Dang Nguyen

CS Ph.D. Student at UCLA


Dang Nguyen is a first-year CS Ph.D. student at the University of California, Los Angeles, where he is advised by Professor Baharan Mirzasoleiman. His research interest lies in developing efficient and scalable machine-learning algorithms for large-scale datasets and architectures, with a specific focus on enhancing model robustness by addressing challenges such as distribution shift, label noise, data poisoning, and spurious correlations. In addition, he is also interested in exploring Generative AI, Multimodal Learning, and Large Language Modeling (LLM), with a particular focus on enhancing math reasoning and prioritizing data efficiency perspectives.

Before joining UCLA, he was an AI Resident at VinAI, a leading AI research-based company in Vietnam. Prior to that, he received his BS degree, summa cum laude, in Information Networking for Innovation and Design from Toyo University. Going further back in time, he was a graduate of High School for Gifted Students (Hanoi University of Science) and a Maths Olympian (silver medal, IMO 2015).


Jan 17, 2024 One paper is accepted at ICLR 2024.
Sep 25, 2023 I start my Ph.D. in Computer Science at UCLA.
Jun 4, 2023 Our paper “On Cross-Layer Alignment for Model Fusion of Heterogeneous Neural Networks” is recognized as one of the top 3% of all papers accepted at ICASSP 2023.
Apr 25, 2023 One paper is accepted at ICML 2023.
Feb 17, 2023 One paper is accepted at ICASSP 2023.