School of Engineering
Showing 1-23 of 23 Results
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Andrei Kanavalau
Ph.D. Student in Electrical Engineering, admitted Autumn 2020
Masters Student in Electrical Engineering, admitted Winter 2023BioAndrei is a PhD candidate in the Electrical Engineering department at Stanford. His research focuses on augmenting control algorithms with machine learning while preserving safety and stability guarantees.
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Taeyoung Kong
Ph.D. Student in Electrical Engineering, admitted Autumn 2017
BioTaeyoung is a Ph.D. student at Stanford University working with prof. Mark Horowitz in VLSI group and he is currently working within the AHA Agile Hardware Project. He is interested in hardware accelerator for deep learning / image processing and hardware design methodology. Taeyoung received a B.S. in Electrical and Computer Engineering from Seoul National University in 2017, and M.S. in Electrical Engineering from Stanford University in 2020.
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Kalhan Koul
Ph.D. Student in Electrical Engineering, admitted Autumn 2019
BioKalhan Koul is an EE Ph.D. student at Stanford University supervised by Prof. Priyanka Raina. Previously, he was a Digital Design Intern at Micron and Silicon Labs. He received a B.S. in Electrical Engineering Honors and a B.A. in Plan II Honors (Liberal Arts) from The University of Texas in 2018 and his M.S. in Electrical Engineering from Stanford University in 2021. During his PhD he has worked on three chip tapeouts. The first was Chimera, a DNN accelerator utilizing RRAM for low energy inference. The next was Amber, a coarse grained reconfigurable array (CGRA) optimized for image processing and machine learning applications. Finally, Kalhan led the tapeout of Onyx, a CGRA accelerating both dense and sparse kernels on the same fabric. His current research focuses on further improving the efficiency of the CGRA and extending its acceleration to end-to-end machine learning workloads.
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Renesmee Kuo
Ph.D. Student in Electrical Engineering, admitted Autumn 2022
BioRenesmee Kuo is an Electrical Engineering PhD candidate at Stanford University supported by NSF GRFP. Her research interests lie at the intersection of engineering and medicine. She focuses on validation of preclinical PET imaging tracers and their translation into the clinic for applications in neuroinflammatory diseases (e.g., MS, AD) and cancer (e.g., brain metastasis) in Prof. Michelle James' lab. She graduated from UC Berkeley with a BS in Bioengineering. At Berkeley, she worked in Prof. Steve Conolly's lab on Magnetic Particle Imaging (MPI), focusing on tracking CAR-T cells in immunotherapy using high-resolution MPI tracers. She also explored commercially-available high-resolution MPI tracers for early diagnosis of pulmonary embolisms and cardiovascular disease in preclinical settings.
