Summary
Pranay Mundra is a PhD student and research engineer with eight years of experience building privacy-preserving algorithms and scalable data systems, currently a Graduate Research Assistant at Yale. His work blends differential privacy, distributed graph algorithms, and practical systems engineering—most recently producing the first distributed LEDP-k-core and triangle counting algorithms with dramatic utility improvements on billion-edge graphs. He has a strong track record of turning theory into fast, usable systems (KOIOS for top-k set similarity, coreset selection yielding 400x speedups) and has collaborated at CSAIL, Caltech, and international research labs. Comfortable in both academic and applied settings, he pairs rigorous proofs and benchmarks with multi-processor simulations and real-world communication models. Based in New Haven, he brings unusual dual fluency in data privacy and high-performance distributed implementations, often leveraging input-sensitive graph properties to tighten guarantees.
8 years of coding experience
4 years of employment as a software developer
Bachelor of Science - BS, Mathematics, Bachelor of Science - BS, Mathematics at University of Washington
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Yale University
Master of Science - MS, Computer Science, 3.8/4.0, Master of Science - MS, Computer Science, 3.8/4.0 at University of Rochester
Neerja Modi School