S
Shahin Nazarian
Researcher at University of Southern California
Publications - 127
Citations - 1854
Shahin Nazarian is an academic researcher from University of Southern California. The author has contributed to research in topics: Logic gate & Smart grid. The author has an hindex of 18, co-authored 121 publications receiving 1420 citations. Previous affiliations of Shahin Nazarian include Magma Design Automation.
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Proceedings ArticleDOI
Trust-aware Control for Intelligent Transportation Systems
TL;DR: In this paper, the authors propose a trust-aware controller for autonomous intersection management (AIM) case study and demonstrate how to synthesize trustaware controllers using an approach based on reinforcement learning.
Journal ArticleDOI
In silico design and immunoinformatics analysis of a universal multi-epitope vaccine against monkeypox virus
Samira Sanami,Shahin Nazarian,Sajjad Ahmad,Elham Raeisi,Muhammad Tahir ul Qamar,Shahram Tahmasebian,Hamidreza Pazoki-Toroudi,Maryam Fazeli,Mahdi Ghatreh Samani +8 more
TL;DR: In this paper , a multi-epitope vaccine against MPXV was proposed, which consists of 7 CTL, 4 helper T lymphocyte, 4 linear B lymphocyte and 5 LBL epitopes.
Proceedings ArticleDOI
CGTA: current gain-based timing analysis for logic cells
TL;DR: This paper introduces a new current-based cell timing analyzer, called CGTA, which has a higher performance than existing logic cell timing analysis tools and relies on a compact lookup table storing the output current gain of every logic cell as a function of its input voltage and output load.
Posted Content
Hybrid Cell Assignment and Sizing for Power, Area, Delay Product Optimization of SRAM Arrays
TL;DR: In this paper, a hybrid cell assignment method based on multi-sized and dual-Vth SRAM cells was proposed to improve the PAD cost function by 34% compared to the conventional cell assignment.
Posted Content
Trust-aware Control for Intelligent Transportation Systems
TL;DR: In this article, the authors propose a trust-aware controller for autonomous intersection management (AIM) case study and demonstrate how to synthesize trustaware controllers using an approach based on reinforcement learning.