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Dong QianView profile

Professor

• Nonlinear finite element methods
• Fatigue and life prediction
• Surface engineering
• +8 more

Dr. Dong Qian is a Professor in the Department of Mechanical Engineering at the University of Texas at Dallas (UTD), serving as Associate Department Head for Graduate Programs. He holds a Ph.D. in Mechanical Engineering from Northwestern University (2002), with prior academic roles at the University of Cincinnati (2002–2012) and UTD since 2012. His research focuses on computational mechanics, multiscale methods, nanomechanics, and surface engineering. Key contributions include work on fatigue life prediction, laser shock peening, and biomimetic materials. Education: Ph.D. in Mechanical Engineering, Northwestern University, 2002 M.S. in Civil Engineering, University of Missouri-Columbia, 1998 B.S. in Bridge Engineering, Tongji University, China, 1994 Research Interests: Nonlinear finite element and meshfree methods Multiscale/multiphysics modeling of nanomaterials and bio-inspired systems Fatigue analysis and surface engineering Machine learning applications in computational mechanics Recent Research Trends: Dr. Qian's publications emphasize advanced manufacturing processes (e.g., laser shock peening, additive manufacturing), energy-efficient materials, and bio-inspired composites. His work bridges computational models with experimental validation, addressing challenges in thermal stress, fatigue, and material failure mechanisms. Awards: University of Cincinnati Distinguished Researcher Award (2012) ICACM Young Investigator Award (2011) Multiple ASEE Air Force Fellowships Grants & Service: Active in editorial roles for journals like Computational Mechanics . Served on NSF review panels and organized international conferences on computational mechanics. Supervises a vibrant research group with over 30 alumni in academia and industry. Labs & Teams: Leads research on advanced materials and manufacturing at UTD, collaborating with industry partners like Caterpillar and Siemens. Current projects include elastocaloric heat pumps and nanocrystalline surface modification.

Tamar ShinarView profile

Associate Professor

• Physics-Based Animation
• Fluid-Structure Interaction
• Biological Cell Modeling
• +3 more

Tamar Shinar is an Associate Professor in the Computer Science & Engineering Department at the University of California - Riverside (UCR). She is also affiliated with the Interdisciplinary Center for Quantitative Modeling in Biology. Her research focuses on physics-based animation, fluid-structure interaction, and computational modeling of biological systems. She teaches courses such as Scientific Computing, Computer Graphics, and Physics-Based Simulation, demonstrating expertise in both theoretical and applied aspects of computational science. Shinar's work bridges computer graphics and computational biology, with notable contributions to fluid dynamics simulations, deformable body modeling, and microtubule-based motion in biological systems. Her publications span topics like implicit surface reconstruction using neural networks, conservative discretization methods, and partitioned fluid-solid coupling algorithms. She actively mentors PhD and MS students and engages in outreach programs to promote STEM education.

Gideon LyngdohView profile

Assistant Professor

• Structural Engineering
• Computational Mechanics
• Multiscale Modeling
• +4 more

Gideon Lyngdoh serves as Assistant Professor of Civil Engineering in the School of Engineering at George Fox University, joining the faculty in 2022 after completing his doctoral studies. His academic foundation spans international institutions with degrees from premier engineering programs in India and the United States. His educational background includes: PhD in Civil Engineering (Structural Engineering), University of Rhode Island Master of Technology in Structural Engineering, Indian Institute of Technology Kanpur, India (2017) Bachelor of Civil Engineering, Jabalpur Engineering College, India (2014) Dr. Lyngdoh's research integrates advanced computational methodologies with structural materials science, specializing in molecular dynamics simulations, finite element analysis, and machine learning applications for cementitious polymers and concrete. His work bridges multiscale modeling and multiphysics phenomena to innovate material design and structural analysis techniques. With extensive pedagogical experience gained through graduate teaching assistantships (2015-2021) at the University of Rhode Island, he brings both research depth and instructional expertise to George Fox University. His doctoral research included summer internships at URI's Multiscale and Multiphysics Mechanics of Materials Research Laboratory, where he investigated computational mechanics applications for construction materials.

Soheil SoghratiView profile

Professor

• Computational Solid Mechanics
• Deep Learning Algorithms
• Cancer Engineering
• +7 more

Soheil Soghrati is a Professor at The Ohio State University (OSU) in the Departments of Mechanical and Aerospace Engineering and Materials Science and Engineering. He earned his Ph.D. in Structural Engineering with a Minor in Computational Science in Engineering from the University of Illinois at Urbana-Champaign in May 2013 and joined OSU as an Assistant Professor that same year. After promotion to Associate Professor with tenure in 2019 and Full Professor in 2023, he also serves on the steering board of the Simulation Innovation and Modeling Center (SIMCenter) and directs the Automated Computational Mechanics Laboratory (ACML). Education: Ph.D., Structural Engineering with Minor in Computational Science in Engineering, University of Illinois at Urbana-Champaign (2013) His research focuses on computational solid mechanics for materials with complex microstructures, utilizing advanced algorithms in microstructure reconstruction, mesh generation, and deep learning. Specific areas include: AI-driven DLD3 technique for structural analysis Microstructure modeling in composites and biomedical applications Cancer engineering for bone fracture risk prediction and radiation effects Phase field and meshfree methods for corrosion and fracture simulation Digital manufacturing in automotive applications Recent publications (2022-2025) highlight his work on deep learning integration with finite element methods, medical simulations for cancer patients, and innovative meshing frameworks. These studies span computational modeling of fiber composites, corrosion damage, and digital twins for biomedical and aerospace systems. Research at ACML is funded by federal agencies including the National Science Foundation, Air Force Office of Scientific Research, and Department of Defense, as well as industry partners like Honda R&D Americas, Ford, and Owens Corning. The lab specializes in AI-assisted simulations, multiscale material design, and virtual reality applications for complex mechanical problems.

Mojdeh RasoulzadehView profile

Associate Professor

• Applied & Computational Mathematics
• Multiphysics Processes in Porous Media
• Numerical Simulation of Coupled Processes
• +4 more

Mojdeh Rasoulzadeh is an Associate Professor in the Department of Mathematics at The University of Alabama. Her academic journey includes a Ph.D. in Mechanics and Energetics from the University of Lorraine, France, and prior roles as Assistant Professor (2017-2024) and industry research engineer. Employment History Associate Professor, The University of Alabama (2024-present) Assistant Professor, The University of Alabama (2017-2024) Research Engineer, Ecole des Ponts (2016-2017) Post-Doctoral Researcher, Schlumberger SPRC (2015) Post-Doctoral Researcher, CNRS (2012-2014) Research Focus : Rasoulzadeh specializes in multiphysics processes in complex media, with expertise in coupled hydro-mechanical-chemical simulations of porous media, probabilistic fluid-solid-particle modeling, and subsurface flow dynamics. Current projects examine microplastic fiber transport, anomalous transport in multiscale systems, and underground hydrogen storage. Awards & Grants : Mentored students received national recognition (PME Speaker Award, Randall Award). Secured multiple research grants (Alabama Water Institute, Diversity Committee, OVPRED) and industry accolades for pedestrian safety and engineering design. Teaching & Mentorship : Actively recruits graduate and undergraduate researchers. Advised students in topics like nonlocal transport in layered media and microplastic filament deformation.

Yang LuView profile

Associate Professor

• Computational Multiscale/Multiphysics Modeling
• Nanotechnology Application in Sustainable Construction
• Service Life Prediction of Durable Cementitious Materials
• +2 more

Dr. Yang Lu (P.E.) is an Associate Professor of Civil Engineering at Boise State University, College of Engineering. He obtained his M.S. from Tsinghua University and Ph.D. from Virginia Tech's Department of Civil and Environmental Engineering. His research focuses on Computational Multiscale/Multiphysics Modeling and Nanotechnology Application in Sustainable Construction , with applications in asphalt concrete, cementitious materials, and energy-harvesting infrastructure. He has published over 50 peer-reviewed papers. Key research areas include microstructural crack modeling, chemo-mechanical degradation, thermography for defect inspection, and machine learning in resilient infrastructure design. His work aligns with UN Sustainable Development Goals for sustainable cities and climate action. Scientific Awards NIST Outstanding Associate Award Dean's Pratt Fellowship (2006) NIST-ARRA Fellowships (2010, 2012) Best Paper Award, COMSOL Conference (2012) ARRA Travel Fund Program Award (2012) Dr. Lu leads the Micromechanics and Smart Infrastructure Group (MMSIG) and plans to establish a Lab for Sustainable Structures and Materials focusing on computational microstructural modeling and climate-resilient infrastructure.

Mahmood MamivandView profile

Associate Professor

• Materials Science
• Mechanical Engineering
• Computational Materials Engineering
• +6 more

Dr. Mahmood Mamivand is an Associate Professor in the Department of Mechanical and Biomedical Engineering at Boise State University, where he has been since 2017. He also serves as an Adjunct Professor in the Department of Materials Science and Engineering and directs the Computational Materials Engineering Lab. His research focuses on accelerating materials design through multiscale multiphysics models, particularly in phase transformations, nanoparticle growth, and performance under extreme conditions. Education: PhD in Mechanical Engineering from Mississippi State University (recipient of Distinguished Graduate Scholar award). Research interests include phase field modeling , machine learning applications in materials science , and computational fracture mechanics . Recent work integrates deep learning and predictive neural networks to analyze microstructure evolution and material properties. His projects are funded by the National Science Foundation (NSF), including a CAREER award for developing rare-earth-free permanent magnets and a CyberTraining grant for data science in materials research. Selected trends from his publications highlight machine learning for inverse design , spatiotemporal modeling , and phase transformation analysis . He has received accolades for highly cited papers and poster presentations, with a focus on zirconia, shape memory ceramics, and nanomaterials. Scientific Awards: Graduate Research Assistantship (CAVS, 2011) Distinguished Graduate Scholar (2013) Top 5 Highly Cited Papers (2014) ScienceDirect Top 25 Hottest Articles (2024) Dr. Mamivand collaborates with institutions on topics like interface science and permanent magnets . He mentors students through NSF-funded undergraduate research programs and leads projects on data-driven materials discovery and predictive modeling .

Daniel R. ReynoldsView profile

Professor

• Scientific Computation
• Time Integration Methods
• Parallel Algorithms
• +2 more

Daniel R. Reynolds is a Professor at Southern Methodist University (joining UMBC in 2025) specializing in large-scale scientific computation. He develops mathematically rigorous algorithms for multiphysics problems in climate science, fusion energy, cosmology, and materials science. His research creates parallel time integration methods and algebraic solvers that ensure accuracy, stability, and scalability for complex simulations, bridging mathematical theory with computational practice. His publications consistently advance time integration techniques for differential-algebraic systems, with recent work emphasizing multirate methods for tokamak plasma turbulence and Earth system models. He leads software development for the SUNDIALS suite and collaborates on DOE/NSF-funded projects including 'Adaptive Multirate Time Integration in the ARKODE Library'. Reynolds teaches graduate courses in computational mathematics and mentors PhD students in algorithm development.

Lianyi ChenView profile

Professor

• Advanced Manufacturing
• Additive Manufacturing
• Materials Science
• +5 more

Professor Lianyi Chen is a faculty member in the Department of Mechanical Engineering at University of Wisconsin-Madison, affiliated with the College of Engineering. He holds the Kuo K. & Cindy F. Wang Professorship (2024) and Charles Ringrose Professorship (2021). His research focuses on advanced manufacturing, particularly additive manufacturing, using in-situ/operando characterization techniques such as synchrotron X-ray imaging and thermal imaging to study material behavior during processing. Education: PhD 2009, Zhejiang University; BE 2004, Yanshan University. Research interests include: Multiscale multimodal characterization for processing-structure-property relationships Advanced materials design (metal matrix nanocomposites, materials for extreme environments) Autonomous metals design integrating AI and additive manufacturing Recent articles highlight advancements in defect mitigation, nanoparticle-enabled energy efficiency, and real-time process monitoring in additive manufacturing. His work bridges materials science with cutting-edge manufacturing technologies. Teaches courses including Manufacturing: Metals and Automation (M E 311), Interdisciplinary Experiential Design Projects (M E 351/352), and PhD/Master's research supervision.

Adrian SanduView profile

Professor

• Numerical Methods
• High Performance Computing
• Uncertainty Quantification
• +5 more

Adrian Sandu is a Professor in the Department of Computer Science at Virginia Tech, affiliated with the College of Engineering. He leads the Computational Science Laboratory and specializes in numerical methods, high-performance computing, uncertainty quantification, and sensitivity analysis. His education includes a Ph.D. and M.S. in Applied Mathematical and Computational Sciences from the University of Iowa, and an M.S. in Electrical Engineering from Technical University Bucharest. He has held roles at Michigan Tech and Courant Institute before joining Virginia Tech in 2003. His research focuses on advancing computational techniques for science and engineering applications, alongside educating future computational scientists. Education: Ph.D. (1997, University of Iowa), M.S. (1997, University of Iowa), Engineer Diploma (1990, Technical University Bucharest). Research Interests: Numerical methods, parallel computing, data assimilation, and computational engineering. Awards: ACM Distinguished Scientist, Virginia Tech College of Engineering Fellow, NSF CAREER Award. Adrian Sandu teaches courses such as CS 3414 (Numerical Methods), CS 4234 (Parallel Computation), and CS 5045 (Computation for the Life Sciences). His work integrates advanced computational algorithms with real-world applications, emphasizing innovation and interdisciplinary collaboration.

Dehao LiuView profile

Assistant Professor

• Advanced Manufacturing
• Materials Science
• Multiscale Modeling
• +3 more

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.

• Computational Mechanics
• Multiscale Methods
• Fluid Mechanics
• +7 more

Arif Masud is the John and Eileen Blumenschein Professor of Mechanics and Computations at the University of Illinois at Urbana-Champaign, holding joint appointments in Civil and Environmental Engineering, Aerospace Engineering, Mechanical Science and Engineering, and Biomedical and Translational Sciences at the Carle-Illinois College of Medicine. His research focuses on computational mechanics, including stabilized and variational multiscale methods, turbulence modeling, biomechanics, and additive manufacturing. He has held academic positions since 1994, including roles as Associate Professor and Professor at UIUC and UIC. Education: PhD in Computational Mechanics (Stanford, 1993), MS in Structural Engineering (Stanford, 1987), BS in Civil Engineering (University of Engineering & Technology, Lahore, 1986). Research Interests: Development of advanced numerical methods for fluid and solid mechanics, biofluid dynamics, and physics-constrained machine learning. Key areas include turbulence closure models, multiphase flows, and multiscale frameworks for coupled chemo-thermo-mechanical problems. His work emphasizes mathematical rigor and scalability for high-performance computing. Professional Contributions: Editorships in journals like International Journal for Multiscale Computational Engineering , leadership roles in societies (SES, EMI, ASME), and organization of major conferences (USNCCM 2021, FEF 2019). Awards include Fellowships from USACM, IACM, ASME, and the 2019 G.I. Taylor Medal. Grants and Labs: Active in federally funded research, leading the Computational Mechanics Lab. His work spans academic-industrial collaborations, with a focus on simulation-based design and multiscale modeling.

Eric DarveView profile

Professor

• Machine Learning
• Numerical Linear Algebra
• High-Performance Computing
• +5 more

Eric Darve is a Professor of Mechanical Engineering and Director of the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. His research focuses on machine learning for science/engineering, numerical linear algebra, high-performance computing, and GPU-accelerated algorithms. He holds a PhD in Applied Mathematics from Pierre et Marie Curie University (2009), and completed postdoctoral work at Stanford/NASA Ames before joining Stanford’s faculty in 2001. Education: PhD, Applied Mathematics, Paris VI University (1999) MS, Applied Mathematics, Paris IX University (1994) BS, Mathematics & Physics, Paris VI University (1993) Research interests include: Machine learning for scientific applications Surrogate/Reduced Order Modeling Anomaly detection in engineering systems Parallel computing and numerical algorithms Physics-informed learning machines Awards include: H. Julian Allen Award (NASA, 2010) Habilitation à Diriger des Recherches (France, 2007) Leslie Fox Prize in Numerical Analysis (2001) James H. Clark Faculty Scholar (2001) Led development of scalable linear solvers and hybrid physics-data approaches. Active in computational engineering education and interdisciplinary research initiatives through ICME.

Jiaoyan LiView profile

Assistant Professor

• Multiscale modeling
• Computational mechanics
• High performance computing
• +3 more

Jiaoyan Li is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University at Buffalo , part of the School of Engineering and Applied Sciences . Her research focuses on multiscale modeling, computational mechanics, and high-performance computing with applications in nano-, bio-, and energy systems. She holds a PhD in Solid Mechanics and Materials Science from George Washington University (2015), an MS from Xi’an Jiaotong University (2009), and a BS from Dalian University of Technology (2006). Education: BS: Engineering Mechanics, Dalian University of Technology (2006) MS: Solid Mechanics, Xi’an Jiaotong University (2009) PhD: Solid Mechanics and Materials Science, George Washington University (2015) Her research interests span nanoconfinement effects on fluids, mesoscopic simulation techniques, and energy systems optimization . Recent work includes studies on hydrocarbon flow in nanochannels, tumor growth modeling, and graphene topological design. She has received prestigious awards such as the ICTAM Early Career Fellowship (2016) and multiple research excellence prizes at George Washington University. Dr. Li’s publications highlight advancements in multiscale methods, including finite element analysis for fracture mechanics and micromorphic theories. She has secured grant support from the NSF Summer Institute on Nano Mechanics and Materials (2012). Her research bridges computational innovation with practical applications in energy, healthcare, and nanotechnology.

Baoxing XuView profile

Associate Professor

• Engineering Mechanics
• Nanomanufacturing
• Wearable Electronics
• +5 more

Dr. Baoxing Xu is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of Virginia. He holds a PhD in Mechanics and Materials from Columbia University (2012) and was a Beckman Postdoctoral Fellow at the University of Illinois at Urbana-Champaign (2012–2014). His research focuses on multiscale-multiphysics mechanics-driven design of functional materials for wearable electronics and biomedical devices, including nanomanufacturing, soft-hard material integration, and thermal transport in nanomaterials. His research group explores applications such as transfer printing in liquids, solution shearing, and bioinspired devices. Key areas include nanomechanics under extreme conditions, nanofluidics, and thermal transport in mechanically deformed materials. Education: PhD in Mechanics and Materials, Columbia University (2012) Dr. Xu’s work has been recognized with awards including the Young Investigator Medal (SES, 2023), Sia Nemat-Nasser Early Career Award (ASME, 2020), and a YIP Award from the Office of Naval Research (2020). He teaches courses like Engineering Mechanics-Statics and The Theory of Elasticity. His lab, the Xu Research Group, pioneers innovations in nanofluidics, wearable sensors, and energy-dissipative materials.