Paper 'Towards An Efficient LLM Training Paradigm for CTR Prediction' under review.
Paper 'Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap' published in ACM Computing Surveys.
Paper 'Simple Fusion of Collaborative Signals Improves LLM Recommendation' to be presented at TheWebConf (WWW) 2025 - E-commerce Workshop.
Paper 'Federated Conversational Recommender System' published in 46th European Conference on Information Retrieval ECIR 2024.
Paper 'End-to-End Adaptive Local Learning for Alleviating Mainstream Bias in Collaborative Filtering' published in 46th European Conference on Information Retrieval ECIR 2024.
Paper 'Enhancing User Personalization in Conversational Recommenders' published in ACM Web Conference TheWebConf (WWW) 2023.
Paper 'Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems' published in 31st ACM International Conference on Information and Knowledge Management CIKM 2022.
Paper 'Towards Fair Conversational Recommender Systems' published in 16th ACM Conference on Recommender Systems RecSys 2022 - FAccTRec: Workshop on Responsible Recommendation.
Paper 'Howdy Y'all: An Alexa TaskBot' published in Alexa Prize TaskBot Challenge Proceedings 2022.
Education
PhD student at Texas A&M University, Department of Computer Science and Engineering, advised by Prof. James Caverlee; Master's degree in Computer Science from Duke University; Bachelor's degree in Computer Science from Ohio State University.
Background
Research interests: machine learning, natural language processing, and information retrieval, with a special emphasis on AI-powered user-centered systems. Recently, his work primarily focuses on leveraging large language models and foundation models to enhance personalization of ads and recommendations.
Miscellany
NSF/CIKM Student Author Travel Award; National Buckeye Scholarship; Ohio State University Trustees Scholarship.