School of Engineering
Showing 6,001-6,100 of 7,566 Results
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Andrew Spakowitz
Tang Family Foundation Chair of the Department of Chemical Engineering, Professor of Chemical Engineering, of Materials Science and Engineering and, by courtesy, of Applied Physics
Current Research and Scholarly InterestsTheory and computation of biological processes and complex materials
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Adrien Specht
Ph.D. Student in Computational and Mathematical Engineering, admitted Spring 2024
BioI'm a PhD student in the Institute for Computational and Mathematical Engineering (ICME) at Stanford University, mentored by Prof. Mignot. My research is at the intersection of artificial intelligence and sleep medicine, focusing on developing predictive models for circadian rhythms and sleep debt from proteomics data. I adopt a problem-oriented approach, selecting methods based on the data and research questions at hand. My techniques range from linear regression to sophisticated deep learning frameworks, aiming to extract maximal insights from the data. I also explore the use of unsupervised and semi-supervised learning, and am interested in the applications of multimodal and foundation models in biology.
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Daniel Spielman
Professor of Radiology (Radiological Sciences Lab) and, by courtesy, of Electrical Engineering
Current Research and Scholarly InterestsMy research interests are in the field of medical imaging, particularly magnetic resonance imaging and in vivo spectroscopy. Current projects include MRI and MRS at high magnetic fields and metabolic imaging using hyperpolarized 13C-labeled MRS.
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Alfred M. Spormann
Professor of Civil and Environmental Engineering and of Chemical Engineering, Emeritus
Current Research and Scholarly InterestsMetabolism of anaerobic microbes in diseases, bioenergy, and bioremediation
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Kavya Sreedhar
Ph.D. Student in Electrical Engineering, admitted Autumn 2019
BioKavya Sreedhar is an electrical engineering PhD candidate advised by Mark Horowitz. Her research interests include architecture design and developing hardware accelerators for cryptography and machine learning applications. On the cryptography side, she has worked on designing a fast extended GCD accelerator for constant-time modular inversion and verifiable delay functions. On the deep learning side, she is working on dynamically adapting the execution of state-of-the-art models for use in real-time systems and accelerating dynamic transformer models for computer vision in an ongoing collaboration with NVIDIA. She previously worked with the Agile Hardware (AHA) Project in developing Lake, a parameterizable memory generator that can be configured at runtime to support different image processing and machine learning applications. As part of her research, she has worked on taping out three chips in SKY130nm, GF12nm, and TSMC16nm. Kavya is supported by the Quad Fellowship (2023 to 2024) and Stanford's Knight-Hennessy Graduate Fellowship (2019 to 2022). She received a B.S. in Electrical Engineering and BEM (Business, Economics, & Management) from Caltech in 2019 and a M.S. in Electrical Engineering from Stanford in 2021.
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Skyler St. Pierre
Ph.D. Student in Mechanical Engineering, admitted Autumn 2020
Current Research and Scholarly Interestsbiomechanics, machine learning, computational modeling
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Kirsten Stasio
Adjunct Lecturer, Atmosphere and Energy
BioKirsten Stasio is CEO of the Nevada Clean Energy Fund (NCEF), Nevada's nonprofit green bank. She also serves as an Adjunct Lecturer at Stanford University, where she co-teaches Understand Energy, a course that gives students the knowledge and tools to engage in the energy and sustainability sectors.
Throughout her career, Kirsten has strived to translate her life-long passion for environmental sustainability into real impact across the policy, education, corporate, and investment sectors. Before joining NCEF, Kirsten worked at MAP Energy, an energy investment firm, where she helped scale investments in renewable energy across the US. Her early career began at the World Resources Institute (WRI), a non-profit, where she worked with policymakers and other stakeholders to implement climate finance solutions. While getting her graduate degree at Stanford, Kirsten worked at Pacific Gas and Electric (PG&E) where she helped launched a new energy efficiency initiative with large businesses in the Bay Area. Kirsten also worked at Apple to implement energy measures at Apple's headquarters, retail stores, and data centers.
Kirsten began teaching at Stanford in early 2015 after graduating from Stanford with an MBA and an MS degree in the Emmet-Interdisciplinary Program on Environment and Resources (E-IPER). Kirsten also earned a dual BA in International Relations and French from the University of California, Davis.
The origins of Kirsten's passion for sustainability trace back to her childhood when she spent time on her family’s fourth-generation ranch in the Sierra Nevada foothills, a place where she enjoys spending time today with her husband and daughter. -
Ryan Stice-Lusvardi
Ph.D. Student in Management Science and Engineering, admitted Autumn 2017
BioRyan is interested in examining how changing applications and increasing adoption of data analytics are shaping the future of work and organizations. In her current research, she seeks to illuminate the ways in which assumptions and values of data analysts are embedded in analytic practices and how this shapes data insights and decisions. Her previous research has examined the impact of perceived analytic ability on HR performance evaluations and the emergence of HR analytics. Ryan has lived in Hong Kong, Thailand, Singapore, France, and South Korea. She enjoys repurposing vintage doors, bouts of gardening, and eating gourmet takeout.
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David G Stork
Affiliate, Electrical Engineering
BioDavid G. Stork teaches and performs research in several disciplines:
• Rigorous computer image analysis of fine art paintings and drawings
• Computational sensing and imaging with metasurface optical elements
• Applications of computer algebra
He is a graduate in Physics from MIT and the University of Maryland, and studied Art History at Wellesley College. He was Chief Scientist of the American arm of the $15B international Ricoh Company and Rambus Fellow at Rambus, Inc. He has held faculty positions in Physics, Mathematics, Computer Science, Statistics, Electrical Engineering, Computation & Mathematical Engineering, Neuroscience, Psychology, and Art and Art History variously at Wellesley and Swarthmore Colleges, Clark, Boston, and Stanford Universities, and the Technical University of Vienna. He is a Fellow of IEEE, OSA, SPIE, IS&T, IAPR, IARIA, AAIA, IAII, and a Senior Life Member of ACM and was a 2023 Leonardo@Djerassi Fellow. He holds 64 US patents, and has published over 220 peer-reviewed scholarly articles and nine books/proceedings volumes, including "Pattern classification" (2nd ed.), "Seeing the light: Optics in nature, photography, color, vision, and holography," "HAL's Legacy: 2001's computer as dream and reality," and "Pixels & paintings: Foundations of computer-assisted connoisseurship." -
Maxwell Bradley Strange
Ph.D. Student in Electrical Engineering, admitted Autumn 2018
BioMax is a Ph.D. student in Electrical Engineering advised by Mark Horowitz. His research focuses on developing infrastructure and tools to facilitate agile hardware development as part of the ongoing efforts by the Stanford AHA! Research Center. His research interests also include domain-specific hardware architectures, hardware/software co-design, and embedded systems design. He graduated from the University of Wisconsin in 2017 with a B.S. in Computer Engineering and Computer Science.
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Robert Street
William Alden and Martha Campbell Professor in the School of Engineering, Emeritus
Current Research and Scholarly InterestsStreet focuses on numerical simulations related to geophysical fluid motions. His research considers the modeling of turbulence in fluid flows, which are often stratified, and includes numerical simulation of coastal upwelling, internal waves and sediment transport in coastal regions, flow in rivers, valley winds, and the planetary boundary layer.
