Research
My research focuses on computational methods for mixed-integer nonlinear programming and derivative-free optimization, where I explore their applications in machine learning, spatial planning, and conservation decision-making.
The 30x30 initiative—preserving 30% of the earth’s lands and waters by 2030—is a particular interest of mine and I explore situations where optimization and machine learning responsibly support this goal.
Publications
Decision-Making for Land Conservation: A Derivative-Free Optimization Framework with Nonlinear Inputs
Cassidy K. Buhler and Hande Y. Benson
Article Preprint Code Poster
Buhler, C. K., & Benson, H. Y. (2024). Decision-Making for Land Conservation: A Derivative-Free Optimization Framework with Nonlinear Inputs. Proceedings of the AAAI Conference on Artificial Intelligence, 38(20), 21932-21939. https://doi.org/10.1609/aaai.v38i20.30195
Optimal Land Conservation Decisions for Multiple Species
Cassidy K. Buhler and Hande Y. Benson
Preprint Article
Buhler, C. K., & Benson, H. Y. (2023). Optimal Land Conservation Decisions for Multiple Species. In Proceedings of the 52nd Northeast Decision Science Institute Annual Conference (Vol. 52, pp. 808-816).
Do Mechanisms Matter? Comparing Cancer Treatment Strategies Across Mathematical Models and Outcome Objectives
Cassidy K. Buhler, Rebecca S. Terry, Kathryn G. Link, Frederick R. Adler.
Article
Cassidy K. Buhler, Rebecca S. Terry, Kathryn G. Link, Frederick R. Adler. Do mechanisms matter? Comparing cancer treatment strategies across mathematical models and outcome objectives[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 6305-6327. doi: 10.3934/mbe.2021315
Working Papers
Regularized Step Directions in Nonlinear Conjugate Gradient Methods
Cassidy K. Buhler, Hande Y. Benson, David F. Shanno
Preprint Code
Under 2nd round of review at Mathematical Programming Computation
Regularized Nonlinear Conjugate Gradient Methods for Machine Learning
Cassidy K. Buhler and Hande Y. Benson
Code