Vinesh Sridhar is a Technical Advisor at Reichman Jorgensen Lehman & Feldberg LLP, where he draws on his computer science background to help the firm’s attorneys understand and analyze the technology at issue in patent litigation. He earned his Ph.D. in Computer Science from the University of California, Irvine’s Center for Algorithms and Theory of Computation.

His doctoral research included the development of a cryptographic protocol for on-demand auditing and recovery of cloud data, routing algorithms that improve the speed of Wi-Fi networks, memory-efficient algorithms for data centers, and fault-tolerant geometric algorithms. In addition, he has contributed to the theory of machine learning by presenting new algorithmic techniques for non-Euclidean geometries.

Vinesh also has industry experience in product management and software quality assurance. As Product Manager at Euna Procurement (powered by EqualLevel), he worked with executives, engineers, and customers to develop an e-invoice platform designed to streamline procurement workflows for public schools and local governments.

Academic Publications

• “Improved Algorithms for Voronoi Diagrams, Delaunay Triangulations, and Support Hulls in Polygonal Hilbert Geometries,” Co-author, CCCG (2026)

• “Raiders of the Lost Log: Synchronous Parallel In-Place Models and Algorithms,” Co-author, APDCM (2026)

• “How to Sort in a Refrigerator: Simple Entropy-Sensitive Strictly In-Place Sorting Algorithms,” Co-author, LATIN (2026)

• “Optimal Parallel Algorithms for Convex Hulls in 2D and 3D under Noisy Primitives,” Co-author, CCCG (2025)

• “Computational Geometry with Probabilistically Noisy Primitive Operations,” Co-author, WADS (2025)

• “Fast Geographic Routing in Fixed-Growth Graphs,” Co-author, CIAC (2025)

• “Dynamic Accountable Storage: An Efficient Protocol for Real-Time Cloud Storage Auditing,” Co-author, ALGOCLOUD (2024)

• “Fast Area-Weighted Peeling of Convex Hulls for Outlier Detection,” Co-author, CCCG (2024)