• Research Scientist at INformation Security and Privacy: Interdisciplinary Research and Education (INSPIRE) center
• Ph.D. candidate in Computer Science at Georgia State University, Department of Computer Science
• Advised by Prof. Daniel Takabi, and Prof. Zhipeng Cai

Research Interest: Natural Language Processing (NLP), Large Language Models (LLMs), and Trustworthy Artificial Intelligence

I am a last-year PhD candidate specializing in Natural Language Processing (NLP), Trustworthy Artificial Intelligence (AI), and Machine Learning (ML/LLMs), with a strong background in various programming languages. Highly motivated, I have contributed to several research projects and publications over the years, showcasing my expertise and dedication to the field. With 8 years of expertise in NLP models and tasks, over 4 years of experience in secure AI, and robust PLMs & LLMs, I am a Robust Machine Learning Researcher proficient in programming languages and algorithm design, with over 10 years of experience. I am currently conducting research aimed at enhancing the robustness and efficiency of Pre-trained Language Models (PLMs) for adversarial NLP applications, and I am scheduled to defend my PhD dissertation in the next few weeks. Actively seeking full-time or part-time positions as an NLP/Generative AI Scientist or Machine Learning Researcher, I am eager to contribute to the further development of robust and efficient PLMs/LLMs for real-world applications.
Previously, I participated in an industrial project aimed at detecting various types of plagiarism in scientific papers. I developed an effective method to retrieve source documents containing plagiarism from a substantial corpus of 2.5 million. Furthermore, I played an integral role in the development of a sophisticated text alignment framework, where I explored fundamental data mining techniques to precisely detect text reuse segments.

News

• [May 2024] Our paper titled RobustSentEmbed: Robust Sentence Embeddings Using Adversarial Self-Supervised Contrastive Learning has been accepted by North American Chapter of the Association for Computational Linguistics (NAACL 2024). The preprint version is accessible via this link.
• [February 2024] Our paper titled A Semantic, Syntactic, And Context-Aware Natural Language Adversarial Example Generator is published on the IEEE Transactions on Dependable and Secure Computing.
• [December 2023] Our paper on robust text representation is accepted by Empirical Methods in Natural Language Processing (EMNLP 2023). The preprint version is accessible via this link.
• [October 2023] The source code for our robust text representation is available in the RobustEmbed repository.