Ngo Van Linh

Lecturer, Department Of Computer Science

M.S. (Hanoi University of Science and Technology, 2013)
B.S. (Hanoi University of Science and Technology, 2011)

Email: linhnv@soict.hust.edu.vn
Web:

• Machine Learning
• Data Mining
• Probabilistic Graphical Model
• Recommender System

• Topic models
• Classification and Clustering
• Variational Bayes
• Stochastic Approximation
• Streaming Learning
• Recommender System

Linh Ngo Van is a lecturer at the School of Information and Communication Technology, Hanoi University of Science and Technology (HUST). He received his M.S. degree in computer science in 2013 from HUST. He is interested in: Topic models, stochastic optimization, big data. His works are published in top conferences and journals. Moreover, he successfully applies modern methods to practical problems such as: Recommender systems, sentiment analysis, etc.

• Cuong Ha-Nhat, Dang Tran, Linh Ngo Van, Khoat Than, “Eliminating overfitting of probabilistic topic models on short and noisy text: The role of Dropout”, International Journal of Approximate Reasoning, Springer, 2019.
• Linh The Nguyen, Linh Van Ngo, Khoat Than and Thien Huu Nguyen, “Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings”, In Proceeding of the Association for Computational Linguistics (ACL), 2019.
• Thanh Hai Hoang, Anh Phan Tuan, Linh Ngo Van, Khoat Than, “Enriching user representation in Neural Matrix Factorization”, In Proceedings of RIVF. IEEE, 2019.
• Hoa Le Minh, Son Ta Cong, Quyen Pham The, Linh Ngo Van, Khoat Than, “Collaborative Topic Model for Poisson distributed ratings”, International Journal of Approximate Reasoning, Volume 95, Pages 62-76, Springer, 2018.
• Nguyen Trong Tung, Vu Hoang Dieu, Khoat Than, Ngo Van Linh, “Reducing Class Overlapping in Supervised Dimension Reduction”, In Proceedings of the Ninth International Symposium on Information and Communication Technology (SoICT). ACM, 2018.
• Ngo Van Linh, Nguyen Kim Anh, Khoat Than, Chien Nguyen Dang, “An Effective and Interpretable Method for Document Classification”, Knowledge and Information Systems (KAIS), Volume 50, Issue 3, pp 763–793, 2017.
• Duc-Anh Nguyen, Kim Anh Nguyen, Linh Ngo, Khoat Than, “Keeping priors in streaming Bayesian learning”, Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science, vol 10234. Springer, 2017.
• Khai Mai, Sang Mai, Anh Nguyen, Linh Ngo, Khoat Than, “Enabling Hierarchical Dirichlet Processes to work better for short texts at large scale”, In Proceedings of PAKDD. Lecture Notes in Computer Science, Springer, 2016.
• Ngo Van Linh, Nguyen Kim Anh, Khoat Than, Nguyen Tat Nguyen, “Effective and Interpretable Document Classification using Distinctly Labeled Dirichlet Process Mixture Models of von Mises-Fisher Distributions”, In Proceedings of DASFAA. Lecture Notes in Computer Science, Springer, 2015.

• Grants for research: ONRG (2018-2020); AFRL/AFORS/AOARD (2015-2017), NAFOSTED (2015-2017), HUST (2015, 2017).
  Travel grant: NAFOSTED (2017)

• IT4040: Artificial Intelligence
• IT4866: Machine Learning
• IT3200: C Introduction
• IT3240: C Advance
• IT1110: Introduction to Information Technology