Hi, I am Yue (/yoo-eh/) Dong, an assistant professor of computer science and engineering at the University of California, Riverside. I obtained my PhD in Computer Science at McGill University and Mila , supervised by Dr. Jackie Cheung .

Apr 1, 2025 · Yue Dong is an assistant professor of computer science and engineering at the University of California Riverside. Her research interests include natural language processing, machine learning, and artificial intelligence.

Yue Dong, Chandra Bhagavatula, Ximing Lu, Jena D. Hwang, Antoine Bosselut, Jackie C.K. Cheung, Yejin Choi Findings of ACL 2021 [12] Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents

Yue Dong, John Wieting, Pat Verga (2022). Faithful to the Document or to the World? Mitigating Hallucinations via Entity-linked Knowledge in Abstractive Summarization .

My PhD thesis was successfully defended on November 28th, 2022. It was a privilege to defend my PhD in front of a phenomenal committee and incredible family, friends, and colleagues.

Yue Dong is an assistant professor of computer science and engineering at the University of California Riverside. Her research interests include natural language processing, machine learning, and artificial intelligence.

Yue Dong is an assistant professor of computer science and engineering at the University of California Riverside. Her research interests include natural language processing, machine learning, and artificial intelligence.

Research Statement – Yue Dong McGill University/Mila, January 2022 Natural language processing (NLP) aims to understand and process human language by machines. Many NLP tasks such as sentiment analysis and reading comprehension have come of age thanks to advances in machine learning. For example, machine …

Apr 1, 2025 · Welcome to UCR NLP Lab! Lab Members Director Yue Dong, assistant professor of computer science and engineering PhD Students Yu Fu, since Fall 2023 2023:EMNLP 2023 Findings

Yue Dong, Nathalie Japkowicz. January, 2018. Cite Paper. Abstract. Anomaly detection in streaming data is an important problem in numerous application domains. Most existing model-based approaches to stream learning are based on decision trees due to their fast construction speed. This paper introduces streaming autoencoder (SA), a fast and ...