Zhonghao Chen

Zhonghao Chen

Ph.D. Student in ECE

University of Florida

University of California, Merced

About Me

I am a Ph.D. student in Electrical and Computer Engineering. My research focuses on high-performance computing and networking, runtime and communication systems, and scalable training/inference of large language models, with a particular interest in distributed and federated learning systems and AI–HPC co-design.

Interests

High-Performance Computing
Networking and Communication
LLM Training/Inference
Federated Learning Systems
AI–HPC Co-design

Education

Ph.D. in Electrical and Computer Engineering
University of Florida
Ph.D. in Electrical Engineering and Computer Science
University of California, Merced
B.E. (with Honors), Computer Science and Technology
Tianjin University

Research

I am a Ph.D. student in Electrical and Computer Engineering at the University of Florida, advised by Prof. Xiaoyi Lu.

My research interests include high-performance computing and networking, runtime and communication systems, scalable training and inference of large language models, distributed and federated learning systems, and AI–HPC co-design.

Selected Publications

Federated Learning

When RDMA Goes Long-Haul: Characterization, Modeling, and Verbs-Level Emulation with Implications for Federated Learning

Characterization, modeling, and verbs-level emulation of long-haul RDMA with implications for federated learning.

Jan 1, 2026

Federated Learning

Can Long-Haul RDMA Benefit Federated Learning?

Communication-centric study of long-haul RDMA for geo-distributed federated learning.

Jan 1, 2025

Federated Learning

FedDES: Discrete Event Based Performance Simulation for Federated Learning Systems

Discrete-event based performance simulation for federated learning systems across heterogeneous compute/network settings.

Jan 1, 2025

High-Performance Computing

HPC-R1: Characterizing R1-like Large Reasoning Models on HPC

Characterization of R1-like large reasoning models on HPC-scale GPU clusters and interconnects.

Jan 1, 2025

See all publications

Selected Projects

High-Performance Computing

HPC-AI Convergence

This project targets HPC-AI convergence for efficient large-scale machine learning, including scheduling, optimization, characterization, and fault-tolerant training systems. The project includes: HPC-R1, a characterization of inference and distillation performance for large reasoning models on HPC-scale GPU clusters and interconnects. SPARe, a fault-tolerant LLM pretraining system for 100k+ GPU scale using stacked parallelism and adaptive reordering.

Jan 1, 2025

Federated Learning

Long-Haul RDMA

This project investigates long-haul RDMA for geo-distributed machine learning systems. The project includes: Characterization, modeling, and verbs-level emulation of long-haul RDMA behavior. Evaluation of whether long-haul RDMA can improve geo-distributed federated learning, including simulation and validation on a real-world testbed.

Jan 1, 2025

Federated Learning

Scalable, Resilient Federated Learning

SRFL targets scalable and resilient federated learning systems across heterogeneous compute and network environments. The project includes: FedDES, a discrete-event based performance simulation framework for federated learning systems. FedMECA, a memory-efficient and concurrent aggregation approach for scalable federated learning. Long-haul RDMA studies for geo-distributed federated learning, including simulation, modeling, and real-world testbed validation.

Jan 1, 2025