جست‌وجوی استاد و پژوهشگر

Zhen Liمشاهده پروفایل

استادیار

• Multiscale/Multiphysics modeling
• Soft matter and Smart materials
• Biophysics and Collective dynamics
• +۷ مورد دیگر

Dr. Zhen Li is an Assistant Professor in the Department of Mechanical Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. He joined Clemson in August 2019 after serving as a research associate professor at Brown University and a postdoctoral research associate at University of California, Merced. Education: Ph.D. in Fluid Mechanics, Shanghai University, 2012 MS in Fluid Mechanics, Shanghai University, 2008 BS in Engineering Mechanics, Wuhan University, 2005 Dr. Li's research focuses on multiscale modeling of soft matter, complex fluids, biophysics, and collective dynamics using both bottom-up (coarse-grained molecular modeling) and top-down (from continuum descriptions to fluctuating hydrodynamics) approaches, along with high-performance computing. His work spans mathematical theory for coarse-graining and model reduction, statistical methods and machine-learning approaches applied to multiscale modeling, memory effects in complex fluids, and concurrent coupling of heterogeneous solvers for scale-bridging. Analysis of Dr. Li's recent publications reveals a strong trend toward integrating machine learning with traditional computational methods, particularly neural operators for multiscale problems. His work spans diverse applications from bubble dynamics and blood flow to materials science and bioprinting, demonstrating the versatility of his computational approaches across multiple disciplines in engineering and physics. Awards and Recognition: CECAS Dean's Professor Award (2024) Award of Excellence - Junior Faculty (2021-2022) Best Research Poster Award at SC19 (2019) 2nd Place Award of Best Poster Presentation at DOE/EFRC AIM for Composites meeting (2024) Dr. Li actively mentors PhD students including Miles Lu, Ryan Wan, Haizhou Wen, and Ali Mohammadi, who have published significant research in computational mechanics. His research is supported by multiple grants including an NSF Elements grant as PI for 'SciMem: Enabling High Performance Multi-Scale Simulation on Big Memory Platforms', an NSF CDS&E grant as co-PI for 'HAM3R: Heterogeneous Automated Management of Multiscale Methods and Resources', a DOE/EFRC grant as Thrust lead co-PI for 'AIM for Composites', and a NASA EPSCoR grant as Science-PI. Dr. Li leads the MuthComp (Multiscale theory and Computation) research group, which focuses on developing interfaces between Engineering, Applied Mathematics, Physics-based Machine Learning, and High Performance Scientific Computing. The group has active collaborations with institutions including Idaho National Laboratory, University of Tokyo, and Brown University, and has developed open-source software including USERMESO for GPU-accelerated DPD simulations.

• Numerical Methods
• Computational Engineering
• Multiphysics Simulation Techniques
• +۲ مورد دیگر

Prof. Thomas Weiland is a Full Professor of Computational Electromagnetics at the Technische Universität Darmstadt since 1989. His research focuses on numerical methods, computational engineering, and multiphysics simulation techniques, particularly in accelerator physics and beam dynamics. He holds a Dr.-Ing. from TU Darmstadt and has held postdoctoral and research positions at CERN and TU Darmstadt. His work includes pioneering contributions to electromagnetic field simulations, including advanced finite element methods, discontinuous Galerkin techniques, and boundary element approaches. Education highlights include his Diplom in Electrical Engineering from TU Darmstadt (1975) and a Habilitation in Experimental Physics from the University of Hamburg (1984). His research spans computational electromagnetics, accelerator physics, and numerical methods for electromagnetic field problems. Notable areas of innovation include transparent boundary conditions, eigenmode calculations, and high-performance simulation frameworks for rotating systems and particle accelerators. His publications emphasize advancements in electromagnetic simulation tools, such as the MagPEEC method and Trefftz-discontinuous Galerkin approaches. Collaborative projects include modeling RF photoinjectors for light sources and electrohydrodynamic droplet dynamics. Technical contributions also extend to wake field analysis in particle accelerators and SAR distribution studies in bioelectromagnetics. Research interests further include multiphysics coupling (thermal-electromagnetic effects in surge arresters), stochastic modeling of electromagnetic systems, and field-circuit co-simulation techniques. His work addresses challenges in large-scale eigenvalue problems, adaptive mesh optimization, and high-precision numerical methods for complex geometries.

Santiago Badiaمشاهده پروفایل

استاد

• Computational Science and Engineering
• Numerical Analysis
• Finite Element Methods
• +۳ مورد دیگر

Santiago Badia is a Full Professor of Computational Science and Engineering at Universitat Politècnica de Catalunya (UPC), holding an adjoint researcher position at the International Center for Numerical Methods in Engineering (CIMNE). He leads the Large Scale Scientific Computing (LSSC) group at CIMNE, focusing on finite element methods, numerical analysis, and high-performance computing. His research emphasizes fluid dynamics, multiphysics problems, and scalable solvers for large-scale systems. Previously, he worked at Politecnico di Milano and Sandia National Labs. He developed the FEMPAR software framework, a parallel finite element tool for PDE simulations, achieving landmark scalability (e.g., 60 billion unknowns on 458,672 cores). FEMPAR is recognized in the High-Q Club of European codes. His expertise includes discontinuous Galerkin methods, XFEM, and domain decomposition preconditioners. Research interests span metal additive manufacturing, superconductor devices, and nuclear engineering applications. Awards include FEMPAR's High-Q Club inclusion. He advises PhD and MSc students (e.g., Jesus Bonilla, Eric Neiva, Marc Olm) and has open positions in postdoc/PhD levels. His team includes researchers like Javier Principe and Alberto Martín. Ongoing projects involve advancing parallel algorithms, multiphysics simulations, and software scalability for exascale computing.

Ian Brownمشاهده پروفایل

استاد

• Energy Conversion
• Electric Machines
• Adjustable Speed Drives
• +۶ مورد دیگر

Ian Brown is a Professor of Electrical and Computer Engineering at Illinois Institute of Technology, part of the Armour College of Engineering. He holds a Ph.D. (2009), M.S. (2003), and B.S. (1999) in Electrical and Computer Engineering from the University of Wisconsin-Madison and Swarthmore College. His research focuses on energy conversion, electric machines, and renewable energy systems, with emphasis on sensorless control, machine design optimization, and traction motor development for electric vehicles. He has extensive industry experience as a principal engineer at A.O. Smith, contributing to electric machine and drive technologies. Research interests include adjustable speed drives, high-power density motors, and applications in sustainable energy. He has advised multiple graduate students and published over 50 peer-reviewed articles in IEEE Transactions and conferences. His recent work explores superconducting circuit breakers, thermal management systems, and advanced winding designs to minimize harmonic distortions. Brown's contributions bridge academic research with industrial applications, particularly in improving energy efficiency and reliability in power conversion systems. He is affiliated with the IEEE and has contributed to journal editorials on electric machines in renewable energy. His lab focuses on experimental prototyping and simulation-driven optimization of electric drives. Current projects include developing brushless capacitive excitation systems for traction motors and analyzing driving cycle-based machine design optimization strategies. Teaching responsibilities include graduate courses on electric machines and power electronics. He maintains active collaborations with industry partners like A.O. Smith and Siemens, emphasizing translational research with commercialization potential.

Carmel Majidiمشاهده پروفایل

استاد

• Soft Robotics
• Wearable Robotics
• Soft Electronics
• +۱۲ مورد دیگر

Carmel Majidi is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering. He leads the Soft Machines Lab, where his research focuses on soft robotics, wearable computing, and advanced materials that mimic biological systems. His work enables robots and machines to safely interact with humans through soft, deformable technologies. Education: Ph.D. in EECS, University of California, Berkeley (2007) B.S. in Civil and Environmental Engineering, Cornell University (2001) His research interests span soft robotics, stretchable electronics, liquid metal materials, and bio-inspired engineering. He develops soft electromechanical materials for applications in haptics, medical devices, and human-machine interfaces. His group pioneers fabrication methods for soft composites and microfluidic systems that function as artificial skin, nervous tissue, and muscle. The recent trend in his publications highlights a strong focus on soft actuators, self-healing materials, liquid metal composites, and haptic interfaces for virtual and augmented reality. His work integrates materials science, robotics, and advanced manufacturing to enable scalable, robust, and practical soft systems. Scientific Awards: 2023 Inno Fire Award for Trailblazing Innovators, Pittsburgh Business Times Majidi leads a major research thrust in a new NSF Engineering Research Center (ERC) dedicated to improving robot dexterity, funded up to $52 million. He has been instrumental in developing soft robotic systems inspired by prehistoric organisms, such as the pleurocystitid, and in creating healthcare devices powered by body heat using liquid metal technologies. His research has been featured in Popular Science , Communications of the ACM , and Ars Technica . He is actively involved in advancing scalable manufacturing of soft electronics and enabling robust integration between soft materials and conventional microelectronics. His lab collaborates across disciplines to push the boundaries of physical AI and wearable robotics.

Sorin Mitranمشاهده پروفایل

استاد

• Computational simulation of multiscale, multiphysics systems
• Data-driven constitutive relations for hyperelastic materials
• Nonlinear mode reduction
• +۴ مورد دیگر

Sorin Mitran is a Professor in the Department of Mathematics at the University of North Carolina at Chapel Hill. His research focuses on computational simulation of multiscale and multiphysics systems, data-driven constitutive relations for hyperelastic materials, and information geometry for reduced stochastic models. PhD in Aerospace Engineering from Politehnica University Bucharest (1995) Professional background includes fellowships at University of Tokyo (1993), Karlsruhe Institute of Technology (1998-1999), and University of Washington (1999-2002) His research develops numerical tools to predict macro-scale behavior from micro-scale interactions, such as plastic deformation of metals from lattice defect dynamics, microtubule mechanics from molecular dynamics, and protein folding from atomic-level simulations. Mathematical approaches include adaptive computation, machine learning for constitutive law prediction, and information geometry for stochastic process analysis. Recent publications (2023-2018) span computational biology, multiscale fluid dynamics, and medical applications of continuum mechanics. Articles frequently explore data-driven modeling, wave propagation in biological systems, and GPU-accelerated numerical methods like Lattice Boltzmann and Lattice Fokker-Planck formulations.

• Mechanical Engineering
• Tribology
• Multimodal Sensing
• +۵ مورد دیگر

Dr. Min Yu is an Imperial College Research Fellow (ICRF) in the Department of Mechanical Engineering at Imperial College London . He leads an independent research program focused on in-situ multimodal sensing of mechanical interfaces , integrating advanced materials, intelligent control, multiphysics modeling, and data-driven technologies. His work bridges tribology, robotics, and sensing with applications in lubrication systems and robotic haptic interfaces. Education: PhD in Mechanical Engineering, Imperial College London (2014–2018) MSc in Engineering, Zhejiang University (2011–2014) BEng in Engineering, Xi’an Jiaotong University (2007–2011) Research Interests: Dr. Yu’s core research areas include tribology , ultrasonic sensing , robotic haptics , lubrication systems , and data-driven control . He develops novel sensing technologies for real-time monitoring of mechanical interfaces, with applications in engines, bearings, transmissions, and robotic systems. His work emphasizes closed-loop intelligent lubrication and bio-inspired robotic sensing . Publications & Trends: Dr. Yu has authored over 60 peer-reviewed papers and holds 6 patents . His recent work (2024–2025) focuses on ultrasonic-based oil film measurement, triboelectric sensors for robotics, and advanced control systems for automotive suspensions. These publications reflect a strong interdisciplinary approach combining mechanical engineering , AI-driven control , and sensor innovation . Awards & Grants: Imperial College Research Fellowship (ICRF 2022–2026) Royal Society International Exchanges – Cost Share Scheme State Key Laboratory of Fluid Power and Mechatronic Systems Open Foundation Taiho Kogyo Tribology Research Foundation Grant Dame Julia Higgins Engineering Postdoc Collaborative Research Fund (2019) Peter Jost Travel Fund (2022) Collaborations & Labs: Dr. Yu collaborates with multiple groups at Imperial College London including the Tribology Group , Non-Destructive Evaluation (NDE) Group , Control and Power Group , Optical & Semiconductor Devices Group , and Geotechnics Group . He also partners with international institutions such as Georgia Tech , Xi’an Jiaotong University , Zhejiang University , HUST , and Tsinghua University , as well as industry leaders like Shell , ExxonMobil , Toyota , and Jaguar Land Rover .

John Mooreمشاهده پروفایل

استادیار

• Computational mechanics
• Materials science
• Engineering design
• +۴ مورد دیگر

Dr. John Moore is an Assistant Professor in the Department of Mechanical Engineering at Marquette University . He leads the Computational Mechanics of Materials Laboratory, focusing on computational mechanics, materials science, and high-performance computing. His research spans alloys, polymers, and biomedical devices. Education Ph.D., Mechanical Engineering, Northwestern University (2015) M.S.E., Civil Engineering, University of Washington (2007) B.S., Aeronautical and Astronautical Engineering, University of Washington (2005) Research Focus Dr. Moore's work combines computational modeling with experimental validation to understand material behavior under extreme conditions. Key areas include: Crystal plasticity modeling of metallic alloys Nonlocal damage mechanics for fatigue prediction Dynamic spallation and porosity evolution High-throughput X-ray imaging of additively manufactured materials UV-sensitive resin material modeling Recent Publications His recent work focuses on advanced computational techniques for material failure analysis, including: Nonlocal approaches for statistical fatigue prediction Microinertia effects in spall modeling Betatron X-ray tomography applications UV resin optimization studies Phase transformation fatigue mechanisms Contact Email: john.a.moore@marquette.edu | Phone: (414) 288-6641 Location: Haggerty Hall, 225, Marquette University, Milwaukee, WI 53201

• Magnetic Recording
• Resistive Switching
• Phase Change Materials
• +۳ مورد دیگر

James A. Bain is a Professor in the Electrical and Computer Engineering (ECE) Department at Carnegie Mellon University’s College of Engineering. He also holds a courtesy appointment in the Department of Materials Science and Engineering and serves as Associate Director of the Data Storage Systems Center (DSSC). Bain’s research focuses on magnetic, optical, and resistive switching devices for information storage, including heat-assisted magnetic recording (HAMR) and phase change materials. Education : B.S. (1988) in Materials Science and Engineering from the University of Pennsylvania; M.S. (1991) and Ph.D. (1993) in Materials Science and Engineering from Stanford University. His work spans nanotechnology, energy-efficient computing, RF metrology, and multiphysics modeling. Bain’s research has received recognition through the Scott Institute seed grant for energy research. He has co-authored over 225 papers and holds a leadership role as ECE’s Associate Department Head for Academic Affairs since 2018. His laboratory investigates thermal transport, device reliability, and emerging memory architectures. Recent publications highlight advancements in HAMR thermal management, resistive switching dynamics, and phase-change RF switches. Bain’s expertise intersects with the IEEE Magnetics, Electron Devices, and Photonics Societies, as well as the Materials Research Society.

Piotr Kulikمشاهده پروفایل

استادیار

• Electrical Engineering
• Materials Science
• Nanofabrication of thin-film materials
• +۲ مورد دیگر

Piotr Kulik is an Assistant Professor at the University of Central Florida (UCF) in the Department of Electrical and Computer Engineering within the College of Engineering and Computer Science. His research focuses on nanofabrication of thin-film materials, microsystems for sensing and communication, and multiphysics devices. Ph.D. in Electrical Engineering, Northeastern University (2020) Dr. Kulik’s research bridges advanced materials synthesis with microwave and MEMS device design. His work on magnetodielectric composites and ferrite thin films enables next-generation RF components for wireless systems. Current projects explore spatiotemporal modulation techniques to achieve magnetic-free non-reciprocal devices, reducing size and complexity in communication hardware. His recent publications emphasize broadband impedance tuning, miniaturized RF circulators, and zero-power wake-up receivers. These contributions leverage multiphysics modeling and nanofabrication to optimize microwave performance in magnetic-free topologies and composite materials. Scientific Awards Association of Old Crows, Nonprofit for Electronic Spectrum Management, Future 5 Award (2022) Experiential PhD Leadership Program (LEADERs Fellow), Gordon Institute of Engineering Leadership (2020) Dr. Kulik actively contributes to academic communities as an affiliate member of the IEEE Microwave Theory & Techniques Society (MTT-13) and serves as a reviewer for journals including IEEE Microwave and Wireless Components Letters and Thin Solid Films . He collaborates with teams in the AIMS Lab on microwave magnetics and system design challenges.

• Electromagnetic Sensors and Actuators
• Human-Machine Interfaces
• Haptic Devices
• +۴ مورد دیگر

Thorsten A. Kern is Professor and Director of the Institute of Mechatronics in Mechanical Engineering at Hamburg University of Technology (TUHH). He joined TUHH in January 2019 after serving as R&D manager for interior components at Continental, leading a team of 300 engineers worldwide. From January 2023 to January 2025, he served as Dean of the Faculty of Mechanical Engineering, and is elected to serve as Vice President for Teaching and Learning from October 2025 to October 2028. Since 2022, he has been Vice President of the EuroHaptics Society. Dipl.-Ing. (2002), Darmstadt University of Technology Dr.-Ing. (2006), Darmstadt University of Technology Prof. Kern's research focuses on electromagnetic sensors and actuators, particularly their system integration in high-dynamic applications. His work spans human-machine interfaces, haptic devices, and the intersection of technology with arts. He has a strong interest in medical applications including robotic rehabilitation systems, wearable exoskeletons, and telemanipulation systems. His research also extends to maritime applications, including ship energy systems and ocean monitoring technologies. Prof. Kern's recent publications reveal a strong focus on haptic interfaces, rehabilitation robotics, and maritime energy systems. His work combines theoretical modeling with practical implementation, often involving interdisciplinary teams. There's a clear trajectory toward tele-rehabilitation systems with haptic feedback, maritime power systems optimization, and novel sensor development. His research demonstrates consistent integration of mechanical, electrical, and control engineering principles to solve complex real-world problems. Over 30 patent families with >120 patent applications worldwide Main editor of "Engineering Haptic Devices" (3rd edition) Vice President of EuroHaptics Society (since 2022) Prof. Kern shows a strong passion for entrepreneurship and mentors young people through the Impossible Founders network. He actively supports students in IP-oriented exploitation of research findings, leveraging his extensive patent experience. His research is supported by various projects in haptics, mechatronics, and rehabilitation engineering, with collaborations spanning academia and industry. Prof. Kern leads the Institute of Mechatronics in Mechanical Engineering (M-4) at TUHH, which houses specialized laboratories including the Haptics Lab, PHiLsLab (Power Hardware-in-the-Loop Laboratory), and Optics Lab (Goniometer Laboratory for Measuring Light Fields). His research team includes multiple research assistants and doctoral students working on electrical measuring systems, autonomous multi-sensor drifters, SMART Sensor Particles, and human-machine collaboration projects.

Julien FAVIERمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Fluid-Structure Interaction
• Lattice Boltzmann Method
• +۳ مورد دیگر

Julien FAVIER is a Professor at Aix-Marseille Université, where he directs the M2P2 laboratory and coordinates the H2020 FALCON project on fluid-structure interaction in aeronautics. He also serves as an associate editor for Computers and Fluids . Research Focus: Fluid-structure interaction (FSI), Lattice Boltzmann Method (LBM), Immersed Boundary Method (IBM), turbulent and compressible flows Applications: Biomedical (aortic valves, mucus transport), aerospace (hypersonic flows), and mechanical systems (rupture/fragmentation) Scientific Contributions include: Developing stable explicit FSI solvers for LBM-IBM coupling Modeling metachronal wave dynamics in cilia arrays Advancing compressible LBM with rotating overset grids Studying drag reduction via flexible filament coatings Pioneering non-Newtonian fluid transport simulations Technical Expertise spans: Multi-grid and dual-time stepping techniques GPU acceleration for heterogeneous architectures Viscoelastic and Herschel-Bulkley flow modeling Validation of immersed boundary methods for turbulent flows

Niels Aageمشاهده پروفایل

دانشیار

• Topology Optimization
• Acoustics and Vibroacoustics
• Biomechanics
• +۵ مورد دیگر

Niels Aage is an Associate Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU). His research focuses on topology optimization, biomechanics, and multiphysics modeling, with applications in acoustic devices, biomedical implants, and microelectromechanical systems (MEMS). He holds roles such as Vice President of the International Society for Structural and Multidisciplinary Optimization (2023–2027). Education and Professional Background: Conducted a 5-month research visit at the University of Colorado, Boulder (USA) in 2010. Specializes in giga-scale numerical modeling, finite element methods, and topology optimization algorithms. Research Interests: Develops novel methods for topology optimization of fluidic, thermal, and acoustic systems. Explores applications in patient-specific spinal implants, metamaterials with vibroacoustic bandgaps, and nonlinear dynamic substructuring. His work integrates machine learning and reduced-order modeling for efficient simulation. Publications: Over 100 peer-reviewed articles, including recent contributions on connectivity promotion in topology optimization (2025), vibroacoustic metamaterial design (2025), and anatomically conforming spinal fusion cages (2024). Research emphasizes high-resolution modeling and bridging computational design with additive manufacturing. Scientific Awards: ISSMO Haftka Young Investigator Award (2021), Equinor Prize 2020, and Hyperion Innovation Excellence Award (2017). Recognized for contributions to structural optimization and computational mechanics. Advising and Grants: Supervises multiple PhD projects, including work on vibroacoustic shape optimization, quantum-opto-mechanical systems, and smart hearing aid modeling. Engages in collaborative projects funded by industry and academia. Labs/Teams: Collaborates with DTU’s Solid Mechanics group and industry partners on projects involving topology optimization, multiphysics simulation, and biomedical engineering. Active in international conferences and serves on editorial boards.

• High Performance Computing
• Multiscale and Multiphysics Simulations
• Fusion Energy Systems
• +۳ مورد دیگر

Jacob Merson is an Assistant Professor in the Department of Mechanical, Aerospace, and Nuclear Engineering at Rensselaer Polytechnic Institute (RPI). He is affiliated with the Scientific Computation Research Center (SCOREC) and the Center for Materials, Devices, and Integrated Systems (CMDIS). His primary research focuses on High Performance Computing (HPC), with a particular emphasis on multiscale and multiphysics simulations. Key areas include fusion energy systems, plasma physics, and biomaterials modeling, leveraging machine learning and advanced computational frameworks. His work integrates cutting-edge tools like the XGC plasma simulation code and the PCMS parallel coupler, supporting digital twin development for fusion energy applications. He has contributed to open-source frameworks for fibrous materials modeling and unstructured mesh tools for energy systems. His research bridges computational methods with practical engineering challenges in fusion, biomechanics, and materials science. Notable contributions include machine learning approaches for constitutive modeling of fibrous materials and plasma turbulence studies in stellarator configurations. His articles emphasize fusion energy innovation, biomaterials mechanics, and HPC workflows. Merson collaborates extensively on multimodel simulation integration and has pioneered tools like Benesh for in situ workflow coordination. Despite his prolific research output, no specific grants or student advisees are explicitly listed in the provided materials.

Fang Luoمشاهده پروفایل

دانشیار

• Power Electronic Converters and Systems
• Power Module Packaging
• EMI Modeling and Mitigation

Fang Luo is the Empire Innovation Associate Professor and Director of the Spellman High Voltage Power Electronics Laboratory at Stony Brook University's Department of Electrical and Computer Engineering. He is affiliated with the College of Engineering and Applied Sciences, focusing on advancing power electronics technologies. His research interests encompass Power Electronic Converters and Systems, Power Module Packaging, and EMI Modeling and Mitigation. These areas address challenges in high-voltage systems, renewable energy integration, and electromagnetic compatibility. Dr. Luo's work emphasizes practical applications, such as optimizing power converters for offshore wind farms and developing advanced packaging solutions for wide bandgap (WBG) semiconductors. His laboratory, the Spellman High Voltage Power Electronics Lab, drives innovation in high-voltage systems and energy-efficient power electronics. His recent publications highlight advancements in HVDC wind energy systems, GaN-based converter designs, and EMI mitigation strategies. He has contributed to the design of modular power converters and cryogenic power electronics for electric aircraft propulsion. As a leader in his field, Dr. Luo's research bridges theoretical modeling and practical engineering solutions, with a focus on sustainable energy systems and cutting-edge semiconductor technologies.