Navid Paydavosi

Navid Paydavosi is an accomplished professional specializing in Solid-state Physics, Quantum Physics, Nanotechnology, Microelectronics, and Nanoelectronics. With expertise in Theory, Simulation, Fabrication, Computational Physics, and Modeling, he excels in Characterization, Performance Analysis, and Yield Analysis of Semiconductor Devices. Proficient in Digital Circuits, Layout, Micro-Architecture, VLSI, and Power Optimization. Skilled in programming languages like C, MATLAB, Python, and tools such as SPICE, Verilog, and RTL. Experienced in Data Analysis, Design of Experiments (DOE), Statistics, and Machine Learning.

Having a strong background in Quantum Computing, Hardware, Accelerator, and GPU technologies, Navid's goal is to design fast and low-power hardware for deep learning and quantum computing applications. His educational journey includes a Ph.D. in Electrical Engineering from the University of Alberta, a B.Sc. in Electrical Engineering from Shahid Beheshti University, and a Diploma in Mathematics and Physics from Allameh Helli school.

Throughout his career, Navid has held various roles at prestigious organizations. He worked in NAND Memory Performance/Power Optimization, as a GPU Power Micro-Architect, and as a Process Engineer at Intel Corporation. Additionally, he served as a Postdoctoral Scholar at UC Berkeley, a Research Associate, Co-supervisor, and Research Assistant at the University of Alberta. He also contributed as a Teaching Assistant during his academic journey and gained valuable industry experience as a Summer Student Engineer at Samsung Electronics.