Hoda Naghibijouybari joined the Computer Science Department at Binghamton University in Fall 2020 as an Assistant Professor. She received her PhD in computer science from University of California, Riverside in 2020, working with Professor Nael Abu-Ghazaleh. Her primary research interests are in the area of computer architecture, and security. Her current research focuses on architecture support for security, microarchitectural attacks, GPU security, and heterogeneous systems. Her research has resulted in the discovery of new attacks that have been disclosed to GPU companies, and received coverage from technical news outlets. Her paper on GPU security (published in CCS-2018) was selected for Top Picks in Hardware and Embedded Security in 2019.

I am a second-year PhD student in Electrical and Computer Engineering interested in new and niche logic families for high-performance computing purposes, including temporal-based encoding (Race Logic), and exploring superconducting architectural and circuit design trade-offs between area, energy, and tolerance to variability. My comfort zone is on the hardware side, but I have co-authored several papers at architecture venues on novel logic paradigms applied to superconducting technologies. I am currently pursuing several projects, including new neuromorphic gates in superconducting electronics, and new fan-out techniques for the same technology. I have a diverse background ranging from instrumentation and testing in High-Energy Physics to embedded development in the health and wellness industry, and I received my undergraduate degree in Electrical Engineering from University of California, Santa Cruz. If you would like to hear about my research, please reach out!

Modern computing platforms are fundamentally energy-limited. My research focuses on how to improve the energy efficiency of computing and spans two major themes: (1) Pushing Traditional (CMOS-based) Computing to Its Limits; (2) Computing with post-CMOS Devices/Paradigms.

I am a research scholar in the Electrical Engineering and Computer Science department at University of California, Irvine advised by Prof. Nader Bagherzadeh. I am interested lie in Accelerator based architecture, memory systems, multicore and parallel computing, and heterogeneous architectures. Currently, I am working on mitigating memory bottleneck in deep neural networks (DNN) accelerator based architectures.