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Sarvagya Mishra has a diverse range of work experience spanning multiple industries and roles. They began their career as an Engineering Intern at Indian Oil Corporation Limited and NTPC in 2010, followed by a role as a Sr. Network & Security Administrator at Microland Limited from 2013 to 2014.

In 2014, Sarvagya joined the Rochester Institute of Technology as a Graduate Research Assistant, where they worked on a project focused on Critical Infrastructure Protection. During this time, they developed course modules on network and computing infrastructure security, as well as enhanced the project website to disseminate these modules.

In 2015, Sarvagya returned to Cisco as an Engineering Intern, where they designed and developed UEFI BIOS explorer and Security Scanner applications. These applications allowed for EFI firmware image exploration, forensic analysis of EFI firmware volumes and Firmware File System (FFS) files, and anomaly detection in BIOS image structure and contents.

Continuing at Cisco, Sarvagya transitioned to a Software Engineer role in 2016. In this position, they designed and implemented Access Control Lists (ACL) and Quality of Service (QoS) features for Multiprotocol Label Switching (MPLS) networks. They also designed and developed features for Nexus 9000 series Data Center switches, including Buffer/Queue Management, Scheduling, and Priority Flow Control (PFC). Additionally, Sarvagya implemented command line and RESTful APIs for administration and telemetry purposes.

Recently, Sarvagya joined Fungible, Inc. in 2018 as a Member Of Technical Staff, and later became a Principal Engineer in April 2022.

Sarvagya Mishra obtained a Master's Degree in Computing Security from Rochester Institute of Technology, which they pursued from 2014 to 2016. Prior to that, they completed their Bachelor's Degree in Electrical Engineering from Dr. A.P.J. Abdul Kalam Technical University, graduating in 2011. In addition to their formal education, Sarvagya has also obtained certifications in various topics related to machine learning and neural networks from Coursera, including "Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization," "Neural Networks and Deep Learning," and "Structuring Machine Learning Projects." The specific months and years of when they obtained these certifications are not provided.