School of Engineering
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Associate Professor of Statistics and of Biomedical Data Science
BioDr. Palacios seek to provide statistically rigorous answers to concrete, data driven questions in evolutionary genetics and public health . My research involves probabilistic modeling of evolutionary forces and the development of computationally tractable methods that are applicable to big data problems. Past and current research relies heavily on the theory of stochastic processes, Bayesian nonparametrics and recent developments in machine learning and statistical theory for big data.
Professor (Research) of Electrical Engineering, Emeritus
BioProfessor Emeritus Arogyaswami Paulraj, Stanford University, is a pioneer of MIMO wireless communications, a technology break through that enables improved wireless performance. MIMO is now incorporated into all new wireless systems.
Paulraj is the author of over 400 research papers, two textbooks, and a co-inventor in 80 US patents.
Paulraj has won over a dozen awards, notably the National Inventors Hall of Fame (USPTO), Marconi Prize and Fellowship, 2014 and the IEEE Alexander Graham Bell Medal, 2011. He is a fellow of eight scientific / engineering national academies including the US, China, India, and Sweden. He is a fellow of IEEE and AAAS.
In 1999, Paulraj founded Iospan Wireless Inc. - which developed and established MIMO-OFDMA wireless as the core 4G technology. Iospan was acquired by Intel Corporation in 2003. In 2004, he co-founded Beceem Communications Inc. The company became the market leader in 4G-WiMAX semiconductor and was acquired by Broadcom Corp. in 2010. In 2014 he founded Rasa Networks to develop Machine Learning tools for WiFi Networks. The company was acquired HPE in 2016.
During his 30 years in the Indian (Navy) (1961-1991), he founded three national-level laboratories in India and headed one of India’s most successful military R&D projects – APSOH sonar. He received over a dozen awards (many at the national level) in India including the Padma Bhushan, Ati Vishist Seva Medal and the VASVIK Medal.
Associate Professor of Aeronautics and Astronautics and, by courtesy, of Electrical Engineering and of Computer Science
BioDr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University, where he directs the Autonomous Systems Laboratory and the Center for Automotive Research at Stanford. He is also a Distinguished Research Scientist at NVIDIA where he leads autonomous vehicle research. Before joining Stanford, he was a Research Technologist within the Robotics Section at the NASA Jet Propulsion Laboratory. He received a Ph.D. degree in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His main research interests are in the development of methodologies for the analysis, design, and control of autonomous systems, with an emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He is a recipient of a number of awards, including a Presidential Early Career Award for Scientists and Engineers from President Barack Obama, an Office of Naval Research Young Investigator Award, a National Science Foundation Early Career (CAREER) Award, a NASA Early Career Faculty Award, and an Early-Career Spotlight Award from the Robotics Science and Systems Foundation. He was identified by the American Society for Engineering Education (ASEE) as one of America's 20 most highly promising investigators under the age of 40. His work has been recognized with best paper nominations or awards at a number of venues, including the European Conference on Computer Vision, the IEEE International Conference on Robotics and Automation, the European Control Conference, the IEEE International Conference on Intelligent Transportation Systems, the Field and Service Robotics Conference, the Robotics: Science and Systems Conference, and the INFORMS Annual Meeting.
Assistant Professor of Management Science and Engineering
Current Research and Scholarly InterestsHis research focuses on understanding and managing financial risk. He develops mathematical financial models and statistical methods, analyzes financial data and engineers computational techniques. His research is divided into three streams: statistical learning in high-dimensional financial data sets, stochastic financial modeling, and high-frequency statistics. His most recent work focuses on developing machine learning solutions to big-data problems in empirical asset pricing.
Assistant Professor of Electrical Engineering
Current Research and Scholarly InterestsDr. Pilanci's research interests include neural networks, machine learning, mathematical optimization, information theory and signal processing.
Professor of Mechanical Engineering, Emeritus
BioPinsky works in the theory and practice of computational mechanics with a particular interest in multiphysics problems in biomechanics. His work uses the close coupling of techniques for molecular, statistical and continuum mechanics with biology, chemistry and clinical science. Areas of current interest include the mechanics of human vision (ocular mechanics) and the mechanics of hearing. Topics in the mechanics of vision include the mechanics of transparency, which investigates the mechanisms by which corneal tissue self-organizes at the molecular scale using collagen-proteoglycan-ion interactions to explain the mechanical resilience and almost perfect transparency of the tissue and to provide a theoretical framework for engineered corneal tissue replacement. At the macroscopic scale, advanced imaging data is used to create detailed models of the 3-D organization of collagen fibrils and the results used to predict outcomes of clinical techniques for improving vision as well as how diseased tissue mechanically degrades. Theories for mass transport and reaction are being developed to model metabolic processes and swelling in tissue. Current topics in the hearing research arena include multiscale modeling of hair-cell mechanics in the inner ear including physical mechanisms for the activation of mechanically-gated ion channels. Supporting research addresses the mechanics of lipid bilayer cell membranes and their interaction with the cytoskeleton. Recent past research topics include computational acoustics for exterior, multifrequency and inverse problems; and multiscale modeling of transdermal drug delivery. Professor Pinsky currently serves as Chair of the Mechanics and Computation Group within the Department of Mechanical Engineering at Stanford.
John M. Fluke Professor of Electrical Engineering and Professor, by courtesy, of Materials Science and Engineering
Current Research and Scholarly InterestsGenerally studies the governing physics and fabrication technology of silicon integrated circuits, including the scaling limits of silicon technology, and the application of silicon technology outside traditional integrated circuits, including power switching devices such as IGBTs. Process simulation tools like SUPREM for simulating fabrication. Recent work has focused on wide bandgap semiconductor materials, particularly SiC and GaN, for power control devices.
Associate Professor of Electrical Engineering
Current Research and Scholarly InterestsOur research focuses on providing theoretical foundations and engineering platforms for realizing electronics that seamlessly integrate with the body. Such systems will allow precise recording or modulation of physiological activity, for advancing basic scientific discovery and for restoring or augmenting biological functions for clinical applications.
Pease-Ye Professor, Professor of Electrical Engineering and, by courtesy, of Materials Science and Engineering
Current Research and Scholarly InterestsThe Pop Lab explores problems at the intersection of nanoelectronics and nanoscale energy conversion. These include fundamental limits of current and heat flow, energy-efficient transistors and memory, and energy harvesting via thermoelectrics. The Pop Lab also works with novel nanomaterials like carbon nanotubes, graphene, BN, MoS2, and their device applications, through an approach that is experimental, computational and highly collaborative.