School of Engineering
Showing 1-100 of 359 Results
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Phil Adamson
Ph.D. Student in Electrical Engineering, admitted Autumn 2020
BioPhil is an Electrical Engineering PhD student conducting inter-disciplinary medical imaging research in the Radiological Sciences Laboratory in the Stanford Medicine Department of Radiology. His research interests include MR methods for metabolic imaging, particularly Deuterium Metabolic Imaging (DMI), and Deep Learning methods for solving inverse problems in limited data regimes with applications to MRI.
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Geun Ho Ahn
Ph.D. Student in Electrical Engineering, admitted Autumn 2018
BioI am a PhD candidate in Electrical Engineering working at Professor Jelena Vuckovic's Nanoscale Quantum Photonics Laboratory. My research interests are computational optimizations of photonic devices and quantum technologies made from nanoscale fabrications.
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Nancy Ammar
Ph.D. Student in Electrical Engineering, admitted Autumn 2021
Masters Student in Electrical Engineering, admitted Spring 2024BioNancy Y. Ammar received her B.Sc. degree (with honors) in electronics and communication engineering from Ain Shams University, Cairo, Egypt, in 2019. In her senior year, she worked as an undergraduate Research Assistant in the Microwaves and Antenna Research Lab at Ain Shams University. She worked as an IC design consultant at Siemens EDA (Mentor Graphics previously).
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Serhat Arslan
Ph.D. Student in Electrical Engineering, admitted Autumn 2018
Current Research and Scholarly InterestsNetwork intelligence
There are 2 main aspects of network management:
Sensing
- Collecting useful and enough amount of information from the network is essential for modern, data-centric decision processes to work well.
Frameworks such as In-band Network Telemetry could be utilized to collect precise information on every single packet in the network.
Control
- Modern data science methodologies allow engineers to infer about the state of the network.
Naturally, the next step is to design tailored control algorithms that would utilize available resources the best.
Potential methods include, but not limited to, machine learning algorithms and control theory. -
Nikhil Bhagdikar
Ph.D. Student in Electrical Engineering, admitted Autumn 2014
BioEase of implementation and energy efficiency are critical for modern digital ICs. I am researching techniques to improve energy efficiency without compromising on performance or silicon area, especially for CGRA.
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Emil Biju
Masters Student in Electrical Engineering, admitted Autumn 2023
BioI am an M.S. in Electrical Engineering student at Stanford University and my research interests include interpretable machine learning, deep learning and NLP. For the last 2 years, I have been working at Microsoft as a Data and Applied Scientist in the Cybersecurity research team. Previously, I graduated with a Bachelors degree in Electrical Engineering and a minor in Deep Learning from the Indian Institute of Technology (IIT) Madras. During this time, I pursued research at the intersection of NLP and deep learning that led to publications in top conferences such as ACL, COLING and ALENEX.
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Alex Carsello
Ph.D. Student in Electrical Engineering, admitted Autumn 2017
BioAlex is currently a Ph.D. student in Electrical Engineering advised by Mark Horowitz and affiliated with the AHA! Agile Hardware Center. He is interested in reconfigurable computing, domain-specific architectures for image processing, and hardware design methodology. He is currently working within the AHA Agile Hardware Project on a next-generation CGRA (coarse-grained reconfigurable architecture) chip generator. Alex received a B.S. in Electrical and Computer Engineering from Washington University in St. Louis in 2017.
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Hugo Chen
Ph.D. Student in Electrical Engineering, admitted Autumn 2022
BioHugo "Jiun-Yu" Chen is currently pursuing his Ph.D. degree in the Department of Electrical Engineering at Stanford University. He earned his M.S. in Photonics and Optoelectronics from National Taiwan University in 2019 and his B.S. in Materials Science and Engineering from National Dong Hwa University in 2017.
Prior to joining Stanford, Hugo worked as an R&D engineer at Taiwan Semiconductor Manufacturing Company (TSMC) in the High Power Program and Analog Power/RF Specialty Technology from 2019 to 2022. His research experience includes investigating GaN high electron mobility transistors (HEMTs) for high power converter applications, oxide-based thin-film transistors (TFTs) for CMOS inverter applications, and III-V quantum dots molecular beam epitaxy (MBE) material growth.
As the first author, Hugo has published two peer-reviewed journal articles, six conference papers, and one US/KR/TW/CN/DE patent. He is currently advised by Professors H.-S. Philip Wong and Kwabena Boahen, and his research focuses on developing ferroelectric field-effect transistors (FeFETs) for dendritic-centric learning.
In his leisure time, Hugo enjoys biking, playing badminton, and watching dramas. -
Po-Han Chen
Ph.D. Student in Electrical Engineering, admitted Winter 2021
Masters Student in Electrical Engineering, admitted Winter 2022BioPo-Han Chen is an EE Ph.D. student at Stanford University supervised by Prof. Priyanka Raina. He received his B.S. in Electrical Engineering and Computer Science (EECS) and M.S. in Electrical Engineering from National Tsing Hua University (Taiwan) in 2016 and 2018 respectively. Before joining Stanford, he was a digital circuit designer at MediaTek where he worked on developing hardware architectures of image processing pipeline. He is interested in designing hardware accelerators. Most of his previous works were related to computational photography algorithms such as digital refocusing. Currently, He is focusing on analyzing and designing architecture of CGRAs to create high-performance, energy-efficient, and reconfigurable computing platforms.
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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 from the theoretical perspective. 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. -
Benjamin Choi
Masters Student in Electrical Engineering, admitted Autumn 2019
BioWebsite: benchoi.me
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Jasmine M. Cox
Ph.D. Student in Electrical Engineering, admitted Autumn 2020
BioJasmine 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 InterestsI am interested in developing novel deep generative models for multi-modal medical imaging, particularly for medical image synthesis. My research aims to improve diagnostic information and patient comfort while decreasing examination costs and toxicity/radiation exposure. I devise new deep architectures and robust learning strategies for medical image reconstruction and synthesis techniques.
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Arjun Desai
Ph.D. Student in Electrical Engineering, admitted Autumn 2019
BioArjun Desai is a 4th year PhD candidate working with Akshay Chaudhari and Chris Ré. He is broadly interested in how we can accelerate the pace at which artificial intelligence can be used robustly, safely and at scale in practice. His research focuses on the intersection of signal processing and machine learning and how we can build scalable deployment and validation systems for applications in heathcare and the sciences.
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Yiwen Dong
Ph.D. Student in Civil and Environmental Engineering, admitted Autumn 2020
Ph.D. Minor, Electrical EngineeringBioYiwen Dong is a Ph.D. student in the Department of Civil and Environmental Engineering at Stanford University, advised by Prof. Hae Young Noh. Her research interest is human behavior characterization and health monitoring through their interactions with the physical structures. Her current work focuses on human and animal health monitoring through footstep/activity-induced structural vibrations.
While structures are traditionally considered as passive and indifferent, her works allow the structures to be both self-aware and user-aware. Yiwen developed systems that utilize ambient structural vibrations to infer human behaviors and health status, which enables many smart building applications such as in-home patient monitoring and elder care, intruder prevention and occupant management, animal health monitoring, and welfare. She strives for the next-generation intelligent infrastructures by exploring the potential of structural monitoring for human-centered purposes.
Yiwen has an interdisciplinary background in structural engineering, electrical engineering, and machine learning. Yiwen received her Master’s degree in Structural Engineering at Stanford University and her Bachelor’s degree in civil engineering at Nanyang Technological University. She won various awards (Best Paper Award, runner-ups in competitions) in ubiquitous computing and cyber-physical system conferences. She is passionate about combining the physical knowledge from structural dynamics, sensing approaches from cyber-physical systems, and data-driven models from machine learning to infer people’s behavior patterns and health status. -
Aidan James Fitzpatrick
Ph.D. Student in Electrical Engineering, admitted Autumn 2018
BioAIDAN FITZPATRICK received the B.S. degree in electrical and computer engineering from the University of Massachusetts Amherst, in 2018, and the M.S. degree in electrical engineering from Stanford University in 2020, where he is currently pursuing the Ph.D. degree in electrical engineering.
His current research interests are in computational imaging - specifically at the intersection of electromagnetics, acoustics, and signal processing for the codesign of imaging algorithms and system hardware for non-contact thermoacoustic/photoacoustic, and millimeter wave applications.
