School of Engineering
Showing 1-20 of 374 Results
-
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.
-
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.
-
Nancy Ammar
Ph.D. Student in Electrical Engineering, admitted Autumn 2021
BioNancy 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).
-
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.
