Alan Kuhnle

Assistant Professor, Computer Science & Engineering, Texas A&M University

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Contact: kuhnle at tamu.edu

About

I am an Assistant Professor of Computer Science & Engineering at Texas A&M University. My work focuses on the design and analysis of scalable algorithms for ubiquitous combinatorial optimization problems arising in data science applications, such as vehicle routing and marketing on social networks.

The main directions of my research are:

• Scalable algorithms for optimization of submodular functions
• Automatically learning and enhancing algorithms for combinatorial optimization by reinforcement learning.

For more information about our research, please see the research page, group members, and our publications.

Selected Publications

• Best of Both Worlds: Practical and Theoretically Optimal Submodular Maximization in Parallel
  Yixin Chen, Tonmoy Dey, Alan Kuhnle
  Neural Information Processing Systems (NeurIPS), 2021.
  [full version]
• Quick Streaming Algorithms for Maximization of Monotone Submodular Functions in Linear Time
  Alan Kuhnle
  Artificial Intelligence and Statistics (AISTATS), 2021
  [full version]

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Teaching

At Texas A&M:

• Fall 2023
  • CSCE689: Algorithmic Game Theory
• Fall 2022
  • CSCE222: Honors -- Discrete Structures for Computing

At Florida State University:

• Spring 2022
  • COT5405: Advanced Algorithms
• Fall 2021
  • COP4521: Secure, Parallel, and Distributed Programming in Python
• Spring 2021
  • CIS4930/5930: Fundamentals of Machine Learning Algorithms
• Fall 2020
  • COT5405: Advanced Algorithms
• Fall 2019
  • CIS4930/5930: Machine Learning