<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Publications | Zhonghao Chen</title><link>https://diogeneschen.github.io/publication/</link><atom:link href="https://diogeneschen.github.io/publication/index.xml" rel="self" type="application/rss+xml"/><description>Publications</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Thu, 01 Jan 2026 00:00:00 +0000</lastBuildDate><image><url>https://diogeneschen.github.io/media/icon_hu18301096222111465208.png</url><title>Publications</title><link>https://diogeneschen.github.io/publication/</link></image><item><title>FedMECA: Scalable Federated Learning via Memory-Efficient and Concurrent Aggregation</title><link>https://diogeneschen.github.io/publication/fedmeca-cais-2026/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://diogeneschen.github.io/publication/fedmeca-cais-2026/</guid><description>&lt;p>FedMECA improves federated learning scalability by making aggregation more memory-efficient and concurrent, targeting complex FL workflows with large model and client counts.&lt;/p></description></item><item><title>SPARe: Stacked Parallelism with Adaptive Reordering for Fault-Tolerant LLM Pretraining Systems with 100k+ GPUs</title><link>https://diogeneschen.github.io/publication/spare-icml-2026/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://diogeneschen.github.io/publication/spare-icml-2026/</guid><description>&lt;p>SPARe studies fault-tolerant LLM pretraining at extreme scale, combining stacked parallelism with adaptive reordering to improve resilience and efficiency for 100k+ GPU systems.&lt;/p></description></item><item><title>When RDMA Goes Long-Haul: Characterization, Modeling, and Verbs-Level Emulation with Implications for Federated Learning</title><link>https://diogeneschen.github.io/publication/long-haul-rdma-hpdc-2026/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://diogeneschen.github.io/publication/long-haul-rdma-hpdc-2026/</guid><description>&lt;p>This work characterizes long-haul RDMA behavior, develops modeling and verbs-level emulation support, and studies the implications for geo-distributed federated learning systems.&lt;/p></description></item><item><title>Building form optimization for renewable energy-economic utility of flexible solar cells as building integrated photovoltaics</title><link>https://diogeneschen.github.io/publication/flexible-solar-cells-scs-2025/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://diogeneschen.github.io/publication/flexible-solar-cells-scs-2025/</guid><description>&lt;p>Journal article on optimizing building form for renewable energy-economic utility of flexible solar cells as building integrated photovoltaics.&lt;/p></description></item><item><title>Can Long-Haul RDMA Benefit Federated Learning?</title><link>https://diogeneschen.github.io/publication/long-haul-rdma-fl-sc25/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://diogeneschen.github.io/publication/long-haul-rdma-fl-sc25/</guid><description>&lt;p>This work studies the potential of long-haul RDMA for federated learning workloads and compares RDMA and TCP/IP under geo-distributed settings.&lt;/p></description></item><item><title>FedDES: Discrete Event Based Performance Simulation for Federated Learning Systems</title><link>https://diogeneschen.github.io/publication/feddes-sec25/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://diogeneschen.github.io/publication/feddes-sec25/</guid><description>&lt;p>FedDES models federated learning training, communication, and aggregation as lightweight events, enabling systematic performance analysis of complex FL workflows under diverse networking conditions.&lt;/p></description></item><item><title>Geometry and Material Criteria for Low-Carbon Design of I/H-Beams in Sustainable Steel Structures Considering Both Mechanical Properties and Carbon Emissions</title><link>https://diogeneschen.github.io/publication/ih-beams-materials-2025/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://diogeneschen.github.io/publication/ih-beams-materials-2025/</guid><description>&lt;p>Journal article on geometry and material criteria for low-carbon design of I/H-beams in sustainable steel structures.&lt;/p></description></item><item><title>HPC-R1: Characterizing R1-like Large Reasoning Models on HPC</title><link>https://diogeneschen.github.io/publication/hpc-r1-sc25/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://diogeneschen.github.io/publication/hpc-r1-sc25/</guid><description>&lt;p>HPC-R1 characterizes inference and distillation performance of R1-like reasoning models on HPC platforms, identifying system bottlenecks and scalable deployment strategies.&lt;/p></description></item><item><title>Risk and Energy Based Optimization for Fire Monitoring System in Utility Tunnel Using Cellular Automata</title><link>https://diogeneschen.github.io/publication/fire-monitoring-utility-tunnel-sustainability-2024/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://diogeneschen.github.io/publication/fire-monitoring-utility-tunnel-sustainability-2024/</guid><description>&lt;p>Journal article on risk and energy based optimization for fire monitoring in utility tunnels using cellular automata.&lt;/p></description></item><item><title>Deep Learning Techniques for EEG-Based BCI: Analysis and Applications</title><link>https://diogeneschen.github.io/publication/eeg-bci-cisp-bmei-2023/</link><pubDate>Sun, 01 Oct 2023 00:00:00 +0000</pubDate><guid>https://diogeneschen.github.io/publication/eeg-bci-cisp-bmei-2023/</guid><description>&lt;p>Conference paper on deep learning techniques for EEG-based brain-computer interfaces (BCI).&lt;/p></description></item><item><title>Is There Any Social Principle for LLM-Based Agents?</title><link>https://diogeneschen.github.io/publication/social-principle-llm-agents-arxiv-2023/</link><pubDate>Tue, 01 Aug 2023 00:00:00 +0000</pubDate><guid>https://diogeneschen.github.io/publication/social-principle-llm-agents-arxiv-2023/</guid><description>&lt;p>Preprint exploring potential social principles for LLM-based agents.&lt;/p></description></item><item><title>An algorithm and system design of Deep Learning based edge-cloud scheduling for neuro-electrophysiological signals</title><link>https://diogeneschen.github.io/publication/edge-cloud-scheduling-neuro-2023/</link><pubDate>Thu, 01 Jun 2023 00:00:00 +0000</pubDate><guid>https://diogeneschen.github.io/publication/edge-cloud-scheduling-neuro-2023/</guid><description>&lt;p>Technical report on algorithm and system design of deep learning based edge–cloud scheduling for neuro-electrophysiological signal workloads.&lt;/p></description></item></channel></rss>