School of Engineering
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Professor of Mechanical Engineering
Current Research and Scholarly InterestsThe research interests of Professor Darve span across several domains, including machine learning for engineering, surrogate and reduced order modeling, stochastic inversion, anomaly detection for engineering processes and manufacturing, numerical linear algebra, high-performance and parallel computing, and GPGPU.
Sr Research Engineer, Mechanical Engineering
University of Toronto Physics B.S (1978)
University of Toronto Aerospace Sciences M.Sc. (1980)
York University Physics Ph.D. (1986)
1986-present Senior Research Engineer, Mechanical Engineering Department
Dr. Davidson’s research interests span the fields of gas dynamics and combustion kinetics. During his tenure at Stanford University he investigated the chemical kinetics of combustion using a wide array of optical and laser-based diagnostic methods and advanced the use of these diagnostics in shock tubes. He co-authored over 350 research publications with his students and Professor Ronald Hanson. He continues to advise and mentor the graduate students who use shock tubes in the High Temperature Gasdynamics Laboratories. An overview of the shock tube studies performed at Stanford under Prof. Hanson’s and Dr. Davidson’s supervision can be found in the report entitled “Fundamental Kinetics Database Utilizing Shock Tube Measurements” available at http://purl.stanford.edu/kb621cw6967.
He claims he is now retired, but apparently, he is still working.
Scott L. Delp, Ph.D.
Director, Wu Tsai Human Performance Alliance at Stanford, James H. Clark Professor in the School of Engineering, Professor of Bioengineering, of Mechanical Engineering and, by courtesy, of Orthopaedic Surgery
Current Research and Scholarly InterestsExperimental and computational approaches to study human movement. Development of biomechanical models to analyze muscle function, study movement abnormalities, design medical products, and guide surgery. Imaging and health technology development. Discovering the principles of peak performance to advance human health. Human performance research. Wearable technologies, video motion capture, and machine learning to enable large-scale analysis.
Masters Student in Mechanical Engineering, admitted Autumn 2022
BioNick Delurgio is a graduate student in Mechanical Engineering at Stanford University. Nick previously received his B.S. in Aerospace Engineering from the University of Texas at Austin, where he developed in interest in Guidance, Navigation, and Control (GNC) for Aerospace applications. At Stanford, Nick is pursuing his interest in GNC through Distributed Space Systems (DSS) research, advised by Professor Simone D'Amico. Nick's research involves the development of dynamics, guidance, and control strategies for RPOD missions in eccentric orbits, as well as creating reduced order modeling techniques to simplify formation flying mission design.