Andrew Nuxoll

About

Andrew M. Nuxoll is an Associate Professor of Computer Science at the University of Portland's Donald P. Shiley School of Engineering. He holds a B.S. in Computer Science from Rose-Hulman Institute of Technology and a Ph.D. in Computer Science and Engineering from the University of Michigan.

Dr. Nuxoll joined the faculty at UP in 2007. He has taught extensively in almost all areas of computer science with particular focus on project-base courses and artificial intelligence.

His research is in artificial intelligence, with a focus on cognitive architectures, episodic memory, and long-term learning in autonomous agents. His work explores how AI systems can use memory-based mechanisms similar to human episodic memory to improve learning and decision-making.

Dr. Nuxoll has held a wide variety of roles at the university.  His most notable service includes multiple terms as department chair and on the faculty senate.  He has been awarded a total of four Fulbright grants as well as an Erskine Fellowship.

Education

• Ph.D., University of Michigan (Ann Arbor, Michigan)
• B.S., Rose-Hulman Institute of Technology (Terre Haute, Indiana)

Links

• LinkedIn
• Google Scholar
• ResearchGate

Recent Publications

1. 1. Megan Ou* , Jacqueline Bouchard* , Landon Harrison* , Eduardo Yuji Sakabe , Ricardo Gudwin, and Andrew Nuxoll. Using Episodic Memory to Aid Deep Reinforcement Learning Performance in Perceptually Aliased Environments. In: Biologically Inspired Cognitive Architectures 2026. BICA 2026. (to be published)
   2. J.C. Cachola*, P. Silliman*, A. Nuxoll. Using Episodic Memory to Aid Machine Learning Performance in Perceptually Aliased Environments. In: Samsonovich, A.V., Ramos, F., Liu, T. (eds) Biologically Inspired Cognitive Architectures 2025. BICA 2025. Studies in Computational Intelligence, vol 1244. Springer, Cham.
   3. B. Tribelhorn, H.E. Dillon, A. Nuxoll, N. Ralston. Using Active Learning to Connect Entrepreneurial Mindset to Software Engineering. Computers in Education Journal, vol. 14, no. 1, 2024.
   4. Braeden Lane*, Connor Morgan*, Kai Vickers*, Max Woods* and Andrew Nuxoll. Towards an Artificial, General Episodic Memory via Learning in Noisy, Perceptually-Aliased Environments, In Advances in Cognitive Systems (ACS), 2022.
   5. Ryan Regier*, Owen Price*, Alex Hadi*, Zachary Faltersack and Andrew Nuxoll. ARO:  A Memory-Based Approach to Environments with State Aliasing,  In Papers from the Association for the Advancement of Artificial Intelligence Spring Symposium Series:Lifelong Machine Learning (AAAI-MAKE), 2020.