A bottom angle of a computer programming screen with coding.

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.

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

HUDSONLab Members

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)