School of Engineering
Showing 11-20 of 49 Results
J. Edward Carryer
BioEd Carryer graduated from the Illinois Institute of Technology in 1975 with a BSE as a member of the first graduating class of the Education and Experience in Engineering Program. This innovative project-based learning program taught him that he could learn almost anything that he needed to know and set him on a path of lifelong learning. That didn’t, however, keep him from going back to school.
Upon completion of his Master’s Degree in Bio-Medical Engineering at the University of Wisconsin Madison in 1978, he was seduced by his love of cars, and instead of going into medical device design, he went to work for Ford on the 1979 Turbocharged Mustang. In later programs at Ford, he got to apply the background that he had gained in electronics and microcontrollers during his graduate work to the 1983 Turbocharged Mustang and Thunderbird and the 1984 SVO Mustang. After leaving Ford, Ed worked on the design and implementation of engine control software for GM and on a stillborn development program to put a turbocharged engine into the Renault Alliance at AMC before deciding to return once again to school. At Stanford University, he did research in the engine lab and earned his PhD in 1992.
While working on his PhD, Ed got involved in teaching the graduate course sequence in mechatronics that is known at Stanford as Smart Product Design. He took over teaching the courses first part time in 1989, then full time after completing his PhD. In teaching mechatronics, Ed seems to have found his calling. The integration of mechanical, electronic, and software design with teaching others how to use all of this to make new products hits all his buttons. He is currently a Consulting Professor and the Director of the Smart Product Design Lab (SPDL). He teaches graduate courses in mechatronics in the Mechanical Engineering department and an undergraduate course in mechatronics in the Electrical Engineering department.
Since 1984, Ed has maintained a consultancy focused on helping firms apply electronics and software in the creation of integrated electromechanical solutions (in 1984, almost no one was using the term mechatronics).The projects that he has worked on include an engine controller for an outboard motor manufacturer, an automated blood gas analyzer, a turbocharger boost control system for a new type of turbocharger, and a heated glove for arctic explorers. His most recent project involved using ZigBee radios and local structural model evaluation to create a wireless network of intelligent sensors to monitor and evaluate the structural health of buildings and transportation infrastructure.
BioCarissa Carter is the Director of Teaching + Learning at the Stanford d.school. In this role she guides the development of the d.school’s pedagogy, leads its instructors, and shapes its class offering. She teaches courses on the intersection of data and design, design for climate change, and maps and the visual sorting of information.
Dennis R Carter
Professor of Mechanical Engineering, Emeritus
Current Research and Scholarly InterestsProfessor Carter studies the influence of mechanical loading upon the growth, development, regeneration, and aging of skeletal tissues. Basic information from such studies is used to understand skeletal diseases and treatments. He has served as President of the Orthopaedic Research Society and is a Fellow of the American Institute for Medical and Biological Engineering.
Associate Professor of Chemistry and, by courtesy, of Chemical Engineering
Current Research and Scholarly InterestsOur research program integrates chemistry, biology, and physics to investigate the assembly and function of macromolecular and whole-cell systems. The genomics and proteomics revolutions have been enormously successful in generating crucial "parts lists" for biological systems. Yet, for many fascinating systems, formidable challenges exist in building complete descriptions of how the parts function and assemble into macromolecular complexes and whole-cell factories. We are inspired by the need for new and unconventional approaches to solve these outstanding problems and to drive the discovery of new therapeutics for human disease.
Our approach is different from the more conventional protein-structure determinations of structural biology. We employ biophysical and biochemical tools, and are designing new strategies using solid-state NMR spectroscopy to examine assemblies such as amyloid fibers, bacterial cell walls, whole cells, and biofilms. We would like to understand at a molecular and atomic level how bacteria self-assemble extracellular structures, including functional amyloid fibers termed curli, and how bacteria use such building blocks to construct organized biofilm architectures. We also employ a chemical genetics approach to recruit small molecules as tools to interrupt and interrogate the temporal and spatial events during assembly processes and to develop new strategies to prevent and treat infectious diseases. Overall, our approach is multi-pronged and provides training opportunities for students interested in research at the chemistry-biology interface.
Professor of Aeronautics and Astronautics
BioProfessor Chang's primary research interest is in the areas of multi-functional materials and intelligent structures with particular emphases on structural health monitoring, intelligent self-sensing diagnostics, and multifunctional energy storage composites for transportation vehicles as well as safety-critical assets and medical devices. His specialties include embedded sensors and stretchable sensor networks with built-in self-diagnostics, integrated diagnostics and prognostics, damage tolerance and failure analysis for composite materials, and advanced multi-physics computational methods for multi-functional structures. Most of his work involves system integration and multi-disciplinary engineering in structural mechanics, electrical engineering, signal processing, and multi-scale fabrication of materials. His recent research topics include: Multifunctional energy storage composites, Integrated health management for aircraft structures, bio-inspired intelligent sensory materials for fly-by-feel autonomous vehicles, active sensing diagnostics for composite structures, self-diagnostics for high-temperature materials, etc.
Ying Chih Chang
BioDr. Ying Chang is an Adjunct Professor of the Department of Chemical Engineering, an affiliate member of Precision Health and Integrated Diagnostic Center of the School of Medicine, and a Co-Director of the Taiwan-Stanford Partnership program, LEAP, at Stanford University. She is also a Research Fellow at the Genomics Research Center, Academia Sinica, and an Adjunct Professor at the Center of Liquid Biopsy at Kaohsiung Medical University, Taiwan. Formerly, she was an Assistant Professor in the Department of Chemical Engineering and Materials Science, and the Department of Biomedical Engineering at the University of California-Irvine, Irvine, CA. Prior to her academic appointments, Dr. Chang had worked in various industrial R&D laboratories including as a Senior Engineer for the hard drive media at Maxmedia California, San Jose, CA (now Seagate), a Postdoctoral Scientist for the materials design of GeneChip at Affymetrix Corp, Santa Clara, CA (now Thermal Fisher Scientific). Her recent invention in circulating tumor cells platform has led to a startup company, Cellmax Life in 2013. Highlights of her research include integrated nanomaterials, microfluidics, and bioreactors to control stem cell fates for tissue engineering and liquid biopsy for cancer diagnostics and precision medicine. Dr. Chang received her BS from National Taiwan University and PhD from Stanford University in Chemical Engineering.
Donald E. Knuth Professor and Professor, by courtesy, of Mathematics
Current Research and Scholarly InterestsEfficient algorithmic techniques for processing, searching and indexing massive high-dimensional data sets; efficient algorithms for computational problems in high-dimensional statistics and optimization problems in machine learning; approximation algorithms for discrete optimization problems with provable guarantees; convex optimization approaches for non-convex combinatorial optimization problems; low-distortion embeddings of finite metric spaces.