Yicun Deng is a research assistant at Teachers College, Columbia University, with expertise in AI technologies and quantitative research methodologies. He holds an Ed.M. in Applied Linguistics from Teachers College, Columbia University, and an M.Sc. in Computer Science from Georgia Institute of Technology.

 Yicun's research focuses on the intersection of language and technology, specifically in utilizing AI to advance language learning and assessment. His work includes developing AI-based educational tools, such as a Language Learning Assistant that integrates models for video and image understanding, automatic speech recognition (ASR), and conversational AI to create personalized learning experiences.

 One of his notable contributions is his conference paper "Rate L2 argumentative essay using GPT4: The effect of human-centric materials," which explores the application of Large Language Models (LLMs) for automated essay scoring and feedback. This research underscores his dedication to leveraging AI for nuanced educational tasks.

 Yicun is proficient in machine learning, deep learning, and natural language processing (NLP), with practical experience in Microsoft Azure Machine Learning and computer vision. He also possesses certifications in Computational Thinking, Algorithms, Neural Networks, deep Learning, and IRB for Social and Behavioral Researchers.

 His role at Teachers College involves data analytics, research, and publication in AI in education. He works with ML/DL algorithms and fine-tunes LLMs to simulate human cognitive processes, applying his NLP and Corpus Linguistic knowledge to develop AI-powered assessment scenarios.