Quan Nguyen

Fields of Interest: 

• Probabilistic machine learning 

• Data-efficient machine learning 

• Sequential decision-making under uncertainty 

• AI for scientific discovery 

Education: Link to current CV: https://krisnguyen135.github.io/files/quan_cv.pdf  

Ph.D.: 2024, Washington University in St. Louis, USA 

Postdoc: 2026, Princeton University, USA 

Profile: Quan is a computer scientist whose research focuses on data-efficient machine learning and sequential decision-making. He develops methods for choosing what data to collect next when labels are costly or limited. He has contributed new frameworks for active learning and Bayesian optimization, with applications in scientific discovery and decision support. His doctoral work received the Turner Dissertation Award for its contributions to learning and decision-making under uncertainty. Quan is also committed to undergraduate and liberal-arts education, and enjoys designing inquiry-driven, project-based learning experiences. 

Select Publications:  

Peer-reviewed publications: 

Liu, Tsung-Wei, and Nguyen, Quan, et al. “Diversity-driven, efficient exploration of a MOF design space to optimize MOF properties.” Chemical Science, 2024. https://pubs.rsc.org/en/content/articlehtml/2024/sc/d4sc03609c  

Nguyen, Quan, and Roman Garnett. “Nonmyopic Multiclass Active Search with Diminishing Returns for Diverse Discovery.” International Conference on Artificial Intelligence and Statistics (AISTATS), 2023. https://proceedings.mlr.press/v206/nguyen23d.html  

Nguyen, Quan, et al. “Local Bayesian Optimization for High-Dimensional Spaces.” Advances in Neural Information Processing Systems (NeurIPS), 2022. https://proceedings.neurips.cc/paper_files/paper/2022/hash/555479a201da27c97aaeed842d16ca49-Abstract-Conference.html  

Nguyen, Quan, et al. “Multifidelity Active Search.” International Conference on Machine Learning (ICML), 2021. https://proceedings.mlr.press/v139/nguyen21f.html  

Technical books: 

Nguyen, Quan. “Grokking Bayes.” Manning Publications, 2026 (in preparation). https://www.manning.com/books/grokking-bayes  

Nguyen, Quan. “Bayesian Optimization in Action.” Manning Publications, 2023. https://www.manning.com/books/bayesian-optimization-in-action  

Research:  

Quan’s research asks how machine learning systems can make better decisions when data collection is expensive. Instead of assuming data are freely available, his work studies how algorithms can actively choose the most valuable observations to acquire. This perspective is especially relevant in scientific and social contexts where experiments, expert labels, or measurements require time, money, or specialized resources.