Nam Nguyen
Ph.D. Student in Information Theory and Machine Learning at Department of Electrical Engineering and Computer Science, Oregon State University
United States
About me
I received the B.Eng. degree (Hons.) in Electronics and Communications Engineering from Posts and Telecommunications Institute of Technology, Hanoi, Vietnam, in 2021; and the M.Sc. degree (Hons.) in wireless communications from Oregon State University, United States, in 2024. I am currently pursuing a Ph.D. in Electrical and Computer Engineering, with a minor in Artificial Intelligence, at Department of Electrical Engineering and Computer Science, Oregon State University, advised by Prof. Thinh Nguyen and Prof. Bella Bose. My research focuses on neural data compression (image/video coding), machine learning, information theory, rate-distortion-classification representation for lossy compression, and high-perceptual image denoising via diffusion-based generative compression.
For more details, please refer to Publications and CV. If you are recruiting and are interested in my profile, feel free to send me an email at nguynam4@oregonstate.edu. Resume [PDF]
Contact
- Nam Nguyen
Department of Electrical Engineering and Computer Science
Oregon State University
3130 Kelley Engineering Center
Corvallis, Oregon, United States - Emails: nguynam4 [at] oregonstate [dot] edu (working)
nguyendinhnam.working [at] gmail [dot] com (personal)
News
- 2026
- 01/26: Our paper, “Cross-Domain Lossy Compression via Rate- and Classification-Constrained Optimal Transport”, has been accepted to The Fourteenth International Conference on Learning Representations 2026. This work represents my first publication in the field of Machine Learning.
- 01/21: Excited to share that I’ve been selected as a member of the 2026 iREDEFINE Fellows cohort by the Electrical and Computer Engineering Department Heads Association (ECEDHA). I will attend the ECEDHA Annual Conference & ECExpo in New Mexico this March.
- 01/16: A new paper titled “Rate-Distortion-Classification Representation Theory for Bernoulli Sources” has been released on arXiv and submitted to the 2026 IEEE International Symposium on Information Theory (ISIT).
- 2025
- 09/27: Excited to share that I have been selected as a recipient of the 2025 IEEE Signal Processing Society Scholarship.
- 06/27: Our paper, “Universal Rate-Distortion-Classification Representations for Lossy Compression”, has been accepted to the 2025 IEEE Information Theory Workshop (ITW). This marks my first publication in the field of Information Theory.
- 06/24: I was offered a machine learning research internship this summer at Deakin University’s Applied Artificial Intelligence Initiative.
- 06/11: I will be participating in the 2025 North American School of Information Theory (NASIT), taking place at the University of Minnesota Twin Cities from June 16–20, 2025. [Poster]
- 04/14: A new paper titled “A Theory of Universal Rate-Distortion-Classification Representations for Lossy Compression” has been released on arXiv.
- 04/12: Submitted a paper to the 2025 IEEE Information Theory Workshop (ITW).
- 04/11: Awarded an NSF student travel grant to attend the AERPAW Spring 2025 Workshop, held at North Carolina State University from May 27–30, 2025.
- 04/04: A new paper titled “On Symbol Error Probability-based Beamforming in MIMO Gaussian Wiretap Channels” has been released on arXiv.
- 2024
- 12/05: Successfully passed my qualifying exam and defended my Master’s thesis titled “On Minimizing Symbol Error Probability Using Beamforming in MIMO Gaussian Wiretap Channels”.
- 10/07: Our conference paper “On Minimizing Symbol Error Probability for Antipodal Beamforming in MIMO Gaussian Wiretap Channels” was published in the 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall). Read it on IEEE Xplore.