Programs, Centers and Institutes
Showing 1-22 of 22 Results
-
Julia Palacios
Associate Professor of Statistics and of Biomedical Data Science
BioDr. Palacios’s research spans Bayesian nonparametrics, probabilistic AI, stochastic processes, and computational statistics. Her group develops stochastic models and efficient inference algorithms for understanding evolutionary dynamics in population genetics, infectious diseases and cancer.
-
Arogyaswami Paulraj
Professor (Research) of Electrical Engineering, Emeritus
BioProf. Arogyaswami Paulraj is an Emeritus Professor at Stanford University. Paulraj's legacy is deeply intertwined with the evolution of wireless communication. His groundbreaking work on MIMO (multiple input, multiple output) technology laid the foundation for today's ubiquitous 4G/5G networks and high-speed Wi-Fi.
Paulraj's journey began in the Indian Navy, where he served from 1965 to 1991. During this time, he led the development of the APSOH anti-submarine sonar system and established three key R&D labs for the Indian government. His contributions earned him the prestigious Padma Bhushan award, India's third highest civilian honor.
Following his naval career, Paulraj joined Stanford University as a postdoctoral researcher. His research focus shifted to wireless communication, where he made groundbreaking contributions to MIMO technology. MIMO enables data transmission using multiple antennas, significantly increasing network capacity and data rates.
Paulraj's innovation has been recognized with numerous accolades, including the 2024 Royal Acad. Eng. Prince Philip Medal, the 2023 IET Faraday Medal, the 2014 Marconi Prize, and the 2011 IEEE Alexander Graham Bell Medal. He is also a fellow of nine national academies in engineering, sciences, and the arts, and an inductee of the US Patent Office’s National Inventors Hall of Fame.
Currently, Paulraj continues to contribute to technological advancement. He chairs several committees for the Government of India, focusing on the Indian Semiconductor Mission and Core ICT initiatives. His dedication to research and development continues to shape the future of wireless communication. -
Marco Pavone
Associate Professor of Aeronautics and Astronautics, Senior Fellow at the Precourt Institute for Energy and Associate Professor, by courtesy, of Electrical Engineering & 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.
-
Markus Pelger
Associate 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: machine learning solutions to big-data problems in empirical asset pricing, statistical theory for high-dimensional data and stochastic financial modeling.
-
Mert Pilanci
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.
-
Peter Pinsky
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.
-
Eric Pop
Pease-Ye Professor, Professor of Electrical Engineering, Senior Fellow at the Precourt Institute for Energy and Professor, by courtesy, of Materials Science and Engineering and of Applied Physics
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.
-
Robert Prakash
Chief Technology Officer & Chief Operating Officer, Stanford Engineering Center for Global and Online Education
Current Role at StanfordChief Technology Officer & Chief Operating Officer, Stanford Engineering | Center for Global & Online Education
-
Adrienne Propp
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2021
BioI am a fourth year PhD student in ICME (the Institute for Computational and Mathematical Engineering). Prior to Stanford, I was working as a technical analyst at the RAND Corporation where I spent most of my time designing microsimulations and other models to investigate topics in healthcare, education, disaster relief, and international relations.
My research interests lie at the intersection of mathematics, data, and modeling, which has led me to a focus on scientific machine learning (SciML). Specifically, I am working on developing new graph-based surrogate modeling methods for low-data regimes. I am grateful to be advised by Daniel Tartakovsky, During my PhD, I have also collaborated with Jenny Suckale to model volcanic lava fountaining, and Susan Athey and Sanath Kumar Krishnamurthy to design improved algorithms for contextual bandits.
Past research projects have ranged from computational models of the heart to inverse modeling to predict satellite performance.
