About Me

Hi! I am Weimin Lyu, a final year Ph.D. student in Computer Science at Stony Brook University, advised by Prof. Chao Chen. I am also very fortunate to collaborate with esteemed professors: Haibin Ling, Fusheng Wang, and Tengfei Ma. I am currently an Applied Scientist Intern at Amazon, focusing on foundation model (LLaMA, Mistral) continuously pre-training and fine-tuning with a strong emphasis on numerical and text features.

Research Interests

My research includes multiple directions addressing text-based problems (BERT variants, LLMs), image classification (CNNs, Vision Transformers, CLIP), as well as multimodal image-to-text generation using Vision-Language Models (BLIP-2, MiniGPT-4, LLaVA, InstructBLIP). I also have expertise in explainability for clinical language models using Electronic Health Records.

News

Industry Experience

Amazon, Seattle, USA (May 2024 - Dec 2024)
Applied Scientist Intern, Full Time

- Focused on foundation model training, with a strong emphasis on numerical and text features.
- Developed the entire pre-training and fine-tuning pipeline, supporting both small-scale and large-scale model training.
- Developed strategies to address multi-task real-world Amazon's user case.
- Delivered a developed model for business review, aimed at production launch.

Selected Publications

Full publications can be found in Google Scholar.

Preprint

Backdooring Vision-Language Models with Out-Of-Distribution Data
Weimin Lyu, Jiachen Yao, Saumya Gupta, Lu Pang, Tao Sun, Lingjie Yi, Lijie Hu, Haibin Ling, Chao Chen
Tech Report

Conference/Workshop/Journal

PivotAlign: Improve Semi-Supervised Learning by Learning Intra-Class Heterogeneity and Aligning with Pivots
Lingjie Yi, Tao Sun, Yikai Zhang, Songzhu Zheng, Weimin Lyu, Haibin Ling, Chao Chen
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2025)

TrojVLM: Backdoor Attack Against Vision Language Models
Weimin Lyu, Lu Pang, Tengfei Ma, Haibin Ling, Chao Chen
The 18th European Conference on Computer Vision (ECCV 2024)
[ECCV]

BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records
Weimin Lyu, Zexin Bi, Fusheng Wang, Chao Chen
American Medical Informatics Association Annual Symposium (AMIA 2024)
[arXiv]

Task-Agnostic Detector for Insertion-Based Backdoor Attacks
Weimin Lyu, Xiao Lin, Songzhu Zheng, Lu Pang, Haibin Ling, Susmit Jha, Chao Chen
The Findings of 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2024)
[NAACL]

Attention-Enhancing Backdoor Attacks Against BERT-based Models
Weimin Lyu, Songzhu Zheng, Lu Pang, Haibin Ling, Chao Chen
The Findings of 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023) (A short version is accepted as Oral at ICLR 2023 Workshop on BANDS)
[EMNLP][Code]

An Integrated LSTM-HeteroRGNN Model for Interpretable Opioid Overdose Risk Prediction
Xinyu Dong, Rachel Wong, Weimin Lyu, Kayley Abell-Hart, Janos G Hajagos, Richard N Rosenthal, Chao Chen, Fusheng Wang
Artificial Intelligence in Medicine (AIIM 2022)
[AIIM]

A Multimodal Transformer: Fusing Clinical Notes With Structured EHR Data for Interpretable In-Hospital Mortality Prediction
Weimin Lyu, Xinyu Dong, Rachel Wong, Songzhu Zheng , Kayley Abell-Hart, Fusheng Wang, Chao Chen
American Medical Informatics Association Annual Symposium (AMIA 2022) (Student Paper Finalist-Equal to Best Student Paper Nomination)
[AMIA][Code]

A Study of the Attention Abnormality in Trojaned BERTs
Weimin Lyu, Songzhu Zheng, Tengfei Ma, Chao Chen
The 2022 Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL 2022)
[NAACL][Code]