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Su YanView profile

Associate Professor

• computational electromagnetics
• multiphysics modeling
• high-performance computing
• +3 more

Su Yan is an Associate Professor and Director of Graduate Studies in Howard University's Department of Electrical Engineering and Computer Science. He directs the IBM-HBCU Quantum Center and leads research in computational electromagnetics, nonlinear multiphysics modeling, and machine learning-enhanced simulations. His group develops extreme-scale algorithms for electromagnetic systems with applications in RF devices, quantum technologies, and computational imaging. Current projects involve AI-accelerated metasurface design, microwave-assisted hydrogen generation, and randomized multiscale solvers. Honors include DOE/NSF Early Career Awards, ACES Early Career Award, and IEEE prize papers. He currently advises 9 PhD students and has graduated multiple doctoral candidates in computational electromagnetics. Research Grants: DOE Early Career Award for randomized multiscale algorithms NSF CAREER for neural network-enhanced RF device simulation NSF grants for AI-enhanced metasurfaces and hydrogen catalyst design Ansys collaboration on non-conforming solvers

Ilya AvdeevView profile

Professor

• Real-Time Modeling
• Energy Storage Systems
• Design Thinking in Education
• +3 more

Ilya Avdeev is a Professor of Mechanical Engineering at the University of Wisconsin-Milwaukee (UWM) and holds multiple leadership roles including Director of the Lubar Entrepreneurship Center (LEC), Co-Founder/Executive Director of the UWM Student Startup Challenge, and Director of the Advanced Manufacturing and Design Laboratory. He is on sabbatical during the Spring 2025 semester. His research focuses on real-time modeling (Digital Twin), energy storage systems, and design thinking in engineering education. Education: PhD in Mechanical Engineering from the University of Pittsburgh (2003), MS and BS in Mechanical Engineering from St. Petersburg State Technical University, Russia (1999 and 1997). Research interests span advanced manufacturing, battery safety, and interdisciplinary education innovation. His work integrates computational modeling with practical applications in energy systems and biomedical devices. Over 20 years of academic and industry collaboration have resulted in impactful contributions to both technical and entrepreneurial ecosystems. Notable initiatives include the Milwaukee Regional Energy Education Initiative (as PI) and the UWM Student Startup Challenge, which fosters student entrepreneurship. His publications address topics like battery impact analysis, MEMS simulation, and educational pedagogy.

Ming HuView profile

Professor

• Thermal Transport
• Nanotechnology
• Energy Systems
• +8 more

Ming Hu is a Professor in the Department of Mechanical Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina. His research focuses on computational thermal science with applications in energy systems, nanotechnology, and advanced materials engineering. Education: Dr.-Ing. in Mechanics from the Institute of Mechanics, Chinese Academy of Sciences (2006) B.S. in Mechanical Engineering from the University of Science and Technology of China (2001) Dr. Hu's research centers on micro-/nano-scale thermal transport phenomena, particularly in low-dimensional materials and nanostructures. His work spans energy nanotechnology, interfacial heat transfer for thermal management, and multi-scale multiphysics modeling of complex energy systems. He employs advanced computational techniques including molecular dynamics, first-principles simulations, and machine learning to investigate phonon transport mechanisms and thermal properties of novel materials. Analysis of recent publications reveals a strategic shift toward artificial intelligence applications in thermal science. Dr. Hu has pioneered graph neural network architectures for high-throughput prediction of phonon properties and thermal conductivity, with significant focus on materials for wide bandgap semiconductor cooling, thermoelectrics, and battery systems. His current work emphasizes accelerating materials discovery through machine learning while maintaining physical interpretability. Scientific Awards: No major awards were documented in the provided information. Dr. Hu leads the Nano Energy and Engineering Laboratory, which specializes in computational thermal science research. The laboratory develops and applies advanced simulation methodologies to investigate heat transfer in next-generation materials. No information regarding graduate students or research grants was available in the provided materials.

Yi WangView profile

Professor

• Computational Fluid Dynamics
• Microfluidics
• Reduced Order Modeling
• +2 more

Dr. Yi Wang is Professor of Mechanical Engineering at University of South Carolina's College of Engineering and Computing. He directs the Integrated Multiphysics & Systems Engineering Laboratory (iMSEL) researching computational and data-enabled science for multiphysics systems including microfluidics, additive manufacturing, and autonomous systems. Research develops physics-based adaptive modeling, reduced order techniques, and data-driven surrogate models for complex engineering systems. Applications include microfluidic device optimization, aeroelastic reduced order models, robotic path planning, and structural health monitoring. Honors include Breakthrough Stars Award, Research Progress Award, and multiple Dean's Excellence Awards. He serves as Associate Editor for Discover Applied Sciences and editorial board member for multiple journals. Current NSF and DoD-funded projects focus on aeroservoelastic reduced order modeling and autonomous systems perception. Research group includes 5 postdoctoral fellows and 12 graduate students working on multidisciplinary computational projects. Teaching includes Flight Dynamics and Control, Engineering Analysis, and senior design courses.

• Structural Dynamics
• Composite Structure Design
• Experimental Modal Analysis
• +5 more

Bekir Bediz is an Associate Professor at Sabanci University , Faculty of Engineering and Natural Sciences, with a focus on structural dynamics and multi-scale manufacturing processes. He holds a Ph.D. in Mechanical Engineering from Carnegie Mellon University (2014) and earlier degrees from Middle East Technical University (B.S. 2006, M.S. 2009). Education: Ph.D., Mechanical Engineering, Carnegie Mellon University (2014) M.Sc., Mechanical Engineering, Middle East Technical University (2009) B.S., Mechanical Engineering, Middle East Technical University (2006) Academic Positions: Associate Professor, Sabanci University (2023–present) Assistant Professor, Sabanci University (2016–2023) Instructor, Middle East Technical University (2015–2016) Post-doctoral Researcher, Carnegie Mellon University (2015) Research Interests include high-fidelity dynamic modeling of complex structures, variable stiffness composite design, experimental modal analysis, mechanical micromachining, and vibration energy harvesting systems. His work integrates spectral Chebyshev methods with lamination parameters to optimize composite panels and develop novel manufacturing techniques for micro-devices. Recent Publications highlight advancements in: Active magnetic bearing spindle design for micro-milling Manufacturable variable stiffness composite laminates Particle damper integration in additive manufacturing Microneedle array fabrication for localized drug delivery Nonlinear vibration analysis of rotating functionally graded beams Scientific Awards include multiple TUBITAK grants for projects on: Multi-objective optimization of stiffened composite panels (TUBITAK 1001, 2023–2026) Ultra-high-speed spindle design (TUBITAK 1002, 2023–2024) Variable stiffness composite manufacturing (TUBITAK 1005, 2020–2022) Research Group includes Ph.D. and M.Sc. students working on composite optimization, microneedle design, and spindle dynamics. His projects often involve collaborations with institutions like Koc University, Istanbul Technical University, and Hacettepe University.

Yifan WangView profile

Assistant Professor

• Computational Fluid Dynamics
• Fluid-Structure Interaction
• Artificial Intelligence
• +3 more

Dr. Yifan Wang is an Assistant Professor in the Department of Mathematics and Statistics at Texas Tech University. His research focuses on computational fluid dynamics, fluid-structure interaction, artificial intelligence, machine learning, and numerical solutions of PDEs, with applications in biomedical engineering and high-performance computing. His research interests include developing numerical methods such as Galerkin finite elements, spectral element methods, discontinuous Galerkin methods, and smoothed particle hydrodynamics to solve complex fluid-structure interaction problems. Specific applications include cardiovascular modeling, blood flow analysis in stented arteries, microfluidic systems for cancer detection, and bioartificial organ design. Wang's publications demonstrate a strong focus on multiscale modeling in biomedical contexts, including computational studies of drug effects in carcinogenesis, stents, and tumor cell dynamics. Recent work explores machine learning applications in biological data regression, medical diagnostics, and predictive modeling of disease outbreaks. He contributes to cardiovascular engineering through computational analysis of stent geometries in tortuous arteries and hemodynamic modeling of stenotic vessels, with emerging work in geothermal energy systems and multiphysics modeling techniques.

Trevor M. MoellerView profile

Professor

• Plasma Physics
• Electromagnetics
• Rocket Propulsion
• +3 more

Trevor M. Moeller is the Jack D. Whitfield Professor and Associate Professor in the Mechanical, Aerospace, and Biomedical Engineering (MABE) Department at the University of Tennessee Space Institute (UTSI). He holds a Ph.D. (1998), M.S. (1993), and B.S. (1991) in Mechanical Engineering from the University of Tennessee and Rose-Hulman Institute of Technology, respectively. His research focuses on plasma physics, electromagnetics, and high-speed propulsion systems, with particular emphasis on electric propulsion, high-temperature plasmas, and advanced manufacturing for aerospace applications. Moeller has secured over $8M in grants, leading projects on electrospray thrusters, hypersonic flow environments, and space environment simulations. He has authored/co-authored over 70 peer-reviewed articles and one patent, and serves on committees for AIAA and IEEE. Education: B.S., Mechanical Engineering, Rose-Hulman Institute of Technology, 1991 M.S., Mechanical Engineering, University of Tennessee, 1993 Ph.D., Mechanical Engineering, University of Tennessee, 1998 Research Interests: High-temperature plasmas, rocket propulsion, electric propulsion devices, electromagnetic acceleration, and plasma diagnostics. Current projects include developing electrospray thrusters and mitigating cryodeposition in vacuum chambers. Publications reflect contributions to computational fluid dynamics, plasma modeling, and experimental propulsion studies. His lab, the Computational and Experimental Aerospace Research (CEAR) group, utilizes advanced facilities like wind tunnels and vacuum chambers, alongside the TEMPEST simulation software. Moeller advises numerous graduate students and collaborates with agencies like AFRL, AFOSR, and DOD. His work addresses challenges in space environment simulation and propulsion system optimization.

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.

Shikui ChenView profile

Associate Professor

• Topology Optimization
• Additive Manufacturing
• Medical Device Design
• +7 more

Dr. Shikui Chen is an Associate Professor in the Department of Mechanical Engineering at Stony Brook University since 2013. His research focuses on topology optimization, additive manufacturing, and computational design, with applications in medical devices, electric machines, and soft robotics. He holds PhDs from Northwestern University (2010) and the Chinese University of Hong Kong (2006). Prior to academia, he worked at Altair Engineering (2010–2013) on finite element solvers and optimization technology. Education: PhD, Mechanical Engineering, Northwestern University, 2010 PhD, Automation and Computer-Aided Engineering, Chinese University of Hong Kong, 2006 Key research areas include topology optimization for vascular devices, thermal-electrical systems, and metamaterials. He leads the Computational Modeling, Analysis, and Design Optimization (CMADO) lab, which develops computational tools for systematic design innovation and additive manufacturing. His work integrates applied mathematics, computer science, and engineering principles to address complex nonlinear and multiphysics problems. Awards include the ASME Compliant Mechanisms Theory Award (2007). His publications span over 50 peer-reviewed articles, emphasizing robust optimization methods, level set techniques, and interdisciplinary applications. Current projects involve generative design of soft robots, conformal electronics, and energy-efficient thermal systems.

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.

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.

Florian FepponView profile

Assistant Professor

• Topology Optimization
• Optimal Design
• Homogenization
• +6 more

Florian Feppon is an Assistant Professor in the NUMA division of the Department of Computer Science at KU Leuven. His research spans topology optimization, optimal design, homogenization, multiscale modeling, uncertainty quantification, data assimilation, and asymptotic analysis of partial differential equations, with applications in 3D printing, heat exchangers, photonics, and microfluidic devices. He completed his PhD in 2019 at École polytechnique under Grégoire Allaire and Charles Dapogny, funded by Safran, and a Master’s thesis in 2016 at MIT with Pierre Lermusiaux. From 2020 to 2022, he was a Hermann-Weyl postdoctoral instructor at ETH Zürich. His research interests include developing algorithms for nonlinear constrained optimization, such as the Null Space Optimizer , and analyzing subwavelength resonances in acoustic metamaterials, fluid-structure interactions, and homogenization of porous media. Scientific awards : Prix Paul Caseau 2019 (EDF, Institut de France), ECCOMAS PhD award 2019, and SMAI-GAMNI thesis award 2021. Grants : FWO Junior Research Project (2024-2028) on topology optimization of large-scale systems under uncertainty. Students : Javier Bevia-Ripoll and Dries Toebat, working on topology optimization with local and global constraints. Software : Lead developer of the open-source Null Space Optimizer for constrained optimization.

• Multiscale modeling
• Nuclear energy
• Nuclear fusion
• +3 more

Prof. Roberto Bonifetto is an Associate Professor in the Department of Energy at Polytechnic University of Turin. His research focuses on nuclear fusion engineering, multiscale modeling of fusion systems, and superconducting magnet technology for tokamak reactors. He serves as Principal Investigator for the DESIGN project (Cloud Systems for Wireless Energy Transfer) and industrial research collaborations with NewCleo S.p.A. on nuclear safety systems. Prof. Bonifetto teaches courses in computational thermal fluid dynamics, solar thermal technologies, and nuclear engineering fundamentals. His awards include the ENEN PhD Prize and IEEE Graduate Study Fellowship in Applied Superconductivity. Current research projects include thermal-hydraulic analysis for DEMO reactor magnets, DTT facility cryogenic system design, and tritium management systems for breeding blankets. Annual ENEN PhD Prize IEEE Council on Superconductivity Graduate Study Fellowship Prof. Bonifetto leads the NEMO Research Group and supervises five PhD students working on fusion reactor safety, magnet system design, and energy transfer technologies. His work contributes to EUROfusion and ITER international collaborations.

• Clean Energy Technologies
• Chemical Reaction Kinetics
• Fuel Conversion Devices
• +3 more

Stephen Dooley is a Professor in the Department of Physics at Trinity College Dublin, with interdisciplinary expertise spanning chemical engineering, clean energy, and combustion science. His research focuses on chemical reaction kinetics, kinetic modeling, and their applications in fuel conversion technologies. He has held academic roles across departments of Chemistry, Mechanical Engineering, and Environmental Science, reflecting his interdisciplinary approach. Professor Dooley has been awarded prestigious grants including two European Research Council (ERC) A-Ratings (StG 2015 and CoG 2017), and honors such as the Combustion Institute Excellence in Research Award (2020). His work emphasizes sustainable aviation fuels, biomass pyrolysis, CO 2 electrolysis, and reduced kinetic models for combustion systems. Affiliations: School of Physics, Trinity College Dublin; Fellowships in Combustion Science and Environmental Engineering. Research Interests: Development of kinetic models for biofuel synthesis, low-carbon energy systems, and advanced combustion technologies. His recent publications (2024–2018) highlight contributions to sustainable aviation fuels’ lifecycle analysis, hemicellulose pyrolysis mechanisms, and machine-learned kinetic models for methane combustion.

Luca Di RienzoView profile

Associate Professor

• Computational Electromagnetics
• Electrical Engineering
• Magnetic Sensors
• +3 more

Luca Di Rienzo is an Associate Professor at the Department of Electronics, Information, and Bioengineering of Politecnico di Milano, specializing in computational electromagnetics and electromagnetic modeling. He holds a Ph.D. (2001) and M.S. (1996) in Electrical Engineering from Politecnico di Milano. His research focuses on magnetic sensors, inverse problems, and high-order surface impedance boundary conditions, with applications in power systems, biomedical engineering, and material characterization. He has served as Associate Editor-in-Chief of the Applied Computational Electromagnetics Society (ACES) Journal and on editorial boards of several journals and conferences. His teaching includes courses on circuits and electronics for aerospace engineering students. Key contributions include stochastic finite integration techniques, reduced-order modeling for electromagnetic systems, and optimization of sensor arrays for current measurement. Research Highlights: Development of advanced numerical methods (FDTD, BEM, PEEC) for electromagnetic simulations, uncertainty quantification in biomedical and power systems, and sensor array design for non-invasive measurements. Editorial Roles: ACES Journal, Sensing and Imaging, and multiple conference proceedings (COMPUMAG, CEFC, OIPE). Teaching: Circuits and Electronics (Aerospace Engineering BSc). Labs/Teams: Active in interdisciplinary projects involving computational electromagnetics and biomedical engineering at Politecnico di Milano. Collaborations include institutions like Xi’an Jiaotong University and Ilmenau University of Technology.