Reyan Ahmed

Reyan Ahmed serves as an Assistant Professor of Practice at The University of Arizona, holding a Ph.D. from the institution with office GS 831. His academic profile bridges theoretical computer science and practical visualization systems.

• Ph.D., The University of Arizona

Dr. Ahmed's research centers on graph-theoretic foundations with emphasis on sparsification techniques, spanner constructions, and geometric optimization. His work advances computational geometry through polygon-based facility assignments and stress-invariant metrics, while pioneering neural network integrations for Steiner tree approximations and vertex-splitting algorithms. Key contributions include multi-level graph sketching frameworks and scalable layout methodologies addressing NP-hard combinatorial problems.

Analysis of his 2021-2025 publications reveals three dominant threads: (1) Algorithmic innovations in graph sparsification using machine learning-assisted search, (2) Theoretical advancements in additive spanners with lightness guarantees, and (3) Practical visualization systems for evolving networks. His methodology consistently merges discrete mathematics with gradient-based optimization for real-world network applications.

Scientific recognition:

• No awards documented in source materials

Research leadership includes development of Monte Carlo tree search frameworks for graph optimization and semantic zooming architectures for massive network visualization. Current projects focus on scale-invariant stress metrics and multi-priority sparsification, with ongoing exploration of neural network accelerators for classical combinatorial problems. No student advising or grant details were provided.

Lab infrastructure supports large-scale graph processing through custom visualization pipelines, though specific team compositions remain undocumented.