Hi there 👋
I’m a Computer Science Master’s student at the University of Pennsylvania, working on Large Language Models (LLMs), Vision-Language Models (VLMs), and NLP applications. I’m graduating in Fall 2026 and currently applying for Ph.D. programs.
I’m fortunate to be advised by Prof. Chris Callison-Burch, Prof. Lyle Ungar, and Delip Rao at the University of Pennsylvania. I also collaborate with Xiaodong Yu from AMD and Jianheng Tang and Prof. Yunhuai Liu at Peking University.
I’m honored to receive the Xiaomi Special Scholarship (Top 10 university-wide), the National Scholarship for Outstanding Students (Top 5), and to have been selected as an Outstanding Graduate of the Class of 2024.
My research is dedicated to advancing Large Language Models and Multimodal LLMs through Effective, Efficient, and Explainable approaches. I’m particularly focused on:
- Unlocking LLMs’ Internal Mechanisms: Developing training-free optimization methods by understanding and enhancing attention patterns, representations, logits, and prompting mechanisms
- Pushing LLM Application Boundaries: Developing innovative applications and benchmarking in security, code understanding, and scientific research automation
- Advancing Model Evolution: Building novel approaches for data synthesis and training optimization
Previously, I worked on reinforcement learning in crowdsensing systems and contributed to HCI research, which shaped my perspective on building practical AI solutions.
📫 Open for Research Collaboration: feijianghan@gmail.com
🔗 Connect & Follow
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📊 Google Scholar Research publications -
💼 LinkedIn Professional network -
🌐 Personal Website Coming soon -
📱 Xiaohongshu (Red Note) Tech sharing in Chinese
🎉 Recent News
- July 2025: Paper accepted to COLM 2025 - “LLMs for WebShell Detection”
- June 2025: Paper accepted to MOSS@ICML2025 - “ZeroTuning: Enhancing LLMs Without Training”
📚 Selected Research Papers
For a complete list of publications, please visit my Google Scholar
🔍 NLP & (M)LLM Applications
COLM 2025Can LLMs handle WebShell detection? Overcoming Detection Challenges with Behavioral Function-Aware Framework Feijiang Han, Jiaming Zhang, Chuyi Deng, Jianheng Tang, Yunhuai LiuUnder ReviewLaTeX2Layout: High-Fidelity, Scalable Document Layout Annotation Pipeline for Layout Detection Feijiang Han, Zelong Wang, Bowen Wang, Xinxin Liu, Skyler Cheung, Delip Rao, Chris Callison-Burch, Lyle UngarUnder ReviewBeyond Detection: A Comprehensive Benchmark and Study on Representation Learning for Fine-Grained Webshell Family Classification Feijiang Han
🔮 Unlocking and Understanding LLMs
MOSS@ICML2025ZeroTuning: Unlocking the Initial Token’s Power to Enhance Large Language Models Without Training Feijiang Han, Xiaodong Yu, Jianheng Tang, Delip Rao, Lyle Ungar
🌟 Foundation Research (RL, Unlearning, Crowdsourcing)
Information Sciences 2023Credit and quality intelligent learning based multi-armed bandit scheme for unknown worker selection in multimedia MCS Jianheng Tang, Feijiang Han, Kejia Fan, et al.UBICOMP 2025CALM: A Ubiquitous Crowdsourced Analytic Learning Mechanism for Continual Service Construction with Data Privacy Preservation Kejia Fan, Yuwei Huang, Jiayi He, Feijiang Han, et al.Information Sciences 2024MAB-RP: A Multi-Armed Bandit based workers selection scheme for accurate data collection in crowdsensing Yuwei Lou, Jianheng Tang, Feijiang Han, Anfeng Liu, et al.IEEE IoT Journal 2023BTV-CMAB: A bi-directional trust verification-based combinatorial multiarmed bandit scheme for mobile crowdsourcing Jianheng Tang, Kejia Fan, Wenbin Xie, Feijiang Han, et al.