Mechanical Engineering
Showing 61-80 of 592 Results
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Wei Cai
Professor of Mechanical Engineering and, by courtesy, of Materials Science and Engineering
BioPredicting mechanical strength of materials through theory and simulations of defect microstructures across atomic, mesoscopic and continuum scales. Developing new atomistic simulation methods for long time-scale processes, such as crystal growth and self-assembly. Applying machine learning techniques to materials research. Modeling and experiments on the metallurgical processes in metal 3D printing. Understanding microstructure-property relationship in materials for stretchable electronics, such as carbon nanotube networks and semiconducting elastomers.
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Mark A. Cappelli
Professor of Mechanical Engineering
BioProfessor Cappelli received his B.Sc. degree in Physics (McGill, 1980), and M.A.Sc and Ph.D. degrees in Aerospace Sciences (Toronto, 1983, 1987). He joined Stanford University in 1987 and is currently a Professor in the Department of Mechanical Engineering and Co-Director of the Engineering Physics Program. He carries out research in applied plasma physics with applications to a broad range of fields, including space propulsion, aerodynamics, medicine, materials synthesis, and fusion.
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Ovijit Chaudhuri
Professor of Mechanical Engineering and, by courtesy, of Bioengineering
Current Research and Scholarly InterestsWe study the physics of cell migration, division, and morphogenesis in 3D, as well cell-matrix mechanotransduction, or the process by which cells sense and respond to mechanical properties of the extracellular matrices. For both these areas, we use engineered biomaterials for 3D culture as artificial extracellular matrices.
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Helen L. Chen
Research Scientist
BioHelen L. Chen is a research scientist in the Designing Education Lab in the Department of Mechanical Engineering at Stanford University. She holds an undergraduate degree in communication from UCLA and a PhD in communication with a minor in psychology from Stanford. Helen is a board member for the Association for Authentic, Experiential and Evidence-Based Learning (AAEEBL) and is a co-author of Documenting Learning with ePortfolios: A Guide for College Instructors and co-executive editor of the International Journal of ePortfolio. She works closely with the Association of American Colleges and Universities and consults with institutions on general education redesign, authentic assessment approaches, design thinking, and personal branding and ePortfolios. Helen's current research and scholarship focus on engineering and entrepreneurship education; the pedagogy of portfolios and reflective practice in higher education; and redesigning how learning is recorded and recognized in traditional transcripts and academic credentials.
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Jianqing Chen
Affiliate, Mechanical Engineering - Design
Visiting Scholar, Mechanical Engineering - DesignBioI work on robotic remote control and manipulation systems utilizing reinforcement learning (RL). My focus is on developing RL-based algorithms that enable robots to learn optimal control strategies for tasks such as navigation, object manipulation, and interaction with dynamic environments. By training robots through trial and error, these systems continuously improve their performance, adapting to new situations and enhancing autonomous control. The goal is to achieve more efficient and precise robot behavior in real-world applications.
I worked over 7 years in investment and asset management. I lead 280 Capital, a Multi-Family Office with over $600M under management, focus on digital assets and cutting-edge technologies.
I have a strong background in AI, Storage, Encryption etc. I am in the great team to do designs in the latest enterprise-grade storage SSD system chips, from 16nm to 7nm, at Broadcom and SK Hynix. I also led the construction and optimization of large-scale biodata software and hardware data architectures at Roche to support scalable advanced DNA sequencing computations. I hold multiple U.S. patents, using various methods such as deep learning and AI to improve storage and computing efficiency.
