Within the US scientific system, the NSF CAREER Award is widely regarded as one of the most prestigious recognitions for young scientists. Beyond honoring outstanding research, it reflects confidence in the recipient’s long-term scientific potential. The award includes funding of US$600,000.

“In other words, this is a national investment in minds expected to lead new research directions over the coming decades. At a time when artificial intelligence is reshaping the world, a young Vietnamese-origin scientist receiving this award in AI and machine learning carries special significance. It shows that Vietnamese intellect is fully engaged in the major currents of global science,” Dung said.

A scientist born in 1987 with deep expertise in AI

Born in 1987, Hoang Trong Nghia is a former student of the High School for the Gifted and graduated from the University of Science with a talent-track bachelor’s degree in Information Technology. He previously served as a lecturer at the University of Information Technology under Vietnam National University Ho Chi Minh City.

He earned his PhD in Computer Science from the National University of Singapore, one of Asia’s leading AI research hubs.

After completing his doctoral studies, Nghia continued his research career at major global institutions, including a postdoctoral position at the Massachusetts Institute of Technology, a role as principal researcher at the MIT-IBM Watson AI Lab, and work at Amazon Web Services AI Labs.

Since 2023, he has returned to academia and established an AI research group at Washington State University.

His research focuses on foundational challenges in modern machine learning, particularly in building AI systems capable of understanding uncertainty. One key direction is developing models that can assess the confidence of their predictions - known as uncertainty-aware machine learning.

In many AI systems, especially in healthcare or automated environments, it is not only important to make accurate predictions but also to know when those predictions might be wrong. Such research enhances the reliability of AI in complex settings with imperfect data.

Another major area of his work is federated learning, a method that allows AI models to be trained on distributed data without requiring all data to be centralized. This approach is particularly important for sensitive domains such as medical data, personal information, and connected smart devices.

His studies have introduced new methods enabling machine learning systems to operate effectively even when data is distributed, heterogeneous, and incomplete, while also optimizing complex systems.

Nghia has also contributed significantly to black-box optimization, an important field where scientists must optimize systems too complex to be fully described by mathematical models. These algorithms have applications across areas such as new material design, microchip and electronic system optimization, large-scale AI, biomedical science, prediction of harmful drug interactions, and analysis of complex biological data.

Hoang Trong Nghia is the son of Professor Hoang Van Kiem, former Chairman of the State Council for Professorship in Information Technology and a leading expert in AI applications in Vietnam.

Le Huyen