School of Engineering
Showing 51-100 of 360 Results
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Bo Wun Cheng
Ph.D. Student in Electrical Engineering, admitted Autumn 2023
BioBo-Wun Cheng is an EE Ph.D. student at Stanford University supervised by Prof. Priyanka Raina. He received his B.S. and M.S. degrees in Computer Science from National Tsing Hua University (Taiwan) in 2021 and 2023, respectively. His current research interest resides in designing and architecting efficient hardware accelerators. Before joining Stanford, his research spans the fields of Graphics Processing Unit memory architecture design and computer vision.
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Dali Cheng
Ph.D. Student in Electrical Engineering, admitted Autumn 2021
Current Research and Scholarly InterestsA light chaser studying photonics both theoretically and experimentally. I am devoted to understanding and improving our world using photonic science and engineering.
My current interest includes photonic systems with nontrivial topology, non-Hermiticity, non-Abelian gauge fields, and in the synthetic dimension. -
Jasmine M. Cox
Ph.D. Student in Electrical Engineering, admitted Autumn 2020
ENGR 240 Grader, Electrical Engineering - Student ServicesBioJasmine Cox is a PhD candidate in Electrical Engineering. She received her B.S. in Electrical Engineering with a minor in Applied Mathematics from Boise State University in 2020. During her undergraduate academic career, Jasmine was a Ronald E. McNair Scholar and a member of the Advanced Nanomaterials and Manufacturing Laboratory focusing on additive manufacturing of flexible hybrid electronics. Her current research as a member of Prof. Debbie G. Senesky’s group, EXtreme Environment Microsystems Lab (XLab), explores the synthesis, fabrication, and characterization of devices and materials in extreme environments that can be found in space.
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Onat Dalmaz
Ph.D. Student in Electrical Engineering, admitted Autumn 2023
Current Research and Scholarly InterestsMy current research centers on developing mathematical tools to enhance the explainability of image reconstruction algorithms in computational magnetic resonance imaging (MRI). By integrating principles from machine learning, signal processing, and generative models, I aim to improve the transparency and reliability of AI applications in medical imaging.
