Javad Rafiei Asl

Javad Rafiei Asl

PhD Candidate, Computer Science Department, Georgia State University

About me

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 positions as an NLP (preferably Generative AI) Scientist/Engineer or Machine Learning Researcher/Engineer, I am eager to contribute to the further development of robust and efficient PLMs/LLMs for real-world applications.

As part of my previous practical work, I have undertaken a variety of projects that showcase my expertise in natural language processing and machine learning, specifically in large language models. I participated in an industrial project aimed at detecting various types of plagiarism (e.g., paraphrases and semantic plagiarism) within a corpus of 2.5 million papers. Furthermore, I played an integral role in developing a sophisticated text alignment framework, where I explored fundamental data mining techniques to precisely detect text reuse segments. I fine-tuned a pre-trained language model (T5) for text summarization using the Prefix-Tuning technique on the CNN/Daily Mail dataset. Additionally, I built Retrieval-Augmented Generation (RAG) systems using LlamaIndex, which involved loading data from various sources, creating vector indexes, querying the indexes, and evaluating the system’s performance. I also explored different prompt engineering techniques, such as Few-shot, Zero-shot, and Chain-of-Thought, to build a useful LLM application. In the realm of sentiment analysis, I created a multi-modal sentiment analysis model for movie reviews using RNN/LSTM, deployed it on AWS, managed the codebase with Git, and utilized multi-modal analysis. Furthermore, I extended a conversational AI assistant to handle customer inquiries and provide support for a hypothetical e-commerce company. Lastly, I developed an intelligent document processing system using Azure AI and Machine Learning services to extract relevant information from scanned documents, classify them based on their content, and provide insightful analysis.

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