Working as lead research scientist in the Algorithmic Solutions Group, working on problems on machine learning, statistical inference, and signal processing.
Focus on optimizing ML models for the edge, driving impact through deep learning and Bayesian inference through collaborations across business and research teams.
Coordinate Science Lab
Thesis: "On the Information Theory of Clustering, Registration, and Blockchains"
Committee: Lav R. Varshney, Pierre Moulin, Venugopal V. Veeravalli, Andrew Miller
Worked under the guidance of Prof. Lav Varshney on the design and information theoretic analysis of learning algorithms on crowdsourcing, image registration, blockchain systems, and network science.
Electrical and Computer Engineering
Course: Information Theory
Science for Social Good
Designed novel blockchain-based solutions for establishing trust in multi-agent computing environments with untrusting individual agents
Used compression-based approximation methods to design a scalable solution and developed a universal (simulation-independent) prototype
Telemetry Group
Designed novel algorithms for aligning non-overlapping borehole image segments using information theory
Implemented the algorithm and evaluated performance on real datasets, benchmarking against prior art
Department of Electrical Engineering
Courses : Communication Techniques; Introduction to Wireless and Mobile Communication
Performance Analysis Lab
Advisor: Prof. Vinod Sharma
Project: Distributed Algorithm Design for Multi-hop Wireless Networks
Designed distributed algorithms for scheduling, routing, and power control in multi-hop wireless networks
Performed Monte-Carlo simulation study of distributed approximations of throughput optimal policy