Detroit Metropolitan Area
Computer Science BS/MS Alum @Georgia Tech Currently a software engineer for KLA Corporation.
● Currently serving as a lead engineer for a new AI initiative driven team focused on evaluating and applying emerging AI usage techniques to improve software engineering productivity, system reliability, and operational efficiency. ● Formerly SWE on the Infrastructure, Site Reliability Engineering (SRE), and DevOps team developing reliable, scalable, maintainable, and efficient data solutions.
● Head TA for CS 8803, AI Privacy Engineering, taught by Dr. Annie Antón
● Developed a comprehensive LLM system to convert radio calls to text and produce a list of all plays in a game to > 95% accuracy. ● Contributed to a team effort to implement CNNs and Markov Chains to classify and predict thousands of Georgia Tech football video clips. ● Organized a team-wide data cleaning effort of video clips. ● Investigated incorrectly classified video clips to identify patterns and flaws in our models. ● Submitted paper to the 2024 International Sports Analytics Conference and Exhibition, where our paper was accepted and won the best paper award. ● Paper published in Springer Nature following ISACE, citation below. Patel, S., Raj, P., Gao, A. et al. The Smart Stadium Testbed for Sports Analytics Systems Research, Development and Deployment. SN COMPUT. SCI. 6, 113 (2025). https://doi.org/10.1007/s42979-024-03633-3
● Conducted comprehensive research and analysis of various solutions to enable the transition from a serial to a parallelized Kubernetes architecture for KLA’s FleetPack insight generation tools. ● Developed a comprehensive Proof of Concept utilizing Argo Workflows and Argo Events infrastructure for efficient parallel application execution. ● Implemented a custom control system utilizing the Kubernetes C# API and asynchronous programming and multithreading, enabling fully automated parallel job scheduling execution.
● Researched, identified, and compared suitable data streaming technologies, allowing KLA engineers to detect issues with wafer testing machines and minimize downtime. ● Deisgned a new data streaming based architecture plan to replace the batch job scheduler that would seamlessly integrate into the production branch tech stack. ● Created a functional Proof of Concept based off this plan using Apache Spark, Apache Kafka, and Apache Camel converting the current batch-based job scheduling system to a real-time streaming architecture that received live data and concurrently ran predictive algorithms that output live insights. ● Presented weekly research powerpoints to the entire FleetHub team, keeping team members educated and up to date with the fine details of live data streaming technologies.