What made you do this? Understanding black-box decisions with sufficient input subsets. - Brandon Carter 001; Jonas Mueller 001; Siddhartha Jain 001; David K. Gifford (AISTATS, 2019)

Antibody complementarity determining region design using high-capacity machine learning. - Ge Liu; Haoyang Zeng; Jonas Mueller 001; Brandon Carter 001; Ziheng Wang; Jonas Schilz; Geraldine Horny; Michael E. Birnbaum; Stefan Ewert; David K. Gifford (Bioinform., 2020)

Critiquing Protein Family Classification Models Using Sufficient Input Subsets. - Brandon Carter 001; Maxwell L. Bileschi; Jamie Smith; Theo Sanderson; Drew Bryant; David Belanger 002; Lucy J. Colwell (J. Comput. Biol., 2020)

Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy. - Lucas Liebenwein; Cenk Baykal; Brandon Carter 001; David Gifford 001; Daniela Rus (MLSys, 2021)

Overinterpretation reveals image classification model pathologies. - Brandon Carter 001; Siddhartha Jain 001; Jonas Mueller 001; David Gifford 001 (NeurIPS, 2021)

Machine learning optimization of peptides for presentation by class II MHCs. - Zheng Dai; Brooke D. Huisman; Haoyang Zeng; Brandon Carter 001; Siddhartha Jain 001; Michael E. Birnbaum; David K. Gifford (Bioinform., 2021)

Maximum n-times Coverage for COVID-19 Vaccine Design. - Ge Liu; Brandon Carter 001; David K. Gifford (CoRR, 2021)

Maximum n-times Coverage for Vaccine Design. - Ge Liu; Alexander Dimitrakakis; Brandon Carter 001; David K. Gifford (ICLR, 2022)

Embedding Comparator: Visualizing Differences in Global Structure and Local Neighborhoods via Small Multiples. - Angie W. Boggust; Brandon Carter 001; Arvind Satyanarayan (IUI, 2022)

Interpretations of Machine Learning and Their Application to Therapeutic Design - Brandon Carter 001 (2023)