021) and the ARC Future Fellowship (2020). He serves as Editor of Computers & Geotechnics and Associate Editor of multiple journals, contributing significantly to the field through editorial and review work. His research spans numerical methods like SPH and DEM, addressing challenges in geohazard prediction, material behavior under extreme conditions, and infrastructure resilience.

Kartik JosyulaView profile

Lecturer

• Computational Mechanics
• Multiphysics Modeling
• Atomistic Simulations
• +4 more

Kartik Josyula is a Lecturer in the Department of Mechanical, Aerospace and Nuclear Engineering at Rensselaer Polytechnic Institute (RPI). He holds a Ph.D. (2018) in Mechanical Engineering from RPI and a B.Tech./M.Tech. (2009) in Mechanical Engineering from IIT Madras, India. His research focuses on computational mechanics, multiphysics modeling, atomistic simulations, and machine learning applied to biological tissues and advanced materials. He is particularly interested in developing experimental protocols for studying thermally damaged skin and phase transformations in energetic materials like RDX. His work bridges computational methodologies with real-world biomedical and materials science applications. Education: Ph.D. in Mechanical Engineering, Rensselaer Polytechnic Institute (2018) B.Tech./M.Tech. in Mechanical Engineering, IIT Madras, India (2009) Research interests span biomechanics of biological tissues, materials science of explosives (e.g., RDX phase transformations), and the integration of machine learning with atomistic simulations. His recent work highlights the limitations of synthetic tissues in burn care simulators and challenges in using porcine skin as a human skin surrogate. Publications (selected): His articles emphasize computational studies of phase transitions in energetic materials and biomechanical analysis of thermal injury effects. Themes include material behavior under extreme conditions, predictive modeling, and translational research for clinical applications. Awards: No awards listed in provided text. Advising/Grants: No formal advisees or grant details provided. His research likely involves collaborative projects in computational mechanics and materials science. Labs/Teams: Active in RPI’s computational mechanics research groups, with potential affiliations to biomedical engineering and materials science initiatives.

Bryony DuPontView profile

Associate Professor

• Design Automation
• Renewable Energy Systems
• Sustainable Design
• +3 more

Bryony DuPont is Associate Professor of Mechanical Engineering at Oregon State University's College of Engineering. Her research develops computational methods for sustainable energy systems design, with joint appointment at the National Renewable Energy Laboratory. Research domains include: Computational design optimization for renewable energy Offshore wind farm configuration Wave energy converter development Environmental impact assessment Sustainable product design methodologies Recent publications focus on wave parameter characterization, community-centered energy transitions, and coupled simulation of floating wind platforms. Article analyses demonstrate strong emphasis on techno-economic modeling and stakeholder engagement frameworks. Professor DuPont teaches engineering design, wind energy systems, and sustainable manufacturing. She leads the Design Engineering Laboratory and manages projects funded by the Department of Energy and National Science Foundation. Her 2019 mentoring award recognizes exceptional graduate student supervision.

Myoungkyu LeeView profile

Assistant Professor

• High-Performance Computing
• Turbulent Flows
• Multiphysics Simulations
• +3 more

Dr. Myoungkyu Lee is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University of Houston (2023–current). Previously, he served as an Assistant Professor at the University of Alabama (2022–2023) and held postdoctoral positions at Sandia National Laboratories (2018–2021) and the Oden Institute of Computational Science and Engineering (2016–2018). His research focuses on high-performance computing, thermal/chemical nonequilibrium flows, and direct numerical simulations of multiphysics turbulent flows. He earned his Ph.D. in Mechanical Engineering from the University of Texas at Austin (2015) and B.S. from Hanyang University (2008). Dr. Lee’s research integrates computational fluid dynamics, turbulence modeling, and high-performance computing to study wall-bounded flows, magnetohydrodynamic effects, and nonequilibrium phenomena. His work emphasizes spectral analysis, data-driven modeling, and advanced numerical methods for resolving complex fluid behaviors. Notable contributions include studies on Reynolds stress scaling, wall-pressure correlations, and stochastic forcing in turbulent flows. His awards include the Early Career Presenter Fellowship (2021) and a Best Student Paper Finalist at SuperComputing SC13 (2013). He has published extensively in journals like Journal of Fluid Mechanics and Physical Review Fluids , with over 30 peer-reviewed articles. His teaching includes courses on fluid dynamics and computational methods. Dr. Lee’s academic trajectory reflects a strong dedication to advancing computational turbulence research and fostering interdisciplinary collaborations in engineering and applied mathematics.

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.

Susan HagnessView profile

Professor

• Bioelectromagnetics
• Computational Electromagnetics
• Microwave Diagnostics
• +4 more

Susan Hagness is the Maria Stuchly Professor of Electrical Engineering at the University of Wisconsin-Madison, affiliated with the College of Engineering's Departments of Electrical & Computer Engineering and Biomedical Engineering. Her research focuses on computational and experimental applied electromagnetics, with emphasis on bioelectromagnetics for medical and agricultural applications. Key areas include microwave diagnostic/therapeutic techniques, dielectric spectroscopy, FDTD theory, and smart-ag sensing technologies. Her work is funded by NIH, NSF, DARPA, and industry partners. Awards include AIMBE and NAI Fellowships, the IEEE Antennas and Propagation Society Distinguished Lecturer title, and the PECASE Presidential Early Career Award. She leads multi-disciplinary teams in Engineering Hall (3415/3559 EH) and collaborates with clinical partners at UW Hospital and agricultural field sites. Recent innovations include TEM cell designs for real-time tissue monitoring during microwave ablation and microwave sensing for cranberry yield estimation. Research Highlights: Microwave breast imaging, thermoacoustic signal analysis, minimally-invasive microwave ablation antennas Key Collaborations: UW Hospital, Wisconsin Cranberry Board, Air Force Office of Scientific Research Teaching: Advises graduate students in ECE 790/890/990 research courses Her lab's work bridges fundamental electromagnetics with translational applications addressing global health and agricultural challenges.

Prashant K. JhaView profile

Assistant Professor

• Fracture Mechanics
• Mechanics of Solids and Granular Media
• Multiphysics and Multiscale Modeling
• +5 more

Prashant K. Jha is an Assistant Professor in the Department of Mechanical Engineering at the South Dakota School of Mines and Technology. Previously, he served as a Mechanical Engineering Lecturer (equivalent to Assistant Professor) at the University of Portsmouth and held postdoctoral positions at Louisiana State University and the Oden Institute at UT Austin. PhD in Civil and Environmental Engineering (2016), Carnegie Mellon University ME in Mechanical Engineering (2012), Indian Institute of Science BE in Mechanical Engineering (2010), Govt. Engineering College Raipur His research focuses on computational mechanics of materials, combining peridynamics , machine learning , and nonlocal modeling to study: Fracture mechanics in soft and granular materials Multiphysics and multiscale simulations Neural operators for scientific computing Uncertainty quantification in material models Design of functional materials under extreme conditions Applications to biomedical imaging and tumor growth Recent publications emphasize neural operator surrogates with error correction, peridynamic modeling of granular media, and multi-fidelity simulations. His work has appeared in journals like CMAME , ARMA , and JCP . NSF ERI award (2025) for multi-fidelity modeling SDBOR CRG award (2025) for magnetic soft materials He advises students in computational mechanics and serves on editorial boards of Journal of Peridynamics and Nonlocal Modeling , JOSS , and Scientific Reports . The CEAD Lab develops methods for fracture analysis, smart material design, and nonlocal neural operator frameworks.

Zeinab El-SayeghView profile

Assistant Professor

• Vehicle System Dynamics
• Autonomous Vehicles
• Ride Comfort
• +5 more

Dr. Zeinab El-Sayegh is an Assistant Professor in the Department of Automotive and Mechatronics Engineering at the University of Ontario Institute of Technology. She is affiliated with the Faculty of Engineering and Applied Science and holds professional engineering licensure (PEng). Her research focuses on vehicle dynamics, autonomous systems, and off-road vehicle design, with a strong emphasis on tire mechanics and soil-terrain interactions. She has been recognized with the 2024 Engineering Research Award in the Early Career Category. Education: PhD (Mechanical Engineering), University of Ontario Institute of Technology (2020) MEng (Aerospace Engineering), Concordia University (2018) MEng (Mechanical Engineering), Concordia University (2015) BEng (Mechanical Engineering), Lebanese International University (2013) Research Interests: Vehicle System Dynamics: Studying vibration and control mechanisms in automotive systems. Autonomous Vehicles: Developing path planning and sensor integration for off-road and multi-wheeled platforms. Tire Mechanics: Analyzing tire-soil interaction using advanced computational tools like FEA and SPH. Soil Dynamics: Modeling terrain effects on vehicle stability and performance in diverse environmental conditions. Modeling & Simulation: Creating scalable models for electric combat vehicles and agricultural tires. Professional Activities & Awards: Co-organizer of the F1tenth Competition at the 2024 IEEE Smart Mobility Conference. Assistant Editor of the International Journal of Heavy Vehicle Systems since 2021. Member of ASME Vehicle Design Executive Committee since 2021. Recipient of the 2024 Engineering Research Award (Early Career). Teaching & Engagement: Dr. El-Sayegh teaches courses such as Automotive Vibrations, Machine Design, Autonomous Vehicles, and Vehicle Dynamics & Control. She actively contributes to interdisciplinary research and outreach through memberships in ASME, SAE, and CSME.