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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

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publications

Chaser: An Enhanced Fault Injection tool for tracing Soft Errors in MPI Applications

Published in 2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2020

An enhanced fault injection tool designed for tracing and analyzing soft errors in MPI applications, providing comprehensive error analysis capabilities.

Recommended citation: Guan, Q., Hu, X., Grove, T., Fang, B., Jiang, H., Yin, H., & DeBardeleben, N. (2020). Chaser: An Enhanced Fault Injection tool for tracing Soft Errors in MPI Applications. 2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN).
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ResilienceVis: A Control-Flow Graph-based Visualization Framework for Resilience Analysis

Published in 2021 Workshop on Silicon Errors in Logic - System Effects (SELSE), 2021

A control-flow graph-based visualization framework for resilience analysis, providing comprehensive insights into program resilience characteristics and error propagation patterns.

Recommended citation: Jiang, H., Guan, Q., Fang, B., Ruan, S., Krishnamoorthy, S., & DeBardeleben, N. (2021). ResilienceVis: A Control-Flow Graph-based Visualization Framework for Resilience Analysis. 2021 Workshop on Silicon Errors in Logic - System Effects (SELSE).
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BatchLens: A Visualization Approach for Analyzing Batch Jobs in Cloud Computing

Published in 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2022

A visualization approach for analyzing and understanding batch jobs in cloud computing environments, providing insights into job patterns and performance.

Recommended citation: Ruan, S., Wang, Y., Jiang, H., Xu, W., & Guan, Q. (2022). BatchLens: A Visualization Approach for Analyzing Batch Jobs in Cloud Computing. 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), 108-111.
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VISILIENCE: An Interactive Visualization Framework for Resilience Analysis using Control-Flow Graph

Published in 2023 IEEE 28th Pacific Rim International Symposium on Dependable Computing (PRDC), 2023

An interactive visualization framework for resilience analysis that utilizes control-flow graphs to analyze and visualize program resilience characteristics.

Recommended citation: Jiang, H., Ruan, S., Fang, B., & Guan, Q. (2023). VISILIENCE: An Interactive Visualization Framework for Resilience Analysis using Control-Flow Graph. 2023 IEEE 28th Pacific Rim International Symposium on Dependable Computing (PRDC).
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Investigating Resilience of Loops in HPC Programs: A Semantic Approach with LLMs

Published in 2024 IEEE High Performance Extreme Computing Conference (HPEC), 2024

A semantic approach using Large Language Models to investigate and analyze the resilience of loops in High-Performance Computing programs.

Recommended citation: Jiang, H., Zhu, J., Fang, B., & Guan, Q. (2024). Investigating Resilience of Loops in HPC Programs: A Semantic Approach with LLMs. 2024 IEEE High Performance Extreme Computing Conference (HPEC).
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Exploring the Digital Landscape of Antidepressant Use: Demographic Insights, Emotional Analysis, and Topic Modeling

Published in JMIR Preprints, 2024

A comprehensive analysis of antidepressant use patterns using digital data, including demographic insights, emotional analysis, and topic modeling to understand usage trends and patterns.

Recommended citation: Zhu, J., Zhang, X., Jin, R., Jiang, H., & Kenne, D. R. (2024). Exploring the Digital Landscape of Antidepressant Use: Demographic Insights, Emotional Analysis, and Topic Modeling. JMIR Preprints, 62680.
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HAppA: A Modular Platform for HPC Application Resilience Analysis with LLMs Embedded

Published in 2024 43rd International Symposium on Reliable Distributed Systems (SRDS), 2024

A modular platform for HPC Application Resilience Analysis that embeds Large Language Models to understand long code sequences and achieves superior predictive accuracy in resilience analysis.

Recommended citation: Jiang, H., Zhu, J., Fang, B., & Guan, Q. (2024). HAppA: A Modular Platform for HPC Application Resilience Analysis with LLMs Embedded. In 2024 43rd International Symposium on Reliable Distributed Systems (SRDS).
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Can Large Language Models Understand IRs?

Published in 2025 International Conference on Machine Learning (ICML'25), 2025

A comprehensive study investigating the capabilities of Large Language Models in understanding Intermediate Representations (IRs) for compiler design and program analysis.

Recommended citation: Jiang, H., Zhu, J., Jin, R., & Guan, Q. (2025). Can Large Language Models Understand IRs? Proceedings of ICML 2025.
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Can Large Language Models Understand Intermediate Representations?

Published in arXiv preprint, 2025

A comprehensive study investigating the capabilities of Large Language Models in understanding Intermediate Representations (IRs) for compiler design and program analysis.

Recommended citation: Jiang, H. (2025). Can Large Language Models Understand Intermediate Representations? arXiv preprint arXiv:2502.06854.
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talks

Doctoral Showcase in SC’24

Published:

Presentation of my PhD research on HPC resilience analysis using Large Language Models at the International Conference for High Performance Computing, Networking, Storage, and Analysis.

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teaching

Data Structures and Algorithms

Undergraduate Course, Computer Science Department, 1900

This course covers fundamental data structures and algorithms essential for computer science students. The curriculum includes:

Introduction to Machine Learning

Graduate Course, Computer Science Department, 1900

This course provides a comprehensive introduction to machine learning algorithms and their applications. Topics covered include: