جست‌وجوی استاد و پژوهشگر

Minh Q. Phanمشاهده پروفایل

دانشیار

• System Identification
• Control Theory
• Robotics
• +۴ مورد دیگر

Minh Q. Phan is an Associate Professor of Engineering at Dartmouth College's Thayer School of Engineering. His expertise spans system identification, iterative learning control, model predictive control, robotic swarm control, and intelligent control systems. He holds a BS from the University of California, Berkeley, and MS/M.Phil/PhD degrees from Columbia University in Mechanical Engineering. Dr. Phan has contributed to over 50 peer-reviewed publications and serves as an Associate Editor for the Journal of Guidance, Control, and Dynamics. His research focuses on advancing control theory applications in robotics, structural health monitoring, and sustainable construction materials. Key contributions include the development of OKID (Observer/Kalman Filter Identification) methods and bilinear system identification frameworks. Education History: Bachelor of Science in Mechanical Engineering, UC Berkeley, 1985 Master of Science in Mechanical Engineering, Columbia University, 1986 Master of Philosophy in Mechanical Engineering, Columbia University, 1988 Doctor of Philosophy in Mechanical Engineering, Columbia University, 1989 Research Interests: Advanced control methodologies for dynamic systems Model-based predictive control strategies Applications in robotics and aerospace engineering Structural health monitoring via system identification Machine learning for materials science Teaching Responsibilities include courses like ENGG 149 (Systems Identification), ENGS 145 (Modern Control Theory), and ENGG 148 (Structural Mechanics). His work bridges theoretical control systems with practical industrial applications, including automation in food processing and sustainable construction practices. Dr. Phan has collaborated on projects addressing viral epidemiology in Vietnam and coastal erosion mitigation strategies.

Guillermo Amadorمشاهده پروفایل

استادیار

• Biophysics
• Biomaterials
• Biomechanics
• +۷ مورد دیگر

Dr. Guillermo Amador is an Assistant Professor in the Experimental Zoology department at Wageningen University & Research. His research focuses on understanding how animals interact with complex environments through locomotion, adhesion, and fluid dynamics. He investigates biological systems like insects, plants, and marine organisms to inspire bio-engineered solutions for robotics, microfluidics, and material science. Amador received his PhD in Mechanical Engineering from Georgia Institute of Technology (USA), followed by postdoctoral research at the Max Planck Institute for Intelligent Systems (Germany) and a Marie Sklodowska-Curie fellowship at TU Delft (Netherlands). His expertise spans biophysics, biomaterials, and biomechanics, with a focus on self-cleaning mechanisms and bioadhesion. He collaborates with the 4TU consortium on Dutch Soft Robotics to develop bio-inspired designs. His work bridges fundamental biology with engineering applications, emphasizing interdisciplinary approaches to solve challenges in robotics and environmental science. Amador teaches courses including Biomimetics and Functional Zoology , integrating his research into education. His research highlights include studies on cuttlefish suction cups, stick insect adhesion, and pollen transport mechanisms in pollinators.

Farzad Mashayekمشاهده پروفایل

استاد

• Turbulent Reacting Flow
• Plasma Flow
• Electrostatic Atomization
• +۹ مورد دیگر

Farzad Mashayek is a Professor and Department Head of Aerospace and Mechanical Engineering at the University of Arizona, College of Engineering. He is a member of the Graduate Faculty and leads the Computational Multiphase Transport Laboratory. His research integrates high-fidelity simulations, machine learning, and experimental validation across diverse domains in fluid dynamics and energy systems. Educational Background: PhD in Mechanical Engineering, State University of New York at Buffalo, Buffalo, NY MS in Mechanical Engineering, Sharif University of Technology, Tehran, Iran BS in Mechanical Engineering, Sharif University of Technology, Tehran, Iran His research interests include turbulent reacting flows, plasma dynamics, electrostatic atomization, solid-ion and lithium batteries, computational fluid dynamics, and machine learning applications in engineering. He employs high-order spectral element methods, phase-field modeling, and deep neural networks to study complex multiphysics phenomena such as drop impact, battery degradation, and turbulence modeling. The recent publications reflect a strong trend toward integrating machine learning with multiphysics simulations, particularly in battery safety (thermal runaway prediction), materials characterization (STEM image analysis), and fluid dynamics (modal analysis of turbulence). His work often involves collaboration with experimental groups to validate models, especially in dental aerosol suppression and electrohydrodynamics. Scientific Awards: Sustained Service Award, American Institute of Aeronautics and Astronautics (AIAA), Spring 2022 Best Presentation Award, The 20th International Conference on Computational Mathematics, Parallel and Distributed Computing, Summer I 2018 Dr. Mashayek has secured funding from NSF (GOALI program) for controlled coating via charged droplet deposition. He advises graduate students and postdoctoral researchers in computational mechanics and energy systems, fostering interdisciplinary research. He has contributed to engineering education, particularly during the pandemic, with active learning strategies in online instruction. He leads a dynamic research team focused on advancing simulation tools and applying them to real-world challenges in energy, manufacturing, and public health.

Megan Valentineمشاهده پروفایل

استاد

• Biophysics
• Mechanical Engineering
• Materials Science
• +۵ مورد دیگر

Megan Valentine is a Professor of Mechanical Engineering at the University of California, Santa Barbara (UCSB), affiliated with the College of Engineering. She leads an interdisciplinary research group focused on biological and bioinspired materials, investigating how forces are generated and transmitted in living systems to design responsive synthetic materials. Her work bridges engineering, physics, chemistry, and biology. Education: PhD in Physics from Harvard University, MS in Physics from the University of Pennsylvania, and BS in Physics from Lehigh University. Affiliations include the California NanoSystems Institute (CNSI), Materials Research Laboratory (MRL), Neuroscience Research Institute, and the Center for Stem Cell Biology and Engineering. Research interests span soft material mechanics, bioengineering, and systems biology, with applications in marine-inspired materials, mechanobiology, and soft robotics. Her lab employs advanced experimental techniques to study biophysical and biochemical mechanisms in living systems and translate them into engineered materials capable of self-healing, movement, and environmental responsiveness. Notable awards include the NSF Early CAREER Award, Fulbright Scholarship, and election as Fellow of the American Physical Society and American Institute for Medical and Biological Engineering. Her contributions emphasize creativity, collaboration, and diversity, with a focus on addressing societal challenges through interdisciplinary innovation.

• Mechanical Engineering
• Autonomous Vehicles
• Artificial Intelligence
• +۳ مورد دیگر

Professor Mohammad FARD is a faculty member at RMIT University's School of Engineering, specializing in Mechanical Engineering and Intelligent Systems. He leads research in autonomous vehicles, crash safety, and driver monitoring using AI. His industry experience includes six years at Nissan Technical Centre, focusing on vehicle body design. He holds a PhD from Tohoku University and has collaborated across Engineering, Health, and Science disciplines, achieving international media coverage for work on driver drowsiness and road safety. Research Interests: Autonomous Vehicles Advanced Crash Safety Driver State Monitoring AI in Noise/Vibration Human Factors/Ergonomics Teaching & Projects: Teaches Advanced CAE, Vehicle NVH, and research supervision in areas like crash simulation and vibration control. Current projects include Formula One safety barriers and driver education for autonomous vehicles. Awards & Labs: No awards listed. Active in cross-disciplinary teams and labs addressing automotive innovation and safety.

• Uncertainty Quantification
• Scientific Machine Learning
• Stochastic Optimization
• +۳ مورد دیگر

Hadi Meidani is a Clinical Associate Professor at the Carle Illinois College of Medicine , specifically within the Department of Biomedical and Translational Sciences at the University of Illinois at Urbana-Champaign . He teaches courses in Civil and Environmental Engineering, including topics like Systems Engineering & Economics , Machine Learning in CEE , and Uncertainty Quantification . Ph.D., Civil Engineering, University of Southern California (2012) M.S., Electrical Engineering, University of Southern California (2012) M.S., Structural Engineering, Sharif University of Technology (2005) B.S., Civil Engineering, K.N. Toosi University of Technology (2002) Dr. Meidani's research focuses on uncertainty quantification , scientific machine learning , and optimization under uncertainty for engineering systems. His work spans stochastic multiscale analysis , physics-informed machine learning , and model reduction techniques. His recent publications emphasize machine learning for infrastructure systems , graph neural networks , physics-informed models , and traffic assignment . Key trends include deep learning , multi-fidelity modeling , and neural operator transformers applied to metamaterial design , seismic reliability , and autonomous freight delivery .

• Biomechanics
• Biomimetics
• Bio-inspired Structures
• +۱۴ مورد دیگر

Renate Sachse is a Researcher at the Chair of Structural Analysis, Technical University of Munich (TUM), where she has worked since May 2024. Previously, she held postdoctoral positions at Harvard University's Bertoldi Lab (2024) and TUM's Chair of Computational Mechanics (2021-2024), following academic staff roles at the University of Stuttgart (2015-2020). Her interdisciplinary work bridges civil engineering, biomechanics, and computational modeling. Her educational foundation includes a Master's in Civil Engineering from the University of Stuttgart (2014; thesis: 'Isogeometric contact analysis of thin-walled structures') and a Bachelor's from the same institution (2011; thesis: 'A Primary School Pavilion for Magagula in South Africa - Structural Analysis'). She also completed ERASMUS studies at ESTP Paris and internships at Foster + Partners and Werner Sobek AG. Dr. Sachse's research centers on biomechanics and biomimetics, with pioneering work on plant-inspired structures. She investigates snapping mechanisms in carnivorous plants (Venus flytrap, waterwheel plant) to develop bio-inspired adaptive systems, soft robotics, and metamaterials. Her expertise spans motion design for large-deformation structures, isogeometric analysis, and hygroscopic actuation in 4D-printed materials, emphasizing computational modeling of contact mechanics and structural stability. Analysis of her 15 most recent publications reveals a dominant focus on biomechanics (60% of articles), particularly plant movement mechanics translated into engineering solutions. Her work consistently integrates computational structural analysis with biological principles, showing increasing emphasis on motion design (25% of recent output) and additive manufacturing applications (15%). Key trends include translating snap-buckling phenomena into robotics and developing material design spaces for responsive structures. Her distinguished awards include the Bertha Benz Prize (2022), Klaus Tschira Boost Fund Fellowship (2022-2024), and University of Stuttgart Publication Award (2022). Additional recognition comprises GAMM Juniors Fellowship (2020-2022), AVK Innovation Award (2017), and Emil Mörsch Study Prize (2014). She has secured independent funding through the Klaus Tschira Boost Fund for high-risk interdisciplinary projects and participates in collaborative initiatives including CoDA, MistralWind, WINSENT, and FlexWing. While teaching advanced courses at TUM (Advanced Finite Element Methods, Theory of Plates), her mentorship focuses on computational mechanics and biomimetic design principles. Currently based at TUM's Chair of Structural Analysis under Prof. Bletzinger, she maintains active collaboration with Harvard University's Bertoldi Lab in developing next-generation adaptive structures.

Olga Sorkine Hornungمشاهده پروفایل

استاد

• Computer Graphics
• Geometric Modeling
• Geometry Processing
• +۳ مورد دیگر

Prof. Olga Sorkine Hornung is a Full Professor of Computer Science at ETH Zürich, leading the Interactive Geometry Lab. She holds a BSc and PhD from Tel Aviv University (2000 and 2006) and conducted postdoctoral research at Technical University Berlin. Her research focuses on computer graphics, geometric modeling, and geometry processing, with applications in shape editing, digital fabrication, and animation. She has received numerous accolades, including the ACM Fellowship (2020), ERC Consolidator Grant (2020), and the Golden Owl Teaching Award (2021). Her work bridges theoretical foundations and practical algorithms, addressing challenges in parameterization, surface compression, and interactive design tools. Her research interests span: Computer Graphics & Visualization Geometric Modeling & Processing 3D Content Creation & Digital Fabrication Garment Design & Simulation Human Motion Analysis & Animation Awards and grants include: 2024: Best Paper Honorable Mention (EUROGRAPHICS) 2023: Member of Swiss Academy of Engineering Sciences (SATW) 2020: ERC Consolidator Grant 2017: Rössler Prize (ETH Zurich) Her lab focuses on developing novel methods for interactive geometry processing, with recent advancements in garment modeling (e.g., AIpparel, Rags2Riches) and motion retargeting systems like WalkTheDog. She actively collaborates on interdisciplinary projects, including biomedical applications and sustainable fashion technology.

Aleksandar Jeremicمشاهده پروفایل

دانشیار

• Biomedical Signal Processing
• Statistical Signal Processing
• Biometrics
• +۷ مورد دیگر

Aleksandar Jeremic is an Associate Professor in both the Electrical & Computer Engineering department and the McMaster School of Biomedical Engineering at McMaster University. His research focuses on biomedical signal processing, statistical signal processing, and biometrics, with applications in healthcare and biomedical systems. He holds a Dipl. Ing. from the University of Belgrade, and an M.S. and Ph.D. from the University of Illinois at Chicago. His expertise spans physiological signal analysis (e.g., ECG/EEG), medical imaging, and machine learning for biomedical applications. He has authored over 50 technical articles and two book chapters, and has been recognized with a teaching award from the McMaster Electrical and Computer Engineering Society (2018). He supervises graduate students in both theoretical and applied research areas. Dr. Jeremic teaches advanced courses such as Biomedical Signal Modeling and Processing and Advanced Probability and Random Processes , emphasizing practical applications of signal processing in healthcare. His work includes clinical implementations like neonatal seizure monitoring software and microwave imaging for breast cancer detection.

• Soft tissue mechanics
• Articular cartilage mechanics and lubrication
• Cartilage tissue engineering
• +۷ مورد دیگر

Gerard A. Ateshian is the Andrew Walz Professor of Mechanical Engineering and Professor of Biomedical Engineering at Columbia University, where he has been a faculty member since 1991. He also served as Chair of the Department of Mechanical Engineering from 2011 to 2014 and is the founding director of the Musculoskeletal Biomechanics Laboratory (MBL), established in 1996. Education: BS (1986), MS (1987), MPhil (1990), and PhD (1991) in Mechanical Engineering from Columbia University. Research Interests: Professor Ateshian's research focuses on the mechanics of soft biological tissues , particularly articular cartilage . His work integrates theoretical, experimental, and computational approaches to understand cartilage lubrication, tissue engineering, and growth and remodeling in living tissues. He has extended continuum mechanics frameworks to model complex biological phenomena such as mass transport, osmotic effects, and reactive mechanics in tissue mixtures. His recent efforts include developing open-source computational tools (FEBio) and translating research into clinical treatments for osteoarthritis . Scientific Awards: H.R. Lissner Medal, ASME (2017) OARSI Basic Science Award (2013) Columbia Engineering Alumni Association Distinguished Faculty Teaching Award (2012) Great Teacher Award, Society of Columbia Graduates (2002) YC Fung Young Investigator Award, ASME (1997) Fellow: ASME, BMES, AIMBE Advising and Mentorship: Professor Ateshian has advised numerous PhD students, including recent graduates Vince Sise and Katherine Spack , who completed their dissertations on cartilage fatigue and osteochondral allografts, respectively. His lab actively mentors students from both Mechanical and Biomedical Engineering programs. Laboratory and Collaborations: He directs the Musculoskeletal Biomechanics Laboratory (MBL) , which collaborates closely with the Cellular Engineering Laboratory of Prof. Clark Hung and with Dr. Jeffrey Weiss at the University of Utah on computational modeling (FEBio). He also collaborates with Columbia University Medical Center faculty on clinical translation of cartilage research.

Amadeus Gebauerمشاهده پروفایل

پژوهشگر

• Cardiac mechanics
• Cardiac growth and remodeling
• Computational mechanics
• +۳ مورد دیگر

Amadeus Gebauer is a Researcher at the Chair of Computational Mechanics within the Institute for Computational Mechanics at the Technical University of Munich (TUM), serving as a Research Associate since 2019. His work specializes in computational biomechanics with emphasis on cardiac mechanics modeling, growth and remodeling processes, and multi-physics simulation frameworks. Education: Master of Science (M.Sc.) in Mechanical Engineering, Technical University of Munich, 2019 Research Interests: Gebauer's research centers on cardiac mechanics modeling, including growth and remodeling of cardiac tissue, cardiac active tissue mechanics, and medical image processing. He develops advanced computational methods for parallel and high performance computing, particularly through the 4C multi-physics simulation framework. His work integrates constrained mixture models to simulate organ-scale biological processes, bridging computational mechanics with clinical cardiology applications and focusing on mechanobiological stability in cardiac systems. Publication Trends: Gebauer's publications (2018-2025) demonstrate consistent innovation in computational cardiology, primarily using constrained mixture models to address cardiac growth and remodeling. His recent work introduces adaptive integration techniques for history variables and homogenized modeling approaches, while expanding into software benchmarking for cardiac elastodynamics and gastric motility simulations. These contributions highlight his expertise in developing robust numerical methods for multi-physics biomedical problems, with increasing focus on patient-specific applications and high-performance computing solutions. Teaching and Advising: Gebauer teaches core computational mechanics courses including Finite Elemente and Numerische Festkörpermechanik across multiple semesters. He has supervised diverse student projects ranging from term papers to Master's theses, with notable collaborations including Maximilian Grill's shoulder biomechanics research (2020) and Janina Datz's artery geometry framework development (2021). His advising consistently focuses on cardiac mechanics, computational modeling, and medical device simulation. Research Environment: As part of Professor Wolfgang A. Wall's Institute for Computational Mechanics (LNM) at TUM, Gebauer contributes to a leading research group in computational solid/fluid mechanics. The LNM develops the 4C simulation framework for complex engineering and biomedical challenges, with current emphasis on cardiac growth modeling, multi-physics integration, and high-performance computing applications in personalized medicine.

• Aerospace Composites
• Damage Tolerance Analysis
• Impact Mechanics
• +۳ مورد دیگر

Dr Andrew Rhead is a Senior Lecturer in the Department of Mechanical Engineering at the University of Bath, specializing in aerospace composites and damage tolerance analysis. His research focuses on impact damage detection, failure mechanism modeling, and Non-Destructive Evaluation (NDE) techniques for composite structures. MSci in Mathematical Sciences (Dynamical Systems) - University of Bristol (2006) PhD in Composite Damage Tolerance - University of Bath (2009) His work develops computationally efficient analytical models for compression after impact (CAI) strength prediction in composite laminates, surpassing traditional finite element methods. Key projects include hydrogen storage systems for aircraft, cryogenic composite testing, and steered fiber manufacturing optimization. Active in 10 projects including ASPIRE and HyFIVE Collaborates with Airbus, GKN Aerospace, and EPSRC Research trends show emphasis on sustainable aviation materials, structural battery integration, and advanced testing methodologies. Current affiliations include the Institute for Mathematical Innovation (IMI) and Centre for Integrated Materials, Processes & Structures (IMPS).

Ron H.J. Peerlingsمشاهده پروفایل

دانشیار

• Micromechanics
• Micro-plasticity
• Multiscale modelling
• +۷ مورد دیگر

Ron H.J. Peerlings is Associate Professor in the Department of Mechanical Engineering at Eindhoven University of Technology (TU/e) , where he leads the Mechanics of Materials research group. Promoted to Associate Professor in 2007 after joining as Assistant Professor in 2000, he has built an extensive portfolio in theoretical and computational mechanics of materials. Education: PhD (1999) – Eindhoven University of Technology, thesis: Enhanced damage modelling for fracture and fatigue Post-doctoral research (1999–2000) – University of Cambridge, Engineering Department Research interests revolve around micromechanics , micro-plasticity , multiscale modelling , homogenisation , damage and fracture , and enriched continuum theories . His work spans advanced high-strength steels, composites, paper and fibrous networks, with strong emphasis on coupling rigorous theoretical developments to industrially motivated problems. His recent publications (2023-2025) demonstrate a clear trajectory towards integrating advanced experimental techniques (e.g., digital image correlation, micro-mechanical testing) with high-fidelity computational frameworks such as crystal-plasticity finite-element modelling, FFT-based solvers and micromorphic homogenisation. Dominant themes include: Deformation and fracture in lath martensite and dual-phase steels Hygro-mechanics of paper and fibrous networks Pattern-transforming mechanical metamaterials Discrete-to-continuum scale bridging methods Scientific awards are not explicitly listed in the provided material; however, his prolific output (294 research items, >6500 citations) attests to significant peer recognition. Teaching & supervision: He delivers courses on Computational Mechanics – Numerical Methods for Fluids and Solids and Fracture Mechanics – Theory and Application , and has supervised >80 student works and numerous PhD candidates whose names appear on joint publications. Laboratory & teams: He heads the Group Peerlings within the Mechanics of Materials cluster, maintaining close collaboration with the Mechanics of Materials Group Geers and extensive national/international experimental and computational networks.

Jurgen Becqueمشاهده پروفایل

دانشیار

• Cold-formed steel structures
• Stainless steel structural behavior
• Local and overall buckling interaction
• +۳ مورد دیگر

Dr. Jurgen Becque is an Associate Professor in Structural Engineering at the University of Cambridge's Department of Engineering. He specializes in cold-formed steel structures, stainless steel structural behavior, and stability analysis, with a focus on local-overall buckling interaction and innovative design methodologies. His work bridges experimental investigations with computational modeling and machine learning applications. Research Interests: Cold-formed steel structural systems Stainless steel column stability Local and overall buckling interaction Mechanics-based design optimization Machine learning for structural behavior prediction Recent publications demonstrate expertise in cross-sectional stability, connection mechanics, and composite systems like UHPC-confined stainless steel columns. His work addresses both monotonic and cyclic loading scenarios, contributing to Eurocode 3 design standards.

• Financial Engineering
• Operations Research
• Stochastic Modeling
• +۳ مورد دیگر

Liming Feng is an Associate Professor at the Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, and has served as Director of the Master of Science in Financial Engineering (MSFE) program since 2022. His academic career at the university spans from Assistant Professor (2006-2012) to his current role. He earned his Ph.D. in Industrial Engineering and Management Sciences from Northwestern University (2006), an M.S. in Mathematics from Northwestern University (2000), and a B.S. in Mathematics from Beijing Normal University (1997). Ph.D., Industrial Engineering and Management Sciences, Northwestern University, 2006 M.S., Mathematics, Northwestern University, 2000 B.S., Mathematics, Beijing Normal University, 1997 Feng’s research focuses on Financial Engineering, Stochastic Modeling, and Computational Methods. He has contributed extensively to quantitative finance, particularly in options pricing, portfolio optimization, and market impact models. His work leverages advanced numerical methods, Fourier transforms, and stochastic calculus to solve complex financial problems. The trends in his publications highlight expertise in Levy processes, jump diffusion models, and numerical algorithms for financial derivatives. He has developed innovative techniques for Bermudan options pricing, discretely monitored barrier options, and portfolio deleveraging strategies. His articles often intersect Operations Research with Financial Engineering, emphasizing computational efficiency and mathematical rigor. ISE Faculty Fellow (2025) INFORMS Financial Services Section Best Student Research Paper (2013) First runner-up of the 2012 Morgan Stanley Prize for Excellence in Financial Markets Feng has served on editorial boards for Operations Research Letters and Mathematical Finance . He has been recognized repeatedly for teaching excellence, including the Sharp Outstanding Teaching Award (2011, 2022) and multiple entries in the List of Teachers Ranked as Excellent by Their Students (2007-2024). He currently leads the MSFE program and contributes to curriculum development through courses like IE 522 (Statistical Methods in Finance) and IE 527 (MSFE Professional Development).