School of Engineering
Showing 111-120 of 706 Results
-
Steven Hartley Collins
Associate Professor of Mechanical Engineering and, by courtesy, of Bioengineering
BioSteve Collins is an Associate Professor of Mechanical Engineering at Stanford University, where he teaches courses on design and robotics and directs the Stanford Biomechatronics Laboratory. His primary focus is to speed and systematize the design and prescription of prostheses and exoskeletons using versatile device emulator hardware and human-in-the-loop optimization algorithms (Zhang et al. 2017, Science). Another interest is efficient autonomous devices, such as highly energy-efficient walking robots (Collins et al. 2005, Science) and exoskeletons that use no energy yet reduce the metabolic energy cost of human walking (Collins et al. 2015, Nature).
Prof. Collins received his B.S. in Mechanical Engineering in 2002 from Cornell University, where he performed undergraduate research on passive dynamic walking robots. He received his Ph.D. in Mechanical Engineering in 2008 from the University of Michigan, where he performed research on the dynamics and control of human walking. He performed postdoctoral research on humanoid robots at T. U. Delft in the Netherlands. He was a professor of Mechanical Engineering and Robotics at Carnegie Mellon University for seven years. In 2017, he joined the faculty of Mechanical Engineering at Stanford University.
Prof. Collins is a member of the Scientific Board of Dynamic Walking and the Editorial Board of Science Robotics. He has received the Young Scientist Award from the American Society of Biomechanics, the Best Medical Devices Paper from the International Conference on Robotics and Automation, and the student-voted Professor of the Year in his department. -
Daniel Norbert Congreve
Assistant Professor of Electrical Engineering
BioDan is an Assistant Professor in the Department of Electrical Engineering at Stanford University. Prior to Stanford, Dan received his B.S. and M.S. from Iowa State in 2011, working with Vik Dalal studying defect densities of nano-crystalline and amorphous silicon. He then received his PhD from MIT in Electrical Engineering in 2015, studying under Marc Baldo. His thesis work focused on photonic energy conversion using singlet fission and triplet fusion as downconverting and upconverting processes, respectively. He spent a year as a postdoc with Will Tisdale in Chemical Engineering at MIT studying perovskite nanoplatelets. He joined the Rowland Institute in 2016 as a Rowland Fellow before starting at Stanford in 2020. Dan is a Moore Inventor Fellow, Sloan Research Fellow, Intel Rising Star, and co-founder of Quadratic3D, a startup looking to commercialize 3D printing technologies. His current research interests focus on engineering nanomaterials to solve challenging problems.
-
Richard Cottle
Professor of Management Science and Engineering, Emeritus
BioRichard W. (Dick) Cottle was born in Chicago in 1934. He received his elementary and high school education in the neighboring village of Oak Park. Dick enrolled at Harvard College to take up political science and premedical studies in order to become a physician (or possibly a foreign service officer if that didn't work out). As it happened, both of these alternatives were abandoned because he was strongly attracted to mathematics and ultimately received his bachelor's degree in that field. He stayed on at Harvard and received the master's degree in mathematics in 1958. This was the Sputnik era, and Dick was moved by a passion to teach secondary-level mathematics. In the first of a series of fateful decisions, he joined the Mathematics Department at the Middlesex School in Concord, Massachusetts where for two years he taught grades 7-12. Midway through this period he married his wife Suzanne (Sue). At this time he began to think of returning to graduate school for a doctorate in mathematics. He decided to study geometry at the University of California at Berkeley and was admitted there. Just before leaving Middlesex, Dick received a telephone call from the Radiation Laboratory at Berkeley offering him the part- time job as a computer programmer for which he had applied. Through this job, he became aware of linear and quadratic programming and the contributions of George Dantzig and Philip Wolfe. Before long, Dick left the Rad Lab to join Dantzig's team at the Operations Research Center at UC Berkeley. Under the tutelage of George Dantzig (and the late Edmund Eisenberg), Dick developed a symmetric duality theory and what was then called the "composite problem". These topics along with a reëxamination of the Fritz John conditions, formed the core of his doctoral dissertation. The composite problem involved a fusion of the primal and dual first-order optimality conditions. It was realized that the resulting inequality system could be studied without reference to the primal-dual structure out of which it was born. The name "complementarity problem" was suggested by Dick and introduced in a joint paper with Habetler and Lemke. After Berkeley, Dick's work took two closely related directions. One was the study of quadratic programming; the other was what we now call "linear complementarity". The interesting role played by classes of matrices in both these areas has always held a special fascination for Dick. In quadratic programming, for instance, with Jacques Ferland he obtained characterizations of quasi- and pseudo-convexity of quadratic functions. Dick (and others) were quick to recognize the importance of matrix classes in linear complementarity theory. It was he who proposed the name "copositive-plus" for a matrix class that arose in Lemke's seminal paper of 1965. The name first appeared in the classic paper of Cottle and Dantzig called "Complementary Pivot Theory of Mathematical Programming". The subjects of quadratic programming and linear complementarity (and the associated matrix theory) remain central to his research interests.
-
Markus Covert
Shriram Chair of the Department of Bioengineering, Professor of Bioengineering and, by courtesy, of Chemical and Systems Biology
Current Research and Scholarly InterestsOur focus is on building computational models of complex biological processes, and using them to guide an experimental program. Such an approach leads to a relatively rapid identification and validation of previously unknown components and interactions. Biological systems of interest include metabolic, regulatory and signaling networks as well as cell-cell interactions. Current research involves the dynamic behavior of NF-kappaB, an important family of transcription factors.
-
Craig Criddle
Professor of Civil and Environmental Engineering, Emeritus
Current Research and Scholarly InterestsCriddle's interests include microbial biotechnology for the circular economy, including recovery of clean water from used water, renewable energy, valuable materials that can replace fossil-carbon derived materials. Current projects include energy-efficient anaerobic wastewater treatment technology, assessment of new treatment trains that yield high quality water; fossil carbon plastics biodegradation, and biotechnology for production of bioplastics that can replace fossil carbon plastics.
-
Yi Cui
Fortinet Founders Professor, Professor of Materials Science and Engineering, of Energy Science and Engineering, of Photon Science, Senior Fellow at Woods and Professor, by courtesy, of Chemistry
BioCui studies fundamentals and applications of nanomaterials and develops tools for their understanding. Research Interests: nanotechnology, batteries, electrocatalysis, wearables, 2D materials, environmental technology (water, air, soil), cryogenic electron microscopy.
-
Murray Connelly Cutforth
Physical Science Research Scientist
BioMurray Cutforth is a research scientist on the PSAAP III project at the Center for Turbulence Research. He works with Professor Eric Darve on uncertainty quantification of laser-ignited turbulent combustion. During his PhD at the University of Cambridge, Murray studied sharp interface methods for multi-material flow, and subsequently has worked on applications of machine learning in medical image and text analysis in industry.
