Hao Yang

👋 Hey, I’m Hao Yang

I’m a postdoctoral research fellow at the CityUHK Architecture Lab for Arithmetic and Security (CALAS), in the Department of Electrical Engineering at City University of Hong Kong.

My research centers on Fully Homomorphic Encryption (FHE), lattice-based cryptography, and privacy-preserving machine learning (PPML) — with a focus on making these primitives practical through GPU acceleration, embedded systems, and CPU assembly optimization.

I’ve published 8+ first- or second-author papers in venues including Nature Machine Intelligence, CHES, IEEE TIFS, TDSC, TC, and TPDS, and I serve as a reviewer for IEEE Internet of Things Journal, TC, and TII.

Selected Publications

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  1. cuFalcon: An adaptive parallel GPU implementation for high-performance Falcon acceleration IEEE TPDS 2026 Forthcoming
  2. Towards compute-efficient Byzantine-robust federated learning with fully homomorphic encryption Nat. Mach. Intell. 2025 15 citations
  3. VeloFHE: GPU acceleration for FHEW and TFHE bootstrapping CHES 2025 9 citations
  4. Phantom: A CUDA-accelerated word-wise homomorphic encryption library IEEE TDSC 2024 54 citations
  5. High-throughput GPU implementation of Dilithium post-quantum digital signature IEEE TPDS 2024 41 citations
  6. CARM: CUDA-accelerated RNS multiplication in word-wise homomorphic encryption schemes for Internet of Things IEEE TC 2022 30 citations