School of Engineering
Showing 611-620 of 792 Results
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Shuran Song
Assistant Professor of Electrical Engineering and, by courtesy, of Computer Science
BioShuran Song is an Assistant Professor of Electrical Engineering at Stanford University. Before joining Stanford, she was faculty at Columbia University. Shuran received her Ph.D. in Computer Science at Princeton University, BEng. at HKUST. Her research interests lie at the intersection of computer vision and robotics. Song’s research has been recognized through several awards, including the Best Paper Awards at RSS’22 and T-RO’20, Best System Paper Awards at CoRL’21, RSS’19, and finalists at RSS, ICRA, CVPR, and IROS. She is also a recipient of the NSF Career Award, Sloan Foundation fellowship as well as research awards from Microsoft, Toyota Research, Google, Amazon, and JP Morgan.
To learn more about Shuran’s work, please visit: https://shurans.github.io/ -
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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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.
