Assistant Professor

Nick Cheney is an Assistant Professor of Computer Science and a core faculty in Complex Systems and Data Science. His research is in bio-inspired artificial intelligence. His research lab, the UVM Neurobotics Lab, draws inspiration from natural systems, and especially biological learning processes (e.g. evolution, development, and lifelong learning) to design machine learning algorithms which create more flexible, scalable, and context-aware robots and decision-making systems (particularly, deep neural networks). This work in automated machine learning (AutoML) aims to automate many of the unintuitive challenges of designing robot and machine learning systems/architectures/pipelines -- helping to reduce the barriers to entry for those new to the field. His lab also works with domain experts across a variety of fields, helping to automate and scale their scientific and clinical pipelines towards this goal.

Prior to joining the faculty at UVM, Nick was a Research Assistant Professor at the University of Wyoming, working alongside Jeff Clune. Nick received his Ph.D. from Cornell University, studying Computational Biology under Hod Lipson and Steve Strogatz, while also serving as a research fellow at NASA Ames, the Santa Fe Institute, and Columbia University. Prior to that, Nick received a B.S. in Applied Mathematics from the University of Vermont.


 Beaulieu, S., Frati, L., Miconi, T., Lehman, J., Stanley, K. O., Clune, J. & Cheney, N. (2020). Learning to Continually Learn. 24th European Conference on Artificial Intelligence.

Cheney, N., Bongard, J., SunSpiral, V., & Lipson, H. (2018). Scalable co-optimization of morphology and control in embodied machines. Journal of The Royal Society Interface, 15(143), 20170937.

Cheney, N., MacCurdy, R., Clune, J., & Lipson, H. (2013). Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding. In Proceedings of the Fifeenth Annual Conference on Genetic and Evolutionary Computation (pp. 167-174). ACM.

Nick Cheney

Areas of Expertise and/or Research

Machine Learning, Deep Learning, Meta-Learning, AutoML, Evolutionary Robotics, Soft Robotics


  • Ph.D., Computational Biology - Cornell University
  • B.S., Applied Mathematics - University of Vermont


  • 802-656-3138
Office Location:

E411 Innovation Hall

Courses Taught

CS/CSYS 287 -- Data Science I
CS/CSYS 352 -- Evolutionary Computation