At the University of Minnesota, I have been deeply engaged in campus research, focusing on Distributed Systems. Guided by mentors Abhishek Chandra, Jon Weissman, Mitch Terrell, Rankyung Hong, and Nikhil Sreekumar, I explored topics including large-scale distributed IoT systems, optimizing recommendation system storage with embeddings, and improving the lifespan of wearable edge devices through adaptive sampling techniques.

Enterprise IoT Systems

Under Mitch Terrel’s guidance, I developed software to integrate IoT devices from diverse vendors, locations, and use cases into a unified monitoring and automation platform [1]. This included building support for large-scale WebSocket connections and enabling Python code execution. I implemented a fully Dockerized tool featuring a web server, WebSocket server, Redis cache, SQL database, and S3 datastore. Although functional, the project’s focus shifted due to the absence of clear use cases.

Optimized Reccomendation Storage

Under Rankyung Hong’s mentorship, I delved into the use of large language models (LLMs) and embeddings. This exploratory project examined whether embeddings could improve data positioning in recommendation system storage to enhance access speed. While the work was open-ended, it provided an excellent opportunity to deepen my understanding of these advanced concepts.

Wearable Edge Device Longevity Optimizations

Under Nikhil Sreekumar’s mentorship I am currently investigating the utilization for different approaches to reduce wearable device energy consumption using enhanced sampling methods.

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