I developed a web-based dump-state analysis tool to automate the diagnosis of various device issues using a full-stack approach. The tool enables log uploads, conditional filtering and highlighting, and automated diagnostics for various issues, reducing diagnosis time by 70% and improved team efficiency. The tool was built with scalable, extensible architecture supporting custom diagnostic rules integration.In addition, made significant contributions to Android UICC and RIL framework to… Show more I developed a web-based dump-state analysis tool to automate the diagnosis of various device issues using a full-stack approach. The tool enables log uploads, conditional filtering and highlighting, and automated diagnostics for various issues, reducing diagnosis time by 70% and improved team efficiency. The tool was built with scalable, extensible architecture supporting custom diagnostic rules integration.In addition, made significant contributions to Android UICC and RIL framework to improve system performance, with solutions slated for commercialization in upcoming models. Show less

Working closely with different teams, I diagnosed and fixed issues within existing models, ensuring peak performance. I created test applications to gather data on solutions under testing, providing valuable feedback throughout the validation process. Furthermore, I shared my knowledge with my team through presentation sessions on TWT, Multiple Enabled Profiles, and Bearer Independent Protocols.

During my internship, I had the opportunity to work with advanced image processing models. Leveraging this expertise, I developed a proof of concept framework that dynamically splits computations between mobile devices and the cloud based on network conditions.

I held the position of Head at the renowned Microsoft Innovation Lab, a student-driven research center. My responsibilities included organizing hackathons and recruitment drives, as well as overseeing two summer internships. These internships focused on cutting-edge research areas such as edge computing, machine learning, and algorithm development.

During my tenure as a Backend Intern, I collaborated with the External Ads team to optimize loading speeds and boost revenue. Additionally, I assisted in multiple feature migrations from the outdated monolithic service to a modern microservice architecture. Furthermore, I drafted a comprehensive data migration plan to seamlessly transfer user data from the older service to the new one with minimal downtime.

Worked on a project: "Context-Based Mythological Scene Mapping", a machine learning project designed to map scenes representing various scenes from mythological stories to the literature of these stories.Have published a conference paper titled "Automated mythological scene recognition using Machine Learning and graphs" which was presented at the International conference of Artificial Intelligence and Signal Processing (AISP '20).