Hardware Utility Design and Software Optimization Networking Laboratory
The Hardware Utility Design and Software Optimization Networking Lab (HUDSONLab) conducts research on future chip design, data science, and high-performance computing and graph algorithms that can be used to address global challenges.
HUDSONLab Members
Research Areas
High-Performance Spectral Methods for Numerical and Graph Problems
Hardware Acceleration of Numerical and CAD Algorithms
Integrated Circuits and Systems
VLSI Design and Computer-Aided Design (CAD)
Hardware and Software Co-Designed System for Graph Analytics and Machine Learning
Lower Level System (E.G. OS) Research, Cloud Computing, Numerical Simulation
Peformance- and Energy-Efficient Artificial Intelligence (AI) Training and Inference System Architectures
Cloud Computing (e.g., Serverless Computing) and High-Performance Computing (HPC) System Design
Robust and Trustworthy Distributed Machine Learning (ML) Algorithms and Systems
Current Projects
High-Performance Incremental Spectral Algorithms for Efficient Modeling and Simulation of Large-Scale Integrated Circuits
Zhuo Feng, Funded by NSF (2024-2027)
Co-optimizing Spectral Algorithms and Systems for High-Performance Graph Learning
Zhuo Feng, Funded by NSF (2022-2026)
Learning Circuit Networks from Measurements
Zhuo Feng, Funded by NSF (2022-2025)
Unlimited Sampling ADC Approaches for Radio Interferometers
Rod Kim, Funded by NSF SWIFT (2024-2027)
Energy-Efficient Millimeter-Wave Communications for Space Applications
Rod Kim, Funded by DARPA YFA (2022-2025)
Low-Power CMOS Ground Penetrating Radar for Planetary Sub-Surface Detection
Rod Kim, Funded by NASA APRA (2022-2025)
Towards the Resilient NextG Network Design for Federated Learning over Mobile Devices
Hao Wang, Funded by CISE/MSI/RDP/CNS (2025–2026)
Enhancing Energy Awareness for Efficient Federated Learning over Mobile AI Systems
Hao Wang, Funded by CSR/Core (2024–2028)
Advancing Model Forensics with Systematic Parsing, Injection Detection, and Model Provenance Attribution
Hao Wang, Funded by SaTC/Core (2024–2026)
Harvesting Idle Resources Safely and Timely for Large-scale AI Applications in High-Performance Computing System
Hao Wang, Funded by OAC/Core (2024–2027)
Critical Learning Periods Augmented Robust Federated Learning
Hao Wang, Funded by SaTC/Core (2023–2025)
High-Efficiency Serverless Computing Systems for Deep Learning: A Hybrid CPU/GPU Architecture
Hao Wang, Funded by OAC/CRII (2022–2025)
Past Projects
Spectral Reduction of Large Graph and Circuits Networks
Zhuo Feng, Funded by SHF/NSF (2019-2022)
Scalable Spectral Sparsification of Graph Laplacians and Integrated Circuits
Zhuo Feng, Funded by SHF/NSF (2016-2019)
Leveraging Heterogeneous Manycore Systems for Scalable Modeling, Simulation and Verification of Nanoscale Integrated Circuits
Zhuo Feng, Funded by CAREER/NSF (2014-2019)