Algorithms · Machine Learning · Multi-Agent Systems

Alan Kuhnle

I design scalable algorithms for submodular optimization and apply them to data selection, multi-agent systems, and environmental monitoring.

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Assistant Professor of Computer Science & Engineering at Texas A&M University. My work spans approximation algorithms, data pruning, cooperative game theory, and applied machine learning.

Google Scholar · DBLP · kuhnle@tamu.edu

Current directions

Scalable optimization with real-world reach

Submodular optimization

Streaming, parallel, and adaptive algorithms with provable approximation guarantees.

Data selection

Ground-set pruning and context selection that make large optimization problems tractable.

Multi-agent systems

Game-theoretic coordination, value allocation, and environmental monitoring applications.