جست‌وجوی استاد و پژوهشگر

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.

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

Pu Zhangمشاهده پروفایل

دانشیار

• Composite Materials
• Architected Materials
• Liquid Metal Technologies
• +۵ مورد دیگر

Pu Zhang is an Associate Professor and Undergraduate Studies Director in the Department of Mechanical Engineering at Binghamton University (State University of New York at Binghamton). He leads the Composite and Architected Materials Group, focusing on advanced materials research. Previously, he held postdoctoral positions at the University of Manchester and earned his PhD from the University of Pittsburgh, with earlier degrees from Hunan University. Education background includes a B.S. and M.S. in Mechanics from Hunan University (China), followed by a Ph.D. in Mechanical Engineering from the University of Pittsburgh (USA). His research interests revolve around the mechanics, design, and manufacturing of composite and architected materials, particularly those incorporating liquid metals. Key areas include: Development of soft conductive composites and metamaterials Advanced manufacturing techniques like additive manufacturing and hybrid methods Multiscale and multiphysics modeling for material behavior prediction Recent publications highlight advancements in liquid metal-based composites, additive manufacturing innovations, and multiscale modeling approaches. These works address challenges in material fabrication, characterization, and application in fields like soft robotics and wearable electronics. He has received notable recognitions, including the NSF CAREER Award (2022) and the Watson Early-Stage Distinguished Research Award (2024). Zhang has taught courses such as Intro to Solid Mechanics and Mechanics of Composites. He has mentored over seven PhD and MS students and holds multiple patents. His research is supported by NSF, IEEC, and industry partnerships. His lab, the Composite and Architected Materials Group, fosters interdisciplinary research in soft functional materials and advanced manufacturing technologies.

• 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.

• computational multiscale modeling
• digital twin development
• applied artificial intelligence
• +۷ مورد دیگر

Davood Pourkargar is an Assistant Professor in the Tim Taylor Department of Chemical Engineering at Kansas State University. He is also a Graduate Faculty Member at the Food Science Institute and a Faculty Researcher at the Johnson Cancer Research Center. His work focuses on integrating data with first-principle models to understand complex systems across multiple scales. Ph.D. in Chemical Engineering from Pennsylvania State University (2015) M.S. in Process Simulation and Control from Sharif University of Technology (2010) B.S. in Chemical Engineering from Sharif University of Technology (2008) His research interests span computational multiscale modeling, digital twin development, applied artificial intelligence, and optimization-based control of complex process networks. Dr. Pourkargar's work integrates process systems engineering with artificial intelligence to address challenging problems in chemical, biological, energy, and food systems. He develops intelligent frameworks for controlling complex process networks, designing cyber-physical architectures for smart manufacturing, and advancing system identification using machine learning and process data analytics. A significant aspect of his research involves physics-informed machine learning applied to cancer dynamics modeling and drug distribution in the human body. Dr. Pourkargar's publication record shows a strong focus on predictive modeling and control of chemical processes, particularly ammonia synthesis systems, polysilicon reactor systems, and food extrusion processes. His recent work increasingly incorporates machine learning techniques, especially transformer architectures and physics-informed approaches, applied to both traditional chemical processes and emerging areas like organ-on-a-chip systems for drug discovery. 2024 Carl R. Ice College of Engineering Outstanding Assistant Professor Award NSF EPSCoR Research Fellowship 2023 Kansas EPSCoR First Award AFOSR Faculty Fellowship Big XII Faculty Fellowship Robert F. Smith School Distinguished Junior Researcher Award from Cornell University (2017) O. Hugo Schuck Best Paper Award (2014) Dr. Pourkargar has successfully mentored numerous graduate students through their master's and doctoral research, with several receiving departmental and college-level awards. His research has been supported by significant grants from the National Science Foundation, Kansas EPSCoR, and K-State's Global Food Systems initiative. His lab has presented extensively at major conferences including AIChE Annual Meetings and American Control Conferences. The Intelligent Systems and Process Systems Laboratory (ISPSL) led by Dr. Pourkargar operates computational and experimental facilities in Durland Hall. The lab is expanding into robotic additive manufacturing and autonomous biomanufacturing, supported by research infrastructure grants. The group maintains active collaborations with the Johnson Cancer Research Center and the Terasaki Institute for Biomedical Innovation.

• Additive Manufacturing
• Computational Mechanics
• Topology Optimization
• +۴ مورد دیگر

Albert To is a Professor at the Swanson School of Engineering, University of Pittsburgh, where he holds the William Kepler Whiteford Professorship. He serves as Director of both the MOST-AM Consortium and the ANSYS Additive Manufacturing Research Laboratory. Since joining Pitt in 2008, he has advanced from assistant to associate (2014) and full professor (2019). Education: BS, MS, and PhD from UC Berkeley; MS from MIT Postdoctoral Research: Northwestern University with Wing Kam Liu Dr. To's primary research interests center around design optimization for additive manufacturing, multiscale methods, and computational mechanics. His work focuses on fast process modeling and topology optimization for metal additive manufacturing. He directs the ANSYS Additive Manufacturing Research Laboratory, which houses advanced metal 3D printers including EOS DMLS, Optomec LENS, and ExOne binder jetting systems. In 2016, he founded the MOST-AM Consortium, which now includes over 30 member companies and research labs collaborating on additive manufacturing research. His research has been consistently supported by major funding agencies including NASA, DOD, DOE, NSF, America Makes, and industry partners like ANSYS. The recent publications demonstrate a strong focus on addressing key challenges in metal additive manufacturing processes, particularly laser powder bed fusion and wire-arc directed energy deposition technologies. His work spans from fundamental process modeling to practical applications in materials science and mechanical engineering. NSF BRIGE Award (2009) Air Force Summer Faculty Fellowship (2009) Board of Visitors Faculty Award (2016) Carnegie Science Award (2018) Best Student Paper Award, 46th Acoustic Emission Working Group Meeting (2003) Dr. To has secured substantial research funding from government agencies and industry partners to advance additive manufacturing technologies. His MOST-AM Consortium facilitates collaboration between academia and industry, accelerating the translation of research findings into practical applications. He has advised numerous graduate students who have contributed to his extensive publication record in top journals. His laboratory at the University of Pittsburgh is equipped with state-of-the-art metal 3D printing systems, enabling both fundamental research and applied development in additive manufacturing. The MOST-AM Consortium provides a framework for industry collaboration, ensuring research addresses real-world challenges in the field.

Mauro Rodriguezمشاهده پروفایل

استادیار

• Computational Fluid Dynamics
• Multiphase Flows
• Viscoelasticity
• +۳ مورد دیگر

Mauro Rodriguez is an Assistant Professor of Engineering at Brown University's School of Engineering. His research focuses on computational fluid dynamics, multiphase flows, viscoelasticity, and high-performance computing. He holds a position at Barus & Holley 441 and can be reached at mauro_rodriguez@brown.edu. His research interests include studying fluid-structure interactions, cavitation bubble dynamics, and inertial effects in complex fluids. Rodriguez explores applications ranging from biomedical engineering (e.g., pulmonary alveolus deformation under ultrasound) to exascale computing for multiphysics simulations. His work often involves numerical modeling and rheometry techniques to characterize material properties. Recent publications highlight advancements in simulating cavitation phenomena, bubble collapse mechanics, and GPU-accelerated algorithms. Rodriguez’s research bridges theoretical fluid dynamics with practical engineering challenges, leveraging high-performance computing resources to tackle multiscale problems. No scientific awards or grants are explicitly listed in the provided materials. His advising activities and lab affiliations remain unspecified.

• Mechanical Metamaterials
• Origami-Inspired Structures
• Wave Propagation
• +۳ مورد دیگر

Charles Dorn is an Assistant Professor in the Department of Aeronautics and Astronautics at the University of Washington. His research focuses on structural mechanics, architected materials, and reconfigurable systems. Prior to UW, he was a Postdoctoral Fellow at ETH Zurich (2021–2024), and completed his PhD in Space Engineering at Caltech (2021), with additional degrees from Ecole Polytechnique and UW-Madison. Education: PhD, Space Engineering, California Institute of Technology, 2021 M.S., Multiphysics and Multiscale Modeling, Ecole Polytechnique, 2018 M.S., Space Engineering, Caltech, 2017 B.S., Engineering Mechanics, UW-Madison, 2016 Research Interests: His work bridges mechanics, computation, and design to create structures with extreme properties like wave steering and shape reconfiguration. Key areas include mechanical metamaterials, origami-inspired systems, and spatially graded metamaterials for vibration suppression. Recent projects explore inverse design methods and multi-configuration rigidity. Key Achievements: Developed ray-tracing tools for elastic waves in graded metamaterials Winner of 2018 R&D 100 Award for video-based vibration measurement techniques Collaborated with industry leaders like Prof. Sergio Pellegrino (Caltech) and Prof. Dennis Kochmann (ETH Zurich) Labs/Teams: Leads a research group at UW focused on advanced structural architectures and their applications across aerospace, robotics, and electronics.

• Porous Media
• Computational Mechanics
• Bayesian Inference
• +۳ مورد دیگر

Mina Karimi is a Postdoctoral Scholar Research Associate in the Department of Mechanical and Civil Engineering at California Institute of Technology (Caltech). She is part of the Bhattacharya group, advised by Professor Kaushik Bhattacharya. Her research focuses on computational mechanics, poromechanics, and Bayesian inference applied to porous media systems. Key areas include reactive flow modeling, multiscale simulations, and data-driven approaches for subsurface engineering challenges. Her work integrates advanced computational methods with geomechanical and materials science problems, emphasizing energy systems and environmental applications. Recent studies explore carbon sequestration mechanisms, chemo-poro-mechanical coupling, and high-dimensional parameter estimation using Bayesian frameworks. She also investigates crack-healing phenomena in shape memory alloy composites and develops accelerated micromechanical models for solute transport. Publications span topics from machine learning-enhanced groundwater modeling to Hessian-informed sampling techniques for high-dimensional inverse problems. Her research bridges theoretical developments with practical applications in subsurface energy storage, geological carbon sequestration, and material behavior under extreme conditions. Advising is conducted under the mentorship of Prof. Bhattacharya, with affiliations to the Resnick Sustainability Institute at Caltech. Current efforts emphasize computational tools for poromechanics and uncertainty quantification in complex multiphase systems.

• Fracture Mechanics
• Aerospace Materials
• Composite Materials
• +۶ مورد دیگر

Dr. Wieslaw K Binienda is a Professor of Civil Engineering at the University of Akron's College of Engineering and Polymer Science, where he has served since 1988. He co-directs the Gas and Turbine Research and Testing Laboratory. His expertise spans fracture mechanics, composite materials characterization, and computational methods like FEA and CFD. He has received prestigious awards including the NASA 'Turning Goals Into Reality Award' and ASCE's Richard R. Torrens Award for editorial leadership. Education: Ph.D., Mechanical Engineering, Drexel University (1987) M.S., Mechanical Engineering, Drexel University (1985) B.S./M.S., Motor Vehicles and Heavy Duty Machines, Warsaw Polytechnic University (1980) Research Interests: Focuses on advanced composite materials' mechanical behavior under extreme conditions, including high-energy impact, thermal cycling, and dynamic loading. Specializes in failure analysis using multiscale modeling and experimental validation. Active in aerospace applications like turbine durability and structural safety. Awards: NASA 'Turning Goals Into Reality Award' ASCE Aerospace Division 2010 Outstanding Professional Service Award ASCE 2013 Richard R. Torrens Award Fellow, American Society of Civil Engineers (ASCE) Lab & Collaborations: Leads the Gas and Turbine Research and Testing Laboratory, advancing turbine blade containment, impact analysis, and material testing. Collaborates with industry on aviation safety and composite material innovations.

Robert L. Jacksonمشاهده پروفایل

استاد

• Tribology
• Contact Mechanics
• Friction
• +۱۰ مورد دیگر

Robert L. Jackson is a Professor in the Department of Mechanical Engineering at Auburn University, College of Engineering. He serves as Editor-in-Chief of the ASME Journal of Tribology, highlighting his leadership in the field. His research focuses on tribology, contact mechanics, friction, wear, lubrication, and electrical contacts, with applications in machine design and electrified systems. His research interests include: Nano, Micro, and Macro Scale Contact Friction, Wear, and Lubrication (Tribology) Electrically Induced Bearing Damage Electrical Contacts and Lubricant Additives Surface Texturing and Nano-lubricants Multiscale/Multiphysics Modeling Design of Machine Components His recent publications reveal a strong trend in the performance of electric vehicle motor greases, electro-pitting, electrical discharge modeling, and mixed lubrication in rolling and sliding contacts. He investigates the role of nanoparticles, surface roughness, and thermal effects in tribological systems. His work spans experimental, analytical, and computational approaches, with a focus on real-world engineering applications such as EVs, connectors, and bearings. He actively mentors graduate students, including current advisees Loren Baugh, Jack Janik, and Sudip Saha. His past students have gone on to careers in industry and academia at organizations like Intel, Amazon, Tesla, and universities in China and India. He has collaborated extensively with researchers on topics ranging from articular cartilage biomechanics to nanocomposite lubricants and electrical connector reliability.

• Uncertainty Quantification
• Multiscale Simulation
• Machine Learning
• +۷ مورد دیگر

Dr. Yumeng Li is an Assistant Professor at the University of Illinois at Urbana-Champaign, Department of Industrial and Enterprise Systems Engineering. Prior to this, he was an Assistant Professor at Wichita State University and a Postdoctoral Researcher at Vanderbilt University. Ph.D., Aerospace Engineering, Virginia Tech (2014) M.S., Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology (2009) B.S., Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology (2007) Dr. Li’s research focuses on uncertainty quantification in multiscale simulation, physics-based machine learning, surrogate modeling, and integrated computational material engineering. His work bridges computational modeling with experimental validation in material design and digital manufacturing. His publications span topics like corrosion modeling, additive manufacturing, battery systems, polymer crystallization, and life cycle assessment. Recent works emphasize multi-physics simulations, adaptive machine learning, and sustainable material design. Dr. Li teaches courses including SE 310 (Design of Structures & Mechanisms), SE 410 (Component Design), and SE 598 (Machine Learning in Material Design).

Gianluca Cusatisمشاهده پروفایل

استاد

• Civil Engineering
• Materials Science
• Concrete Mechanics
• +۷ مورد دیگر

Gianluca Cusatis is a Professor of Civil and Environmental Engineering at Northwestern University, with a courtesy appointment in Mechanical Engineering. He leads research in multiscale mechanics of infrastructure materials, focusing on constitutive modeling of concrete, cementitious composites, and wood. His work bridges computational modeling, experimental validation, and material innovation. He holds a Ph.D. and Laurea from Politecnico di Milano, Italy. Research interests include quasi-brittle material behavior, 3D concrete printing, infrastructure durability (e.g., alkali-silica reaction), and wood mechanics. He directs the Multiscale Mechanics of Infrastructure Materials (M2IM) group, collaborating with institutions like the University of Maine. Key achievements include pioneering the Lattice Discrete Particle Model (LDPM) and advancing understanding of cement hydration expansion via the Shard Test. Awarded ASCE/EMI Fellow, Cusatis serves on professional boards (ASCE Engineering Mechanics Institute, ACI Committees). His lab integrates advanced facilities for structural testing, 3D printing, and environmental control. Major projects include sustainable timber structure design, Martian concrete for extraterrestrial habitats, and mesoscale modeling of composite materials.

Lucy Zhangمشاهده پروفایل

استاد

• Computational Mechanics
• Fluid-Structure Interactions
• Computational Biomechanics
• +۳ مورد دیگر

Lucy Zhang is a Professor and Associate Dean for Research Innovations, Partnerships, and Workforce Development at Rensselaer Polytechnic Institute (RPI). She holds a Ph.D. from Northwestern University and previously served as an assistant professor at Tulane University before Hurricane Katrina. Her primary affiliation is in the Department of Mechanical, Aerospace, and Nuclear Engineering, with a secondary affiliation in Biomedical Engineering within the School of Engineering. Dr. Zhang’s research focuses on computational mechanics, particularly fluid-structure interactions, computational biomechanics, and multiphysics-multiscale simulations. Her work bridges engineering and biomedical applications, including aerodynamics, cardiovascular modeling, and material corrosion analysis. Notable contributions include the development of the OpenIFEM software framework for fluid-structure interaction simulations and studies on respiratory droplet dynamics during pandemics. Her recent research integrates machine learning with fluid dynamics solvers, explores material degradation under extreme conditions, and addresses challenges in neurorehabilitation modeling through NSF-funded initiatives. Dr. Zhang collaborates across disciplines, leveraging high-performance computing and numerical methods to solve complex engineering and biomedical problems. Her NSF grant on fibrous material performance under real-world conditions underscores her commitment to translational research.

Marta D'Eliaمشاهده پروفایل

استاد مدعو

• Scientific Machine Learning
• Nonlocal Modeling
• Fractional Calculus
• +۴ مورد دیگر

Marta D'Elia is an Adjunct Professor at Stanford's Institute for Computational and Mathematical Engineering (ICME), specializing in Scientific Machine Learning and nonlocal modeling. Her research develops data-driven algorithms for multiscale/multiphysics simulations, integrating numerical analysis, uncertainty quantification, and fractional calculus. Core applications include subsurface transport, turbulence modeling, image processing, and materials science. She leads innovations in nonlocal operator regression, physics-informed neural networks, and fractional Laplacian formulations. Current work focuses on embedded machine learning for constitutive modeling, Bayesian uncertainty frameworks, and computational homogenization. D'Elia pioneered approaches for nonlocal-to-local model coupling and fractional Helmholtz decompositions, advancing simulation capabilities for anomalous transport phenomena. Her Ph.D. in Applied Mathematics (Emory University) underpins rigorous mathematical foundations, while collaborations with national labs address high-performance computing implementations. Research contributes to open-source scientific software and computational mathematics education through ICME courses on numerical methods and machine learning.