Publications and Notes Publications John Darges, Laura Weidensager. A Weighted Kernel Method for Approximation that Adapts to Learned Multivariable Structure. Preprint. 2026. [Link] John Darges, Babak Maboudi Afkham, Matthias Chung. Neural Optimal Design of Experiment for Inverse Problems. Journal of Mathematical Imaging and Vision. 2026. [Link] John Darges, Alen Alexanderian, Pierre Gremaud. Variance-based sensitivity of Bayesian inverse problems to the prior distribution. International Journal for Uncertainty Quantification. 2025. [Link] John Darges, Alen Alexanderian, Pierre Gremaud. Extreme learning machines for variance-based global sensitivity analysis. International Journal for Uncertainty Quantification. 2024. [Link] Jun Hu, Zhenkun Guo, Peter E Mcwilliams, John E Darges, Daniel L Druffel, Andrew M Moran, Scott C Warren. Band gap engineering in a 2D material for solar-to-chemical energy conversion. Nano Letters. 2016. [Link] Presentations Neural Optimal Design of Experiment for Inverse Problems. Paradise Bay, Malta. International Conference on Inverse Problems: Modeling and Simulation. June 2026. An Occam’s Razor Approach to Uncovering and Encoding Multivariable Structure in Kernel Learning. Minneapolis, MN, USA. SIAM Conference on Uncertainty Quantification. March 2026. Weighting Inputs by Sensitivity in Random Feature Expansions. Vanderbilt University. Nashville, TN, USA. Shanks Conference: Constructive Functions. May 2025. [Link] Random feature expansions guided by input sensitivity. Emory University. Atlanta, GA, USA. CODES Seminar. March 2025. [Link] Randomized function approximation. North Carolina State University. Raleigh, NC, USA. Applied Mathematics Graduate Student Seminar. November 2023. [Link] Variance-based sensitivity of Bayesian inverse problems to the prior distribution. North Carolina State University. Raleigh, NC, USA. Research Training Group. October 2023. [Link] Identifying important prior hyperparameters in Bayesian inverse problems with efficient variance-based global sensitivity analysis. North Carolina State University, Raleigh, NC, USA. Applied Mathematics Graduate Student Seminar. April 2023. [Link] Extreme learning machines for variance-based global sensitivity analysis. RAI Amsterdam Convention Center, Amsterdam, Netherlands. SIAM Conference on Computational Science and Engineering. March 2023. [Link] Extreme learning machines for variance-based global sensitivity analysis. Walter E. Washington Convention Center, Washington, D.C., USA. Joint Statistical Meetings. August 2022. [Link] Extreme learning machines for variance-based global sensitivity analysis. Florida State University, Tallahassee, FL, USA. Conference on Sensitivity Analysis of Model Output (SAMO). March 2022. [Link] Research Notes On high probability of universal approximation in random basis expansions with non-continuous weight sampling. 2026. [Link] Measuring the additivity of functions with variance-based global sensitivity analysis. 2022. [Link] On the approximation of higher order Sobol’ indices with ELM surrogates. 2021. [Link] Global sensitivity analysis for optimization under uncertainty. 2020. [Link] Course Notes and Projects Uncertainty quantification for heat transfer in turbulent flow. 2021. [Link] Classifying the winning player in Connect Four with machine learning. 2020. [Link] Introduction to sub-Riemannian geometry. 2020. [Link] Riemannian structure on Lie groups. 2020. [Link]