CEDAR: Modeling impact of component error derating and read frequency on system-level vulnerability in high-performance processors. - Hossein Asadi 001; Alireza Haghdoost 001; Morteza Ramezani; Nima Elyasi; Amirali Baniasadi (Microelectron. Reliab., 2014)

Exploring the impact of memory block permutation on performance of a crossbar ReRAM main memory. - Morteza Ramezani; Nima Elyasi; Mohammad Arjomand; Mahmut T. Kandemir; Anand Sivasubramaniam (IISWC, 2017)

GCN meets GPU: Decoupling "When to Sample" from "How to Sample". - Morteza Ramezani; Weilin Cong; Mehrdad Mahdavi; Anand Sivasubramaniam; Mahmut T. Kandemir (NeurIPS, 2020)

On Provable Benefits of Depth in Training Graph Convolutional Networks. - Weilin Cong; Morteza Ramezani; Mehrdad Mahdavi (NeurIPS, 2021)

On the Importance of Sampling in Learning Graph Convolutional Networks. - Weilin Cong; Morteza Ramezani; Mehrdad Mahdavi (CoRR, 2021)

GraphGuess: Approximate Graph Processing System with Adaptive Correction. - Morteza Ramezani; Mahmut T. Kandemir; Anand Sivasubramaniam (Euro-Par, 2022)

Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks. - Morteza Ramezani; Weilin Cong; Mehrdad Mahdavi; Mahmut T. Kandemir; Anand Sivasubramaniam (ICLR, 2022)

Predicting Protein-Ligand Docking Structure with Graph Neural Network. - Huaipan Jiang; Jian Wang 094; Weilin Cong; Yihe Huang; Morteza Ramezani; Anup Sarma; Nikolay V. Dokholyan; Mehrdad Mahdavi; Mahmut T. Kandemir (J. Chem. Inf. Model., 2022)

LiGNN: Graph Neural Networks at LinkedIn. - Fedor Borisyuk; Shihai He; Yunbo Ouyang; Morteza Ramezani; Peng Du; Xiaochen Hou; Chengming Jiang; Nitin Pasumarthy; Priya Bannur; Birjodh Tiwana; Ping Liu 002; Siddharth Dangi; et al. (KDD, 2024)