A General Framework for Auditing Differentially Private Machine Learning. - Fred Lu; Joseph Munoz; Maya Fuchs; Tyler LeBlond; Elliott Zaresky-Williams; Edward Raff; Francis Ferraro; Brian Testa (NeurIPS, 2022)

Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile. - Tyler LeBlond; Joseph Munoz; Fred Lu; Maya Fuchs; Elliott Zaresky-Williams; Edward Raff; Brian Testa (AISec@CCS, 2023)

TISCC: A Surface Code Compiler and Resource Estimator for Trapped-Ion Processors. - Tyler LeBlond; Ryan S. Bennink; Justin G. Lietz; Christopher M. Seck (SC Workshops, 2023)

Advanced Resource Estimation through Lattice Surgery Compilation and Logical Error Modeling. - Tyler LeBlond; Ryan S. Bennink (QCE, 2024)