جست‌وجوی استاد و پژوهشگر

Changhong Keمشاهده پروفایل

استاد

• Experimental Mechanics of Nanostructures
• Nanoscale Adhesion and Interfaces
• Nanocomposites
• +۵ مورد دیگر

Changhong Ke is a Professor in the Department of Mechanical Engineering at Binghamton University. His research focuses on experimental mechanics of nanostructures, nanoscale adhesion, nanocomposites, bio-inspired systems, and NEMS. He holds editorial roles for Dataset Papers in Nanotechnology and Multiscale & Multiphysics Mechanics , and leads the Nanomechanics Laboratory. Education: BS and MS in Mechanical Design/Fluid Mechanics – Beijing Institute of Technology PhD in Mechanical Engineering – Northwestern University Postdoctoral Research – Duke University (DNA/RNA elasticity and radiation-induced DNA damage) Research Interests: His work explores the mechanics of 1D/2D nanostructures, bio-inspired sensor design, and advanced materials like boron nitride nanotubes. Key areas include nanocomposite reinforcement, thermal and mechanical properties of nanomaterials, and additive manufacturing techniques. Key Contributions: Recent studies address bio-inspired acoustic sensors, oxidation-resistant interfaces in composites, and novel manufacturing methods. His work bridges nanotechnology with practical applications in sensors, biomedical systems, and advanced materials. Awards & Grants: Not explicitly stated in the text, but his research on corrosion-resistant composites and micromanufacturing has attracted NSF funding (e.g., 2022 NSF grant for tinier circuit techniques). Labs & Teams: Directs the Nanomechanics Laboratory, focusing on nanoscale characterization and device fabrication. Collaborations span multidisciplinary projects in nanotechnology and materials science.

Dehao Liuمشاهده پروفایل

استادیار

• Advanced Manufacturing
• Materials Science
• Multiscale Modeling
• +۳ مورد دیگر

Dehao Liu is an Assistant Professor in the Department of Mechanical Engineering at Binghamton University. He holds a BS from Tsinghua University (2016) and a PhD from Georgia Institute of Technology (2021). Before joining Binghamton in 2022, he was a postdoctoral researcher at Texas A&M University. His research focuses on advanced manufacturing processes, particularly multiscale multiphysics modeling, physics-informed machine learning, and optimization methodologies. He leads the Intelligent Manufacturing & Materials Design Lab and is affiliated with Binghamton’s College of Engineering and Applied Sciences. Education: Bachelor of Science in Mechanical Engineering, Tsinghua University, 2016 Doctor of Philosophy in Mechanical Engineering, Georgia Institute of Technology, 2021 Research interests include: Multiscale multiphysics modeling and simulation Physics-informed machine learning for materials design Process monitoring and control in additive manufacturing Optimization and uncertainty quantification His recent work emphasizes predictive modeling of material microstructures and integrating AI with physical constraints to enhance manufacturing precision. He has contributed to exascale microstructure reconstruction, generative models for large-scale objects, and data-driven process optimization in metal additive manufacturing. Notable collaborations include projects funded by the National Science Foundation and industry partnerships in sustainable energy and biomedical materials. His lab actively explores applications in MEMS devices, microbial fuel cells, and biomaterial scaffolds.

• Electromagnetic Modeling
• Finite Element Analysis
• Computational Electromagnetics
• +۳ مورد دیگر

Jianming Jin is the Y. T. Lo Chair and Professor in the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Grainger College of Engineering. He also serves as Assistant Dean for Zhejiang University-UIUC Institute Engagement. His research focuses on computational electromagnetics, finite element methods, and high-frequency device modeling. Key contributions include physics-informed machine learning for electromagnetic optimization, 3D finite element analysis of resonators, and thermal modeling of nonlinear materials. Affiliations: UIUC Grainger College of Engineering, Zhejiang University-UIUC Institute Key Awards: IEEE Fellow (2001), Harrington-Mittra Award (2017), ACES Awards (2014-2016) Research interests span electromagnetic simulation, multiphysics modeling, and quantum nanowire physics. Recent work emphasizes integrating AI with traditional computational methods to enhance efficiency in high-frequency device design. Collaborations include studies on nonlinear magnetic materials and resonator dynamics. Labs/Teams: Computational Electromagnetics Group at UIUC

Xuekun Luمشاهده پروفایل

مدرس ارشد

• Lithium-ion batteries
• Operando imaging
• 3D microstructure-resolved electrochemical modeling
• +۶ مورد دیگر

Dr. Xuekun Lu is a Senior Lecturer in Green Energy at Queen Mary University of London's School of Engineering and Materials Science, where he also serves as Deputy Chair of the Undergraduate Subject Exam Board. His academic career focuses on the intersection of materials science, electrochemistry, and sustainable energy technologies, with particular emphasis on advanced characterization and modeling of energy storage systems. Dr. Lu's research primarily centers on three interconnected areas: (1) revealing the interplay between 3D microstructure and performance/failure mechanisms of electrochemical energy devices (lithium-ion batteries, fuel cells) using correlative operando characterization techniques (X-ray CT, ptychography, XRD-CT, FIB-SEM, optical microscopy, TEM); (2) multiscale multiphysics modeling of mass transport and electrochemical performance at electrode, device, and system levels; and (3) developing scalable and sustainable engineering solutions to fabricate advanced electrodes with tunable microstructure to enhance energy density, power density, and cycle life of next-generation lithium-ion batteries. His publication record includes 57 total papers with an h-index of 27 and 2,713 citations, featuring 14 first-authored papers in high-impact journals including Nature Communications, Energy & Environmental Science, and Joule. His work demonstrates a clear progression toward increasingly sophisticated multimodal characterization approaches combined with advanced computational modeling to address fundamental challenges in energy storage technology, with particular focus on microstructure-performance relationships in battery electrodes. ESI Highly Cited Paper (citation > 400) Editor's Highlights in Nature Communications 2020 Top 50 Chemistry and Materials Sciences Articles The Top 25 Chemistry and Materials Sciences Articles of 2023 Invited Reviewer for EPSRC Open Postdoctoral Fellowship Dr. Lu currently mentors PhD students Alex Han (researching 'Fabricating Novel-Structured Lithium-Ion Batteries') and Daqing Li (working on 'Advanced Manufacturing of Novel-Structured Next-Generation Automotive Lithium-Ion Battery Electrodes'). He has secured significant research funding, including a £448,598 EPSRC grant for 'From 2D to 4D: correlative imaging and modelling for next-generation automotive' research. His work has been translated into industry R&D for lithium-ion batteries, demonstrating strong academic-industrial collaboration. His research group employs a comprehensive approach combining cutting-edge imaging techniques with advanced computational modeling to tackle critical challenges in energy storage technology, with particular focus on understanding and optimizing the relationship between electrode microstructure and battery performance across multiple spatial and temporal scales.

Rob M.C. Mestromمشاهده پروفایل

استادیار

• Medical Engineering
• Electromagnetic Field Modeling
• Neurostimulation
• +۵ مورد دیگر

Rob M.C. Mestrom is an Assistant Professor in the Electrical Engineering Department at Eindhoven University of Technology (TU/e), specializing in electromagnetic field modeling for medical applications. His research focuses on neurostimulation (e.g., TMS, tDCS), hyperthermia treatment optimization, and multiphysics modeling in clinical contexts. He holds an MSc (cum laude) and PhD from TU/e, with postdoctoral work on RF-MEMS antenna technology. His affiliations include the Electromagnetics for Care+Cure Research Lab (EM4Care+Cure) and the NeuroPlatform, alongside roles in education management. Education: MSc in Mechanical Engineering (TU/e, 2005, cum laude) PhD in Dynamics & Control (TU/e, 2009) Research interests include electromagnetic applications in healthcare, such as personalized neurostimulation protocols, thermal modeling for hyperthermia, and clinical device development. His work bridges engineering and medicine, with projects like HD-EEG-based epilepsy treatment and collaborations with clinical partners. Recent publications (2025–2024) explore wideband dielectric tissue characterization via MRI, uncertainty analysis in ultrasound stimulation, and mechanisms of tDCS. His contributions span 82+ research outputs, 4 major projects, and 18 supervised works. Projects: Personalized Neurostimulation for Epilepsy (2019–2023) PANAMA (RF-MEMS Antenna Research, 2009–2011) Labs/Teams: EM4Care+Cure Lab, NeuroPlatform, and the Eindhoven MedTech Innovation Center. Teaching includes courses on electromagnetic modeling, neurophysiology, and physics for engineering.

• Computational Engineering
• Photonics
• Optics
• +۵ مورد دیگر

Dr. Masoud Seifikar is a Senior Teaching Fellow in Computational Engineering at the Department of Aeronautics, Faculty of Engineering at Imperial College London. He holds a Senior Fellowship from the Higher Education Academy (SFHEA) and focuses on pedagogical innovation, particularly in active learning strategies and technological tools for education. Previously, he served as a Senior Strategic Teaching Fellow in Physics Education Innovation at Imperial College (2019–2023), leading blended learning initiatives and optimizing student learning experiences through online and in-class activities. Dr. Seifikar earned his PhD in Physics from University College Cork (2008–2013), followed by postdoctoral research in nonlinear dynamics of semiconductor lasers and the European PiezoMAT project. His academic qualifications include a Master’s in Teaching and Learning in Higher Education (University College Cork, 2020), a Postgraduate Diploma (2018), and a Postgraduate Certificate (2017) in the same field. Earlier, he completed an MSc in Physics at Shahid Beheshti University (2001–2024). His research spans computational engineering, photonics, and semiconductor materials. Notable contributions include work on dilute nitride alloys, coupled semiconductor lasers, and piezoelectric nanowire sensors. He has published extensively in journals like Applied Optics , Physical Review B , and Nanoscale Research Letters . Dr. Seifikar’s educational innovations include developing pre-class activities for conceptual understanding and in-class tools for self-evaluation. He has collaborated on initiatives such as the Blended Learning Project and actively contributes to academic technology conferences like ISSOTL and APT.

Daniel J. Rixenمشاهده پروفایل

استاد

• Structural Dynamics
• Robotics
• Mechatronics
• +۵ مورد دیگر

Prof. Daniel J. Rixen is a full Professor and Head of the Department of Applied Mechanics at Technische Universität München (TUM). With over 25 years of expertise, his research focuses on structural dynamics, robotics, and mechatronics, integrating theoretical, numerical, and experimental approaches. He holds a Doctorate from the University of Liège (1997) and has held academic leadership roles at TU Delft and TUM. Education: Engineering Degree (Electro-Mechanics), University of Liège (Belgium) MSc in Aerospace Vehicle Design, Cranfield University (UK) PhD in Engineering, University of Liège (Belgium) Research Interests: His work spans robotics path planning, nonlinear structural dynamics, model reduction, and parallel computing applications in aerospace, automotive, and medical instrumentation. Collaborations with industry partners drive innovations in wind energy, mechatronics, and microsystems. Key Contributions: His 2017 IEEE Robotics paper advanced real-time path planning for bipedal robots, while his textbook Mechanical Vibrations (3rd ed., 2015) remains a foundational resource. Hyper-reduction techniques for nonlinear dynamics and impulse-based substructuring methods highlight his computational mechanics expertise. Awards: Best Lecturer Award (TU Delft, 2009 & 2010) Advising & Collaborations: Leads TUM’s Applied Mechanics group, with industry ties in aeronautics and medical devices. No specific student names or grant details provided in the text. Labs/Teams: Research integrates academic-industry partnerships, focusing on robotics control and multiphysical system modeling through TUM’s Engineering and Design School infrastructure.

Noelia Grande Gutiérrezمشاهده پروفایل

استادیار

• bioengineering
• computer-aided design (CAD)
• biomechanics
• +۲ مورد دیگر

Noelia Grande Gutiérrez is an Assistant Professor in Mechanical Engineering and Biomedical Engineering at Carnegie Mellon University's College of Engineering. She holds a courtesy appointment in Biomedical Engineering. Her research integrates computational engineering with cardiovascular medicine, developing multiphysics models to support clinical decision-making for cardiovascular diseases. Key areas include bioengineering, biomechanics, fluid dynamics, and medical device technology. Her computational frameworks simulate complex physiological processes from platelet deposition to arterial occlusion, enabling patient-specific thrombosis investigations in coronary artery disease. Recent work focuses on multiscale modeling approaches bridging molecular interactions with systemic hemodynamics. Grande Gutiérrez received the American Heart Association Fellowship (2016-2018) for her innovative cardiovascular research.

Zhen Liuمشاهده پروفایل

دانشیار

• Soil Mechanics
• Foundation Engineering
• Artificial Intelligence
• +۴ مورد دیگر

Zhen (Leo) Liu is an Associate Professor at the University of Virginia. His research focuses on integrating soil mechanics, foundation engineering, artificial intelligence (AI), and numerical simulations to address challenges in geotechnical applications and interdisciplinary innovations. Key areas include unsaturated soil mechanics, geohazards, energy geotechnics, advanced geomaterials, intelligent geosystems (for smart cities and cyber-physical systems), and system resilience improvements. His work extends to 'big data' solutions for transportation systems and infrastructure safety, with applications in environmental protection and advanced materials. Research interests emphasize multiphysics collaboration, multiscale focus, and AI-driven intelligence. His studies explore traditional geotechnical topics alongside emerging interdisciplinary fields like smart city infrastructure and cyber-physical systems. The lab’s location is Olsson 281, and the email contact is leoliu@virginia.edu. Notable trends in his recent articles (2023–2025) highlight vascular insulin resistance mechanisms, diabetes-related complications, and therapeutic interventions (e.g., automated insulin delivery systems). Exercise physiology and metabolic responses are recurrent themes, particularly regarding ghrelin dynamics, obesity, and sex differences. The articles also address biomarkers for diabetes diagnosis, circadian rhythm impacts on vascular health, and drug efficacy studies (e.g., empagliflozin). No scientific awards or grants are explicitly mentioned in the provided text. His advising and lab activities are centered at the Olsson 281 laboratory, though specific team details or future projects beyond the stated research interests are not detailed.

• Spintronics
• Condensed Matter Physics
• Optical Physics
• +۳ مورد دیگر

Chuangtang Wang is a Research Fellow in the Department of Physics at the University of Michigan, Ann Arbor. He is affiliated with the Randall Laboratory and Homer A. Neal Lab, contributing to cutting-edge research in spintronics and magneto-optical devices. His office is located in West Hall 243, and he can be reached at wangct@umich.edu . His work integrates theoretical and experimental approaches to advance understanding of magnetic materials and their applications in optoelectronics and nanotechnology. Wang's research interests span multiple disciplines, including: Spintronics Condensed Matter Physics Magneto-Optical Effects Plasmonics Nanotechnology Optical Manipulation of Magnetic Order His recent publications highlight innovations in all-optical helicity-dependent switching, spin-orbit torque systems, and monolithic integration of isolators. This body of work demonstrates a focus on optimizing magneto-plasmonic sensors and exploring novel material configurations for nanoscale devices. His research often combines experimental characterization with advanced computational modeling to address challenges in magnetic dynamics and optoelectronic integration. No scientific awards have been explicitly mentioned in the provided text. Advising details and grant information are not available in the current dataset. Chuangtang Wang’s contributions include support for the Department’s Demonstration Lab and involvement in initiatives related to Physics Shops and student group resources. He actively participates in the College of Literature, Science, and the Arts (LSA) community, though specific educational background details are not documented here. Wang’s laboratory affiliations at the Randall Lab and Homer A. Neal Lab provide state-of-the-art facilities for studying ultrathin magnetic films, van der Waals heterostructures, and plasmonic systems. These environments enable his team to conduct experiments requiring precision in material synthesis, optical measurements, and device fabrication.

Alison Marsdenمشاهده پروفایل

استاد

• Cardiovascular Engineering
• Biomechanics
• Medical Device Design
• +۵ مورد دیگر

Alison Marsden is the Douglass M. and Nola Leishman Professor of Cardiovascular Diseases at Stanford University, with joint appointments in Pediatrics (Cardiology) and Bioengineering, and a courtesy appointment in Mechanical Engineering. She previously held faculty positions at UC San Diego (2007–2015) and completed her postdoctoral training in Bioengineering and Pediatric Cardiology at Stanford (2005–2007). Her research integrates computational modeling, medical device design, and clinical translation to address congenital heart disease and cardiovascular hemodynamics. Education: BSE in Mechanical Engineering, Princeton University (1998) MSE in Mechanical Engineering, Stanford University (2000) PhD in Mechanical Engineering, Stanford University (2005) Research focuses on numerical methods for cardiovascular blood flow simulation, optimization of medical devices, and mechanobiological processes in congenital heart defects. Her lab develops open-source software like SimVascular and collaborates on clinical trials for pediatric heart surgery. Recent work includes bioprinted vascular grafts, hemodynamic predictors of aneurysm growth, and computational tools for arrhythmia prediction. Key awards include the NSF CAREER Award (2011), Van C. Mow Medal (2023), and multiple teaching/mentorship recognitions. She serves on editorial boards for leading journals in biomechanics and chairs the Fontan Outcome Registry using CMR Examinations (FORCE) initiative. Advising and grants: Supervises graduate students in bioengineering and mechanical engineering; secures funding from NSF, NIH, and private foundations. Over 160 peer-reviewed publications demonstrate her leadership in cardiovascular computational modeling. Labs/Teams: Director of the Cardiovascular Biomechanics Computation Lab, co-founder of the SimVascular open-source project, and collaborator on interdisciplinary teams in pediatric cardiology and biomedical engineering.

• Numerical Analysis
• Mathematical Physics
• Electromagnetics
• +۴ مورد دیگر

Dr. Jinjie Liu is a Professor in the Division of Physics, Engineering, Mathematics, and Computer Science at Delaware State University. His research focuses on numerical analysis and mathematical physics, particularly in electromagnetics, nonlinear optics, metamaterials, and semiconductor devices. He holds a B.S. from the University of Science and Technology of China (2001), an M.S. (2003), and Ph.D. (2006) from Stony Brook University, followed by postdoctoral training at Arizona State University and the University of Arizona. Research Highlights: Development of advanced FDTD methods for material interfaces and nonlinearities. Investigation of transformation optics and cloaking phenomena. Simulations of semiconductor devices and hydrodynamic electron fluid models. Awards: DSU Vice President’s Choice Awards for Research (2013). Selected Projects: Transformation Optics Based Local Mesh Refinement Subpixel Smoothing FDTD Method Invisibility Cloaking Simulation

• Robot Vision
• Machine Learning
• Autonomous Systems
• +۹ مورد دیگر

Prof. Dr. Sebastian Houben is a Professor of Robot Vision and Machine Learning at Bonn-Rhein-Sieg University of Applied Sciences (H-BRS). He holds roles as International Affairs Representative and Head of the Master's Examination Board for Autonomous Systems. His research focuses on AI-driven solutions for autonomous systems, environmental monitoring, and energy efficiency. Key projects include: INNERVATE : Enhancing driver maneuver evaluation via AI for automotive safety. DKZ.2R : Promoting data literacy and high-performance computing across disciplines. DigitalTwin-4-Multiphysics Lab : Urban and industrial digital twins for efficiency optimization. GARRULUS : AI-driven reforestation using drones and precision seeding. He leads the Institute for AI and Autonomous Systems (A2S) and collaborates with industry partners like GKN Driveline. His research spans robotics, computer vision, and sustainable energy systems. Prof. Houben is a member of the Promotionskolleg Nordrhein-Westfalen (Doctoral College North Rhine-Westphalia). His work emphasizes explainable AI (XAI), safety-critical systems, and interdisciplinary applications.

Pierre Lefrancمشاهده پروفایل

مدرس

• Power Electronics
• Power Converters
• Optimization
• +۳ مورد دیگر

Pierre Lefranc serves as a Lecturer at the School of Energy, Water and Environment (ENSE3) within Grenoble Institute of Technology, affiliated with the G2Elab research laboratory where he is a key member of the Power Team (Équipe de Puissance). His academic responsibilities span teaching and research in power electronics, converter design, and power switch control across undergraduate and graduate programs. His educational foundation includes: Supélec Engineer (2002) DEA in Electrical Engineering from Paris (2002) Doctorate in Electrical Engineering from INSA Lyon (2005) Dr. Lefranc's research centers on power converter modeling and optimization , developing methodologies for pre-sizing, sizing, and algorithmic optimization while addressing power-control interactions. He pioneers close control of advanced power switches (IGBT, MOSFET, HEMT-GaN), focusing on high-galvanic-isolation signal transmission, planar/coreless transformer design with large air gaps, and control circuit optimization for next-generation wide-bandgap devices. His work bridges theoretical optimization with practical implementation challenges in high-frequency power systems. Analysis of his 11 publications (2009-2013) reveals consistent innovation in virtual prototyping and constraint-based optimization for DC-DC/DC-AC converters. His research spans transportation electrification, medium-voltage multi-level converters (10-50kV), and plasma reactor applications, with strong emphasis on multi-physics constraints (thermal, spatial, electromagnetic) and real-world implementation of wide-bandgap semiconductor technologies. Dr. Lefranc maintains active international collaborations including: NTNU (Norway) on Carnot 3D-MMC project for medium-voltage modular converters CentraleSupélec on predictive control of hybrid power systems LAAS Laboratory on micro-inductor optimization While his teaching portfolio spans instrumentation electronics, power converters, and switch control across ENSE3's 1A-3A programs, the source text indicates no formal student advisement records or major scientific awards. His research continues through G2Elab's Power Team on transformer design, plasma reactors, and micro-inductor optimization.

Marek Behrمشاهده پروفایل

استاد

• Computational Mechanics
• Physiological Modeling
• Numerical Methods
• +۷ مورد دیگر

Professor Marek Behr is a Universitätsprofessor (C4/W3) at the Chair for Computational Analysis of Technical Systems (CATS) in the Faculty of Mechanical Engineering at RWTH Aachen University, Germany. He serves as founding director of the Center for Simulation and Data Science (JARA-CSD) since 2018, President of the German Association for Computational Mechanics since 2021, and is an Adjunct Professor of Chemical and Biomolecular Engineering at Rice University since 2005. Professor Behr's research focuses on computational mechanics with particular emphasis on physiological model development and numerical methods for complex fluids. His work spans Computational Fluid Dynamics (CFD) , High-Performance Computing (HPC) , Fluid-Structure Interaction (FSI) , and 4D Finite Element Methods . His research group at CATS develops advanced computational techniques for engineering and biomedical applications, with recent focus on cardiovascular modeling including hemolysis prediction, stent restenosis, and left ventricular dynamics under assist device support. The publication record of Professor Behr shows a strong trajectory in computational biomechanics, particularly in cardiovascular applications. His recent work (2023-2025) demonstrates increasing focus on multiphysics modeling of blood flow, stent-artery interactions, and computational approaches to understanding hemolysis and restenosis mechanisms. His research integrates advanced numerical methods with physiological modeling to address clinically relevant problems in cardiovascular medicine. Professor Behr has received significant recognition for his contributions to computational mechanics: Elected Fellow of the International Association for Computational Mechanics (IACM) in 2014 Recipient of the Leading Scientist Award (3.6 M€) from St. Petersburg, Russian Federation in 2010, described as "the largest individual scientific award in the world" Google Scholar h-Index of 45 and ISI h-Index of 30, based on approximately 3,600 citations Professor Behr serves as speaker of the International Research Training Group 2379 Modern Inverse Problems in collaboration with the Oden Institute at the University of Texas at Austin. He has established significant research collaborations across institutions and has supervised numerous PhD and Master's students. His work is supported by substantial research funding, including the 3.6 M€ Leading Scientist Award. The Chair for Computational Analysis of Technical Systems (CATS) under Professor Behr's leadership forms part of the Center for Simulation and Data Science (JARA-CSD), a collaboration between RWTH Aachen University and Forschungszentrum Jülich. CATS focuses on developing and applying advanced computational methods to solve complex engineering problems, with particular expertise in fluid dynamics, solid mechanics, and their interaction in biological systems.