Welcome to Dai Hai Nguyen's Homepage
Dai Hai Nguyen (Nguyễn Đại Hải)
PhD. Student
Bio-Knowledge Engineering Research Laboratory
Bioinformatics Center
Institute for Chemical Research
Kyoto University
Research Interests
My current research topics include: sparse learning models, convex optimization, kernel learning, optimal transport, etc.
Education
- Oct., 2017-present, PhD. student @ Kyoto University, Japan.
- Sep., 2008-Aug., 2013, Undergraduate @ Hanoi University of Science and Technology, Vietnam.
Honors and Awards
- JSPS Research Fellowship for Young Scientists (DC2), Japan Society for The Promotion of Science, 2019.
- ICR Award for Graduate Students, Institute for Chemical Research, Kyoto University, 2019.
- Best Presentation Award (Runner Up), for the presentation at the BIC presentation day, Kyoto University, 2019.
Experience
- Oct., 2017-present: Research Assistant, Kyoto University, Kyoto, Japan.
- Jul., 2018: Visiting Researcher, Massachusetts Institute of Technology, USA.
- Jul., 2018: Tutorial Speaker, IBSB, Boston University, USA.
Selected Publications
Journals
5. Dai Hai Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka, "ADAPTIVE: leArning DAta-dePendenT, concIse molecular VEctors for fast, accurate metabolite identification from tandem mass spectra", Bioinformatics, (Proceedings of the 27th International Conference on Intelligent Systems for Molecular Biology (ISMB/ECCB 2019), Basel, Switzerland), Pages i164–i172 .[Link]
4. Anh Duc Le, Dai Hai Nguyen, Bipin Indurkhya, Masaki Nakagawa, "Stroke order normalization for improving recognition of online handwritten mathematical expressions", International Journal on Document Analysis and Recognition (IJDAR), Volume 22, Issue 1, pp 29–39, 2019. [Link]
3. Dai Hai Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka, "Recent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches", Briefings in Bioinformatics, bby066, 2018. [Link]
2. Dai Hai Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka, "SIMPLE: Sparse Interaction Model over Peaks of MoLEcules for Fast, Interpretable Metabolite Identification from Tandem Mass Spectra". Bioinformatics, 34 (13) (Proceedings of the 26th International Conference on Intelligent Systems for Molecular Biology (ISMB 2018), Chicago, USA), Pages i323–i332. [Link]
1. Dai Hai Nguyen, Le Duc Anh, Masaki Nakagawa, "Recognition of online handwritten math symbols using deep neural networks", IEICE Transactions on Information and Systems 99 (12), 3110-3118, 2016.
Conferences
to be updated!
Contact
Emails: hai[at]kuicr.kyoto-u.ac.jp or haidnguyen0909[at]gmail.com
Address: Gokasho, Uji, Kyoto, 611-0011 Japan