Hailong Jiang - Assistant Professor

👋 Hello, World!

I am Hailong Jiang, a Computer Science Ph.D. with expertise in HPC systems, compiler technologies, and the intersection of program analysis and large language models (LLMs). I have a proven track record in building innovative tools, publishing impactful research, and leading collaborative projects. I am currently a Tenure Track Assistant Professor in Computer Science at Youngstown State University (YSU).

I am honored to have presented my PhD research at SC’24 - The International Conference for High Performance Computing, Networking, Storage, and Analysis in Atlanta, GA. My doctoral showcase highlighted three pivotal works that advance HPC resilience analysis using LLMs:

• HAPPA Platform: Achieved MSE of 0.078 in SDC prediction, outperforming existing models by 30%
• Loop Resilience Analysis: Quantified SDC rates for the 13 dwarfs of parallelism using semantic analysis
• IR Code Analysis: Evaluated LLM capabilities in low-level code understanding and program analysis

View SC’24 Proceedings →

🔬 Latest Research

🎉 MAJOR ACHIEVEMENT: ICML 2025 ACCEPTED!

My latest research investigates the capabilities of Large Language Models in understanding Intermediate Representations (IRs) for compiler design and program analysis. This pioneering empirical study evaluates GPT-4, GPT-3, Gemma 2, LLaMA 3.1, and Code Llama across four critical tasks:

• Control Flow Graph (CFG) reconstruction
• IR decompilation
• Code summarization
• Execution reasoning

This work has been accepted to ICML 2025 - one of the most prestigious conferences in machine learning, representing a significant breakthrough in my research career at the intersection of AI and compiler technologies.

Read arXiv Paper → | ICML 2025 Conference →

🏥 Emerging Research Interest

I am increasingly interested in applying LLMs to medical data analysis, especially in building interpretable and reliable AI for healthcare. I’m actively exploring opportunities to contribute to interdisciplinary research at the intersection of AI and medicine, including:

• Medical Data Analysis: Pattern recognition and insights extraction
• Interpretable AI: Building transparent and trustworthy healthcare AI systems
• Reliable Healthcare AI: Ensuring robustness and safety in medical applications
• Interdisciplinary Collaboration: Bridging computer science and medical research

🎯 Research Interests

My research interests lie at the intersection of:

• High-Performance Computing: Resilience analysis and fault tolerance in HPC systems
• Large Language Models: Advanced code intelligence and program analysis using LLMs
• Compiler Technologies: LLVM/Clang, CUDA/OpenMP, parallelization languages, IR analysis
• Artificial Intelligence: Deep learning, neural networks, and machine learning applications
• System Programming: C++/C, Python, Shell scripting, and system-level development
• Healthcare AI: Medical data analysis, interpretable AI, and reliable healthcare systems

🎓 Education & Background

• Ph.D. in Computer Science - Kent State University (2018-2025)
  • Thesis: “Research on resilience in high-performance Computing (HPC) applications with Large Language Models”
  • Supervised by Prof. Qiang Guan
  • Presented papers at 3 IEEE conferences with contributions published in 2 Springer journals
  • Featured at SC’24 Doctoral Showcase - Premier HPC conference
  • Published in SRDS’24 - International Symposium on Reliable Distributed Systems
• M.Eng. in IC Engineering - University of Chinese Academy of Sciences, Beijing, China (2014-2017)
  • Thesis: “The Study of Cu2ZnSnS4 films generation by sulfur-free annealing process and device application”
• B.Sc. in Electronic Science and Technology - Xidian University, Xi’an, China (2010-2014)
  • Thesis: “A novel infrared object tracking algorithm”

💼 Current Work

I am currently working as a Tenure Track Assistant Professor at Youngstown State University, where I lead projects in:

• Program resilience analysis on High-Performance Computing (HPC) systems
• HAPPA platform development - LLM-integrated resilience analysis (Published in SRDS’24)
• LLM IR understanding research - Investigating AI capabilities in compiler-level tasks
• Healthcare AI research - Exploring AI applications in medical data analysis
• Soft error simulation platform development on LLVM
• Resilience analysis platform with transformer/LLM integration
• Visualization framework for error propagation study using control-flow graphs

🔧 Professional Experience

Research Assistant | Kent State University | 2018-Present

• Built a soft error simulation platform on LLVM
• Developed HAPPA platform with transformer/LLM integration for resilience prediction
• Published HAPPA research in SRDS’24 - International Symposium on Reliable Distributed Systems
• Conducted pioneering research on LLM understanding of Intermediate Representations
• Improved resilience prediction accuracy by 30%
• Built visualization framework for error propagation study
• Presented at SC’24 Doctoral Showcase - Premier HPC conference

Research Aide Technical | Argonne National Laboratory | Feb 2023-Aug 2023

• Research on translation between parallelization languages
• Completed metadata process in “EXCELLENT” project
• Engineering in combination of Noelle and SPLENDID
• Setup experiment environment for OpenMP/CUDA decompilation

Summer Internship | Los Alamos National Laboratory | Feb 2023-Aug 2023

• Developed FI-VIS tool based on Pin and Pinfi
• Trace and visualize error propagation in program execution at instruction level

🛠️ Technical Skills

• Programming Languages: C++/C, Python, Shell/Cmake
• Tools & Technologies: LLVM/Clang, CUDA/OpenMP, PyTorch/TensorFlow, GNN, LLM/BERT, LSTM, KeyBERT
• Research Tools: Git, LaTeX, VS Studio
• Cloud Platforms: High-performance computing environments

My research has been published in leading IEEE conferences and Springer journals, focusing on HPC resilience and code intelligence. I actively contribute to the academic community through peer-reviewed publications and conference presentations, including the prestigious SC’24 Doctoral Showcase and SRDS’24 conference publication. My latest work on LLM understanding of IRs has been published on arXiv, and I’m actively exploring AI applications in healthcare.

🎓 Teaching & Mentoring

I am passionate about education and have experience in:

• Graduate-level research supervision
• Technical tool development and documentation
• Collaborative research project leadership
• Current Role: Tenure Track Assistant Professor at Youngstown State University

📞 Get in Touch

I’m always interested in collaborating on interesting projects and discussing research opportunities. Feel free to reach out if you’d like to connect or learn more about my work.

• Email: hjiang@ysu.edu
• Alternative Email: jianghl106@gmail.com
• Phone: (937) 789-3969
• Address: Youngstown, OH, USA
• GitHub: hjiang13
• LinkedIn: Hailong Jiang
• Google Scholar: Hailong Jiang
• ORCID: 0000-0003-3246-7177

🤝 Seeking Students & Collaborators

I am actively seeking motivated students and research collaborators in the following areas:

Graduate Students (M.S./Ph.D.)

• HPC Systems & Resilience Analysis: Students interested in high-performance computing, fault tolerance, and system reliability
• LLM for Code Intelligence: Students passionate about using large language models for program analysis and compiler optimization
• Healthcare AI: Students interested in applying AI/ML to medical data analysis and building interpretable healthcare systems

Research Collaborators

• Academic Partners: Faculty and researchers working on HPC, compiler technologies, or healthcare AI
• Industry Partners: Companies developing HPC solutions, compiler tools, or healthcare applications
• Interdisciplinary Research: Collaborators from medicine, biology, or other fields interested in AI applications

What I Offer

• Access to state-of-the-art HPC infrastructure and LLM resources
• Experience in publishing at top-tier conferences (SC, SRDS, ICML)
• Strong industry connections through national lab collaborations
• Support for conference travel and research funding

If you’re interested in joining my research group or collaborating on exciting projects, please reach out via email or schedule a meeting to discuss potential opportunities.