Mechanical Engineering
Showing 161-180 of 613 Results
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Sean Follmer
Associate Professor of Mechanical Engineering and, by courtesy, of Computer Science
Current Research and Scholarly InterestsHuman Computer Interaction, Haptics, Robotics, Human Centered Design
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Lorenza Garau Paganella
Postdoctoral Scholar, Mechanical Engineering
BioLorenza was born in Italy in 1997 and is currently a postdoctoral fellow at Stanford University with Prof. Chaudhuri, supported by an SNSF fellowship. Her research focuses on engineering biomaterials to investigate cell–extracellular matrix interactions and mechanotransduction in 2D and 3D cell cultures, aiming to advance biomedical understanding of tissue remodeling and disease.
Lorenza obtained her PhD (2024) in Mechanical and Process Engineering from ETH Zurich, where she developed hydrogel scaffolds and protocols for protein isolation to study cell behavior in engineered microenvironments.
Before her PhD, Lorenza completed her MSc at ETH Zurich and BSc at University of Trieste in Process Engineering, graduating both cum laude. During this time her focus was on biomaterials for drug delivery which she complemented with an internship in Roche. She has worked in interdisciplinary teams combining engineering and biology and is motivated by research that bridges fundamental science with clinical impact. -
J. Christian Gerdes
Professor of Mechanical Engineering, Emeritus
BioChris Gerdes is a Professor Emeritus of Mechanical Engineering at Stanford University. His laboratory studies how cars move, how humans drive cars and how to design future cars that work cooperatively with the driver or drive themselves. Vehicles in the lab include X1, a student-built electric, steer-by-wire test vehicle; Takumi, a modified Toyota Supra capable of autonomous drifting in tandem with another car; and Marty, the electrified, automated, drifting DeLorean. Chris' interests in vehicle safety extend to ethics and government policy, having helped to develop the US Federal Automated Vehicle Policy while serving as the first Chief Innovation Officer of the US Department of Transportation.
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Siegfried Glenzer
Professor of Photon Science and, by courtesy, of Mechanical Engineering
Current Research and Scholarly InterestsPlease see our website for detailed information: https://heds.slac.stanford.edu
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Abeynaya Gnanasekaran
Research Advisor, Mechanical Engineering - Mechanics and Computation
BioI am a PhD student in the Institute for Computational and Mathematical Engineering. My research interests lie in Numerical Linear Algebra and Parallel Computing. I'm working with Prof. Eric Darve on developing fast algorithms for general linear systems. I obtained my B.Tech (Honors) in Chemical Engineering from Indian Institute of Technology Madras, India.
I was born and brought up in Neyveli, an industrial town in south India. I enjoy listening to Indian music and reading novels. -
Kenneth Goodson
Vice Provost for Graduate Education and Postdoctoral Affairs, Davies Family Provostial Professor, and Professor, by courtesy, of Materials Science and Engineering
Current Research and Scholarly InterestsProf. Goodson’s Nanoheat Lab studies heat transfer in electronic nanostructures, microfluidic heat sinks, and packaging, focussing on basic transport physics and practical impact for industry. We work closely with companies on novel cooling and packaging strategies for power devices, portables, ASICs, & data centers. At present, sponsors and collaborators include ARPA-E, the NSF POETS Center, SRC ASCENT, Google, Intel, Toyota, Ford, among others.
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Catherine Gorle
Associate Professor of Civil and Environmental Engineering and, by courtesy, of Mechanical Engineering
Current Research and Scholarly InterestsGorle's research focuses on the development of predictive flow simulations to support the design of sustainable buildings and cities. Specific topics of interest are the coupling of large- and small-scale models and experiments to quantify uncertainties related to the variability of boundary conditions, the development of uncertainty quantification methods for low-fidelity models using high-fidelity data, and the use of field measurements to validate and improve computational predictions.
