School of Engineering
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Alcatel-Lucent Professor of Communications and Networking and Professor of Particle Physics and Astrophysics
BioAlex Aiken is the Alcatel-Lucent Professor of Computer Science at Stanford. Alex received his Bachelors degree in Computer Science and Music from Bowling Green State University in 1983 and his Ph.D. from Cornell University in 1988. Alex was a Research Staff Member at the IBM Almaden Research Center (1988-1993) and a Professor in the EECS department at UC Berkeley (1993-2003) before joining the Stanford faculty in 2003. His research interest is in areas related to programming languages.
Vance D. and Arlene C. Coffman Professor
BioProf. Alonso is the founder and director of the Aerospace Design Laboratory (ADL) where he specializes in the development of high-fidelity computational design methodologies to enable the creation of realizable and efficient aerospace systems. Prof. Alonso’s research involves a large number of different manned and unmanned applications including transonic, supersonic, and hypersonic aircraft, helicopters, turbomachinery, and launch and re-entry vehicles. He is the author of over 200 technical publications on the topics of computational aircraft and spacecraft design, multi-disciplinary optimization, fundamental numerical methods, and high-performance parallel computing. Prof. Alonso is keenly interested in the development of an advanced curriculum for the training of future engineers and scientists and has participated actively in course-development activities in both the Aeronautics & Astronautics Department (particularly in the development of coursework for aircraft design, sustainable aviation, and UAS design and operation) and for the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. He was a member of the team that currently holds the world speed record for human powered vehicles over water. A student team led by Prof. Alonso also holds the altitude record for an unmanned electric vehicle under 5 lbs of mass.
Associate Professor of Electrical Engineering
Current Research and Scholarly InterestsMy group's research covers RF circuits and system design for (1) biomedical, (2) sensing, and (3) Internet of Things (IoT) applications.
Assistant Professor of Aeronautics and Astronautics
Current Research and Scholarly InterestsManan Arya leads the Morphing Space Structures Laboratory. His research is on structures that can adapt their shape to respond to changing requirements. Examples include deployable structures for spacecraft that can stow in constrained volumes for launch and then unfold to larger sizes in space, terrestrial structures with variable geometry, and morphing robots. Key research thrusts include lightweight fiber-reinforced composite materials to enable innovative designs for flexible structures, and the algorithmic generation of the geometry of morphing structures – the arrangement of stiff and compliant elements – to enable novel folding mechanisms.
He has published more than 20 journal and conference papers and has been awarded 5 US patents. Prior to joining Stanford, he was a Technologist at the Advanced Deployable Structures Laboratory at the Jet Propulsion Laboratory, California Institute of Technology, where he developed and tested breakthrough designs for space structures, including deployable reflectarrays, starshades, and solar arrays.
Thomas More Storke Professor, Senior Fellow at the Woods Institute for the Environment and Professor, by courtesy, of Education
BioJeremy Bailenson is founding director of Stanford University’s Virtual Human Interaction Lab, Thomas More Storke Professor in the Department of Communication, Professor (by courtesy) of Education, Professor (by courtesy) Program in Symbolic Systems, a Senior Fellow at the Woods Institute for the Environment, and a Faculty Leader at Stanford’s Center for Longevity. He earned a B.A. cum laude from the University of Michigan in 1994 and a Ph.D. in cognitive psychology from Northwestern University in 1999. He spent four years at the University of California, Santa Barbara as a Post-Doctoral Fellow and then an Assistant Research Professor.
Bailenson studies the psychology of Virtual and Augmented Reality, in particular how virtual experiences lead to changes in perceptions of self and others. His lab builds and studies systems that allow people to meet in virtual space, and explores the changes in the nature of social interaction. His most recent research focuses on how virtual experiences can transform education, environmental conservation, empathy, and health. He is the recipient of the Dean’s Award for Distinguished Teaching at Stanford. In 2020, IEEE recognized his work with “The Virtual/Augmented Reality Technical Achievement Award”.
He has published more than 200 academic papers, spanning the fields of communication, computer science, education, environmental science, law, linguistics, marketing, medicine, political science, and psychology. His work has been continuously funded by the National Science Foundation for over 20 years.
Bailenson consults pro bono on Virtual Reality policy for government agencies including the State Department, the US Senate, Congress, the California Supreme Court, the Federal Communication Committee, the U.S. Army, Navy, and Air Force, the Department of Defense, the Department of Energy, the National Research Council, and the National Institutes of Health.
His first book Infinite Reality, co-authored with Jim Blascovich, emerged as an Amazon Best-seller eight years after its initial publication, and was quoted by the U.S. Supreme Court. His new book, Experience on Demand, was reviewed by The New York Times, The Wall Street Journal, The Washington Post, Nature, and The Times of London, and was an Amazon Best-seller.
He has written opinion pieces for Harvard Business Review, The Washington Post, The Wall Street Journal, CNN, PBS NewsHour, Wired, National Geographic, Slate, The San Francisco Chronicle, and The Chronicle of Higher Education, and has produced or directed six Virtual Reality documentary experiences which were official selections at the Tribeca Film Festival. His lab’s research has exhibited publicly at museums and aquariums, including a permanent installation at the San Jose Tech Museum.
Barney and Estelle Morris Professor
Current Research and Scholarly InterestsResearch
My students and I devise new algorithms to improve the imaging of reflection seismic data. Images obtained from seismic data are the main source of information on the structural and stratigraphic complexities in Earth's subsurface. These images are constructed by processing seismic wavefields recorded at the surface of Earth and generated by either active-source experiments (reflection data), or by far-away earthquakes (teleseismic data). The high-resolution and fidelity of 3-D reflection-seismic images enables oil companies to drill with high accuracy for hydrocarbon reservoirs that are buried under two kilometers of water and up to 15 kilometers of sediments and hard rock. To achieve this technological feat, the recorded data must be processed employing advanced mathematical algorithms that harness the power of huge computational resources. To demonstrate the advantages of our new methods, we process 3D field data on our parallel cluster running several hundreds of processors.
I teach a course on seismic imaging for graduate students in geophysics and in the other departments of the School of Earth Sciences. I run a research graduate seminar every quarter of the year. This year I will be teaching a one-day short course in 30 cities around the world as the SEG/EAGE Distinguished Instructor Short Course, the most important educational outreach program of these two societies.
2007 SEG/EAGE Distinguished Instructor Short Course (2007); co-director, Stanford Exploration Project (1998-present); founding member, Editorial Board of SIAM Journal on Imaging Sciences (2007-present); member, SEG Research Committee (1996-present); chairman, SEG/EAGE Summer Research Workshop (2006)
Samsung Professor in the School of Engineering
BioStephen P. Boyd is the Samsung Professor of Engineering, and Professor of Electrical Engineering in the Information Systems Laboratory at Stanford University. He has courtesy appointments in the Department of Management Science and Engineering and the Department of Computer Science, and is member of the Institute for Computational and Mathematical Engineering. His current research focus is on convex optimization applications in control, signal processing, machine learning, and finance.
Professor Boyd received an AB degree in Mathematics, summa cum laude, from Harvard University in 1980, and a PhD in EECS from U. C. Berkeley in 1985. In 1985 he joined Stanford's Electrical Engineering Department. He has held visiting Professor positions at Katholieke University (Leuven), McGill University (Montreal), Ecole Polytechnique Federale (Lausanne), Tsinghua University (Beijing), Universite Paul Sabatier (Toulouse), Royal Institute of Technology (Stockholm), Kyoto University, Harbin Institute of Technology, NYU, MIT, UC Berkeley, CUHK-Shenzhen, and IMT Lucca. He holds honorary doctorates from Royal Institute of Technology (KTH), Stockholm, and Catholic University of Louvain (UCL).
Professor Boyd is the author of many research articles and four books: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least-Squares (with Lieven Vandenberghe, 2018), Convex Optimization (with Lieven Vandenberghe, 2004), Linear Matrix Inequalities in System and Control Theory (with El Ghaoui, Feron, and Balakrishnan, 1994), and Linear Controller Design: Limits of Performance (with Craig Barratt, 1991). His group has produced many open source tools, including CVX (with Michael Grant), CVXPY (with Steven Diamond) and Convex.jl (with Madeleine Udell and others), widely used parser-solvers for convex optimization.
Professor Boyd has received many awards and honors for his research in control systems engineering and optimization, including an ONR Young Investigator Award, a Presidential Young Investigator Award, and the AACC Donald P. Eckman Award. In 2013, he received the IEEE Control Systems Award, given for outstanding contributions to control systems engineering, science, or technology. In 2012, Michael Grant and he were given the Mathematical Optimization Society's Beale-Orchard-Hays Award, for excellence in computational mathematical programming. He is a Fellow of the IEEE, SIAM, INFORMS, and IFAC, a Distinguished Lecturer of the IEEE Control Systems Society, a member of the US National Academy of Engineering, a foreign member of the Chinese Academy of Engineering, and a foreign member of the National Academy of Engineering of Korea. He has been invited to deliver more than 90 plenary and keynote lectures at major conferences in control, optimization, signal processing, and machine learning.
He has developed and taught many undergraduate and graduate courses, including Signals & Systems, Linear Dynamical Systems, Convex Optimization, and a recent undergraduate course on Matrix Methods. His graduate convex optimization course attracts around 300 students from more than 20 departments. In 1991 he received an ASSU Graduate Teaching Award, and in 1994 he received the Perrin Award for Outstanding Undergraduate Teaching in the School of Engineering. In 2003, he received the AACC Ragazzini Education award, for contributions to control education, with citation: “For excellence in classroom teaching, textbook and monograph preparation, and undergraduate and graduate mentoring of students in the area of systems, control, and optimization.” In 2016 he received the Walter J. Gores award, the highest award for teaching at Stanford University. In 2017 he received the IEEE James H. Mulligan, Jr. Education Medal, for a career of outstanding contributions to education in the fields of interest of IEEE, with citation "For inspirational education of students and researchers in the theory and application of optimization."
Leticia Britos Cavagnaro
BioLeticia Britos Cavagnaro, Ph.D., is co-Director of the University Innovation Fellows, a program of the Hasso Plattner Institute of Design (d.school), which empowers students to be co-designers of their education, in collaboration with faculty and leaders at their schools. Leticia was Deputy Director of the National Center for Engineering Pathways to Innovation (Epicenter), an NSF-funded initiative that operated from 2011 to 2016 to foster innovation and entrepreneurship in engineering education nationwide. She is an adjunct professor at the d.school, where she teaches Stanford students of all disciplines how to build their creative confidence to become engines of innovation in teams and organizations. Leticia has a Ph.D. in Developmental Biology from Stanford's School of Medicine, and is a former member of the Research in Education & Design Lab (REDlab) at Stanford’s School of Education. Having witnessed the journey of students who are transformed by their experience at the d.school, bringing design thinking to more people beyond Stanford has become a priority for Leticia, and she has worked with hundreds of educators and students of all ages, as well as corporate and non-profit leaders in the US and abroad. In the Summer of 2013, Leticia engaged thousands of people from over 130 countries in learning design thinking and applying the methodology to innovate in their contexts, via an experiential MOOC (http://novoed.com/designthinking).
Find out more about Leticia's work at:
Designing for Change: Using social learning to understand organizational transformation (book about the UIF program): https://www.amazon.com/Designing-Change-understand-organizational-transformation/dp/1733735402/
Connect with Leticia:
Twitter: @LeticiaBritosC (twitter.com/leticiabritosc)
Current Research and Scholarly InterestsMy genetics research focuses on analyzing genome wide patterns of variation within and between species to address fundamental questions in biology, anthropology, and medicine. We focus on novel methods development for complex disease genetics and risk prediction in multi-ethnic settings. I am also interested in clinical data science and development of new diagnostics.I am also interested in disruptive innovation for healthcare including modeling long-term risk shifts and novel payment models.
Barnum-Simons Chair of Math and Statistics, and Professor of Statistics and, by courtesy, of Electrical Engineering
BioEmmanuel Candès is the Barnum-Simons Chair in Mathematics and Statistics, a professor of electrical engineering (by courtesy) and a member of the Institute of Computational and Mathematical Engineering at Stanford University. Earlier, Candès was the Ronald and Maxine Linde Professor of Applied and Computational Mathematics at the California Institute of Technology. His research interests are in computational harmonic analysis, statistics, information theory, signal processing and mathematical optimization with applications to the imaging sciences, scientific computing and inverse problems. He received his Ph.D. in statistics from Stanford University in 1998.
Candès has received several awards including the Alan T. Waterman Award from NSF, which is the highest honor bestowed by the National Science Foundation, and which recognizes the achievements of early-career scientists. He has given over 60 plenary lectures at major international conferences, not only in mathematics and statistics but in many other areas as well including biomedical imaging and solid-state physics. He was elected to the National Academy of Sciences and to the American Academy of Arts and Sciences in 2014.
Ann and Bill Swindells Professor, Emeritus
BioDr. Carlsson has been a professor of mathematics at Stanford University since 1991. In the last ten years, he has been involved in adapting topological techniques to data analysis, under NSF funding and as the lead PI on the DARPA “Topological Data Analysis” project from 2005 to 2010. He is the lead organizer of the ATMCS conferences, and serves as an editor of several Mathematics journals
BioCarissa Carter is the Academic Director 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 offerings. She teaches courses on the intersection of data and design, design for climate change, design for emerging tech, and maps and the visual sorting of information.
Associate Professor of Electrical Engineering, Senior Fellow at the Precourt Institute for Energy and Associate Professor, by courtesy, of Materials Science and Engineering
Current Research and Scholarly InterestsWide bandap materials & devices for RF, Power and energy efficient electronics
John M. Cioffi
Hitachi America Professor in the School of Engineering, Emeritus
BioJohn M. Cioffi teaches Stanford's graduate electrical engineering course sequence in digital communications, part-time as recalled emeritus presently, from 1986 to the present. Cioffi's research interests are in the theory of transmitting the highest possible data rates on a number of different communications channels, many of which efforts spun out of Stanford through he and/or his many former PhD students to companies, most notably including the basic designed globally used 500 million DSL connections. Cioffi also oversaw the prototype developments for the worlds first cable modem and digital-audio broadcast systems. Cioffi pioneering the use of remote management algorithms to improve (over the internet or cloud) both wireline (DSL) and wireless (Wi-Fi) physical-layer transmission performance, an area often known as Dynamic Spectrum Management or Dynamic Line Management. Cioffi is co-inventor on basic patents for vectored DSL transmission and optimized MIMO wireless transmission. In his early career, Cioffi developed the worlds first full-duplex voiceband data modem while at Bell Laboratories, and the worlds first adaptively equalized disk read channel while at IBM. His courses and research projects over the years center on the area of multiuser transmission methods.
Professor of Mechanical Engineering
Current Research and Scholarly InterestsProfessor Darve's research is focused on the development of numerical methods for high-performance scientific computing, numerical linear algebra, fast algorithms, parallel computing, anomaly detection, and machine learning with applications in engineering.