Towards Fast and Scalable Private Inference. - Jianqiao Mo; Karthik Garimella; Negar Neda; Austin Ebel; Brandon Reagen (CF, 2023)

Generating High-Performance Number Theoretic Transform Implementations for Vector Architectures. - Naifeng Zhang; Austin Ebel; Negar Neda; Patrick Brinich; Benedict Reynwar; Andrew G. Schmidt; Mike Franusich; Jeremy Johnson 001; Brandon Reagen; Franz Franchetti (HPEC, 2023)

Orion: A Fully Homomorphic Encryption Compiler for Private Deep Neural Network Inference. - Austin Ebel; Karthik Garimella; Brandon Reagen (CoRR, 2023)

CiFlow: Dataflow Analysis and Optimization of Key Switching for Homomorphic Encryption. - Negar Neda; Austin Ebel; Benedict Reynwar; Brandon Reagen (ISPASS, 2024)

Osiris: A Systolic Approach to Accelerating Fully Homomorphic Encryption. - Austin Ebel; Brandon Reagen (CoRR, 2024)

Economics and optimal investment policies of Attackers and Defenders in cybersecurity. - Austin Ebel; Debasis Mitra (J. Cybersecur., 2024)

Orion: A Fully Homomorphic Encryption Framework for Deep Learning. - Austin Ebel; Karthik Garimella; Brandon Reagen (ASPLOS (2), 2025)

EinHops: Einsum Notation for Expressive Homomorphic Operations on RNS-CKKS Tensors. - Karthik Garimella; Austin Ebel; Brandon Reagen (IACR Cryptol. ePrint Arch., 2025)

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption. - Karthik Garimella; Austin Ebel; Gabrielle De Micheli; Brandon Reagen (CoRR, 2025)