Nghia Hoang

Nghia Hoang is a tenure-track Assistant Professor at the School of Electrical Engineering and Computer Science, Washington State University. Prior to his current appointment, he held positions as a Senior Research Scientist at AWS AI Labs (Amazon, 2020-2022), Research Staff Member at the MIT-IBM Watson AI Lab (2018-2020), Postdoctoral Research Associate at MIT's Laboratory for Information and Decision Systems (2017-2018), and Research Fellow at the National University of Singapore (2015-2017).

Dr. Hoang earned his B.Sc. from the University of Science (Vietnam) in 2009 and completed his Ph.D. in Computer Science at the National University of Singapore in 2015 under the supervision of Associate Professor Kian Hsiang Low.

His research focuses on Machine Learning, particularly in Federated Learning, Bayesian Methods, Optimization, and Deep Learning. He has made significant contributions to probabilistic federated learning, offline optimization techniques, and knowledge representation for collaborative learning across heterogeneous systems. His work often bridges theoretical foundations with practical applications in diverse domains including healthcare and multi-agent systems, with recent publications addressing challenges of data heterogeneity and scarcity in distributed learning environments.

Dr. Hoang's publication record reveals strong trends in making federated learning more robust to non-IID data distributions, improving offline optimization through surrogate modeling, and developing techniques for knowledge transfer across heterogeneous systems without requiring orthologue mappings. His work spans both theoretical contributions and practical implementations, with increasing emphasis on real-world applicability of machine learning techniques.

Dr. Hoang actively mentors students in research, with several publications led by his advisees including PhD students Pei-Yau Weng and Long Bui, as well as undergraduate mentee Cuong Dao. He has successfully guided multiple students to publish at top-tier conferences including NeurIPS, ICML, and UAI, demonstrating his commitment to student development and collaborative research.

Professionally, Dr. Hoang serves on the boards of prestigious journals including Machine Learning Journal and Neural Networks as an Action Editor. He has been a program committee member for major AI conferences including AAAI, ICLR, ICML, NeurIPS, and IJCAI across multiple years (2018-2024), and organized workshops such as the Practical Bayesian Methods for Big Data workshop at IBM Research AI Week and the NeurIPS-21 Workshop on New Frontiers in Federated Learning.