Khoi Nguyen is a PhD in Computer Science graduating from Oregon State University advised by Professor Sinisa Todorovic. His primary research focuses on generative models, particularly diffusion and autoregressive models for visual content synthesis. He is also actively involved in advancing 2D and 3D understanding, including areas such as reconstruction, detection, and segmentation. Additionally, Khoi is passionate about few-shot, zero-shot learning, open-vocabulary learning, and vision-language models. He has published over many papers in top-tier Computer Vision conferences, including CVPR, ICCV, ECCV, and NeurIPS. His contributions extend beyond research, serving as a reviewer and Area Chair for prestigious conferences and journals like CVPR, ICCV, ECCV, ICLR, NeurIPS, and TPAMI. He earned the Outstanding Reviewer Award at ECCV 2020, and his team received the Best Paper - Honorable Mention Award at WACV 2023. At VinAI, Khoi leads the GenAI research theme, showcasing his leadership in cutting-edge research.
PhD in Computer Science, 2021
Oregon State University
MSc in Computer Science, 2017
Oregon State University
BSc in Computer Science, 2014
Ho Chi Minh City University of Technology (Bach Khoa University)
[05/26] Two papers accepted to CVPR 2026: InverFill and SwiftPie (Findings). Cheers!
[06/25] Two papers accepted to ICCV 2025: CSD-VAR, NASA. Cheers!
[02/25] Three papers accepted to CVPR 2025: Any3DIS, SwiftEdit, SharpDepth. Cheers!
[12/24] Two papers on 3D Scene Completition and Video Virtual Try-on are accepted to AAAI 2025. Congrats to all authors!
[07/24] Two papers (DiverseDream and SwiftBrushv2) accepted to ECCV 2024. Cheers!
Computer Vision is a diverse research domain encompassing various topics. This guide compiles essential and up-to-date tutorials, carefully selected from those presented at top-tier conferences in the field, including CVPR, ICCV, ECCV, and SIGGRAPH.
Context: Welcome to the reading guide for “Probabilistic Machine Learning” by Kevin Murphy. The first version of this book, published in 2012 (probml.github.io/pml-book/book0.html) gained widespread recognition in the Machine Learning community and was often referred to as the “Bible of Machine Learning.” The book’s popularity stemmed from its comprehensive coverage of a vast array of Machine Learning topics, providing readers with a deep understanding of the subject. Recognizing the rapidly evolving landscape of Machine Learning, Kevin Murphy has taken the initiative to update and expand the knowledge presented in the original book. As a result, he decided to release a new version of the book, dividing it into two separate volumes with a total of more than 2000 pages. The first book serves as a core introduction to the subject, while the second book delves into more advanced topics.
This is the PhD guidance from my own experience including:
It is specially written for Vietnamese student, but you can use Google Translate for your convinence.
This is my first post on Machine Learning, Deep Learning and Computer Vision series in Medium. I am currently a Ph.D. Student in Computer Science with research interests are Computer Vision and Machine Learning. On this series, I will share with you the roadmap I have experienced. I hope that everything I share is somehow helps you save time when exploring Machine Learning field.
One of the most powerful and common tools for your research is Google Scholar. It contains many useful features that are necessary for your research. If you can make use of it, you do not need any proprietary software. In this article, I will introduce some useful features namely Google Scholar Search, My Profile, My library, Alerts, and Metrics.