Than Quang Khoat

Associate Professor
Department Of Computer Science

Ph.D. (Knowledge Science, Japan Advanced Institute of Science and Technology, 2013)
M.S. (Computer Science, Hanoi University of Science and Technology, 2009)
B.S. (Applied Mathematics & Informatics, Vietnam National University, 2004)

Email: khoattq@soict.hust.edu.vn
Web: https://users.soict.hust.edu.vn/khoattq/

• Artificial Intelligence
• Data Science
• Computer Science

• Học máy (Machine Learning)
• Mô hình tạo sinh (Deep Generative Models)
• Lý thuyết học sâu (Deep Learning Theory)
• Học liên tục (Continual Learning)
• Lý thuyết học (Learning Theory)

Khoat Than is currently Director of Data Science Laboratory, and Assistant Professor at Department of Information Systems, SOICT, HUST. He received Ph.D. (2013) from Japan Advanced Institute of Science and Technology. He often joins the Program Committees of various leading conferences in the areas of artificial intelligence and data science, including ICML, NIPS, IJCAI, ICLR, PAKDD, ACML. His research has been being supported from various funding sources including ONRG (US), AFRL (US), ARL (US), NAFOSTED (VN), MOET (VN).

• Viet Nguyen, Giang Vu, Tung Nguyen Thanh, Toan Tran, Khoat Than. “On inference stability for diffusion models”. In Proceedings of the AAAI Conference on Artificial Intelligence, 2024. [Oral, Top 2% of all submissions]
• Nam Le Hai, Trang Nguyen, Linh Ngo Van, Thien Huu Nguyen, Khoat Than. “Continual variational dropout: a view of auxiliary local variables in continual learning”. Machine Learning, Volume 113, pages 281-323, 2024.
• Bach Tran, Anh Nguyen-Duc, Linh Ngo, Khoat Than. “Dynamic transformation of prior knowledge into Bayesian models for data streams”. IEEE Transactions on Knowledge and Data Engineering, 2023.
• Khang Nguyen, Kien Duc Do, Truong Tuan Vu, Khoat Than. “Unsupervised Image Segmentation with Robust Virtual Class Contrast.” Pattern Recognition Letters, Volume 173, Pages 10-16, 2023.
• Ha Nguyen, Hoang Pham, Son Nguyen, Linh Ngo Van, Khoat Than. “Adaptive Infinite Dropout for Noisy and Sparse Data Streams”. Machine Learning, 2022.
• Tung Nguyen, Trung Mai, Nam Nguyen, Linh Ngo Van, Khoat Than. “Balancing stability and plasticity when learning topic models from short and noisy text streams”. Neurocomputing, Volume 505, Pages 30-43, 2022.
• Quyen Tran, Lam Tran, Linh Chu Hai, Linh Ngo, Khoat Than. “From Implicit to Explicit feedback: A deep neural network for modeling sequential behaviors and long-short term preferences of online users”. Neurocomputing, Vol 479, pp 89-105, 2022.
• Linh Ngo Van, Bach Tran, Khoat Than. “A graph convolutional topic model for short and noisy text streams.” Neurocomputing 468 (2022): 345-359.
• Son Nguyen, Duong Nguyen, Khai Nguyen, Nhat Ho, Khoat Than, Hung Bui. “Structured Dropout Variational Inference for Bayesian Neural Networks”. In Advances in Neural Information Processing Systems (NeurIPS), 2021.

• Student Best Paper: IEEE International Conference on Research, Innovation & Vision for the Future, 2008
• Best Student Paper: The 10th IEEE RIVF International Conference on Computing and Communication Technologies, Hanoi, 2013.
• Monbukagakusho Scholarship: Japanese Government MEXT Scholarship, 10/2009-9/2013
• Travel grants: NEC C & C Foundation (2010), Japan Association for Mathematical Sciences (2012), NAFOSTED (2016); ARL (2019)

• Artificial Intelligence
• Machine Learning
• Introduction to Data Science
• Machine Learning for Big Data

• Learning an effective representation for the hidden semantics (Role: Dirrector / Principle Investigator)
• Multi-tasking Evolutionary Algorithms for Optimizing Artificial Neural Network and Graph-based Models (Role: Investigator)
• Develop new transition-metal-rare-earth materials for permanent magnets by machine learning Models (Role: Investigator)