End-to-End Framework for Imputation and State Discovery in Longitudinal Energy Data. - Alexander Ladd; Kwan Ho Ryan Chan; Sam Nguyen; Jose Cadena; Brenda Ng (e-Energy, 2021)

gridds: a data science toolkit for energy grid machine learning. - Alexander Ladd; Indrasis Chakraborty (e-Energy, 2022)

Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models. - Alexander Ladd; Kyung Geun Kim; Jan Balewski; Kristofer E. Bouchard; Roy Ben-Shalom (Frontiers Neuroinformatics, 2022)

Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation. - Fengxue Zhang; Jialin Song; James C. Bowden; Alexander Ladd; Yisong Yue; Thomas Desautels; Yuxin Chen 001 (ICML, 2023)

Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics. - Conor F. Hayes; Felipe Leno da Silva; Jiachen Yang; T. Nathan Mundhenk; Chak Shing Lee; Jacob F. Pettit; Claudio Santiago; Sookyung Kim; Joanne Taery Kim; Ignacio Aravena Solis; Ruben Glatt; Andre R. Goncalves; et al. (CoRR, 2025)