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Machine learning for science
Machine learning that shapes science.
Areas:
- Foundation Models of science: Building the associative memory and inference engine for science from data.
- Science-informed machine learning: Exploring the interplay between science and machine learning.
- Materials science: Exploring faster ways to compute materials properties and generate new kinds of materials.
- Computational chemistry: Exploring the molecular space and interactions.
- Computational
biology:We aim to unlock the mystery of life
hidden under our genome, cells and organisms.
- Drug discovery: We aim to accelerate the finding of new drugs for a new target.
Talks/Tutorials
-
Generative AI to accelerate discovery of materials, Keynote @PRICM11, Nov 2023.
- AI for automated materials discovery via learning to represent, predict, generate and explain, @Thuyloi University, May 2023.
- Machine learning and reasoning for drug discovery Tutorial @ECML-PKDD, Sept 2021.
- Climate
change: Challenges and AI-driven solutions, @Swinburne Vietnam, Hanoi,
Vietnam, Dec 2019.
- Modern
AI for drug discovery, VietAI
Summit, Nov 2019.
- Lecture on Deep
learning for biomedicine, Southeast
Asia Machine Learning (SEA ML) School, Depok, Greater Jakarta,
Indonesia, July 2019.
- Deep
learning for genomics: Present and future, Genomic Medicine 2019, Hanoi,
Vietnam, June 2019.
- AI for matters, Phenikaa University, Hanoi, Vietnam, Jan 2019.
- Deep
learning for biomedicine: Genomics and Drug design, Institute of Big Data, Hanoi,
Vietnam, Jan 2019.
- Deep
learning for biomedical discovery and data mining, Tutorial at PAKDD'18, Melbourne, Australia.
Preprints
- Enabling discovery of
materials through enhanced generalisability of deep learning models, Tawfik, Sherif Abdulkader, Tri Minh Nguyen, Salvy P. Russo, Truyen
Tran, Sunil Gupta, and Svetha Venkatesh.
arXiv preprint arXiv:2402.10931.
- Relational dynamic
memory networks, Trang Pham, Truyen
Tran, Svetha Venkatesh, arXiv
preprint arXiv:1808.04247
- Hybrid generative-discriminative models for inverse materials design, Phuoc Nguyen, Truyen Tran, Sunil Gupta, Svetha Venkatesh. arXiv preprint arXiv:1811.06060.
Publications
Materials science:
- Towards understanding structure–property relations in materials with interpretable deep learning, Tien-Sinh Vu, Minh-Quyet Ha, Duong Nguyen Nguyen, Viet-Cuong Nguyen, Yukihiro Abe, Truyen Tran, Huan Tran, Hiori Kino, Takashi Miyake, Koji Tsuda, Hieu-Chi Dam, npj Computational Materials, 9(215), (2023).
- Hierarchical GFlowNet for crystal structure generation, Nguyen, Tri, Sherif Tawfik, Truyen Tran, Sunil Gupta, Santu Rana, and Svetha Venkatesh. In AI for Accelerated Materials Design-NeurIPS 2023 Workshop. 2023.
- Machine learning-aided exploration of ultrahard materials, Tawfik, Sherif Abdulkader, Phuoc Nguyen, Truyen Tran, Tiffany R. Walsh, and Svetha Venkatesh. The Journal of Physical Chemistry C 126, no. 37 (2022): 15952-15961.
- Variational hyper-encoding networks, P Nguyen, T Tran, S Gupta, S Rana, HC Dam, S Venkatesh, ECML-PKDD'21, 2021
- Incomplete conditional density estimation for fast materials discovery, Phuoc Nguyen, Truyen Tran, Sunil Gupta, Svetha Venkatesh. SDM'19.
- Committee machine that votes for similarity between materials; Duong-Nguyen Nguyen, Tien-Lam Pham, Viet-Cuong Nguyen, Tuan-Dung Ho, Truyen Tran, Keisuke Takahashi and Hieu-Chi Dam. IUCrJ, 2018 Nov 1; 5(Pt 6): 830–840.
Computational chemistry:
- Graph transformation policy network for chemical reaction prediction, Kien Do, Truyen Tran, Svetha Venkatesh, KDD'19.
- Graph memory networks for molecular activity prediction, Trang Pham, Truyen Tran,
Svetha Venkatesh, ICPR'18.
- Neural reasoning for chemical-chemical interaction. Trang Pham, Truyen Tran,
Svetha Venkatesh, NIPS 2018 Workshop on Machine Learning for Molecules and Materials.
- Graph classification via deep learning with virtual nodes Trang Pham, Truyen Tran, Hoa Dam, Svetha
Venkatesh, Third Representation
Learning for Graphs Workshop (ReLiG 2017).
- Learning Recurrent Matrix
Representation, Kien Do, Truyen
Tran, Svetha
Venkatesh. Third Representation Learning for Graphs
Workshop (ReLiG 2017).
Computational biology:
- Personalized
Annotation-based Networks (PAN) for the prediction of breast cancer
relapse, T Nguyen, SC Lee, TP Quinn, B Truong, X Li, T Tran, S Venkatesh, TD Le, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021.
- Deep
in the bowel: Highly interpretable neural encoder-decoder networks
predict gut metabolites from gut microbiome, V Le, TP Quinn, T Tran, S Venkatesh, BMC Genomics (21), 07/2020.
- DeepTRIAGE:
Interpretable and individualised biomarker scores using attention
mechanism for the classification of breast cancer sub-types. A
Beykikhoshk, TP Quinn, SC Lee, T Tran,
S Venkatesh, BMC Medical Genomics, 2020.
- Graph transformation
policy
network for chemical reaction prediction, Kien Do, Truyen Tran, Svetha Venkatesh, KDD'19.
- Attentional multilabel
learning over graphs: A message passing approach, K Do, T Tran, T Nguyen, S Venkatesh, Machine Learning, 2019.
Drug discovery:
- Learning to discover medicines, Nguyen, Minh-Tri, Thin Nguyen, and Truyen Tran. International Journal of Data Science and Analytics (2022): 1-16.
- Mitigating cold-start problems in drug-target affinity prediction with interaction knowledge transferring, Nguyen, Tri Minh, Thin Nguyen, and Truyen Tran. Briefings in Bioinformatics 23, no. 4 (2022): bbac269.
- Explaining black box drug target prediction through model agnostic counterfactual samples, Nguyen, Tri Minh, Thomas P. Quinn, Thin Nguyen, and Truyen Tran. IEEE/ACM Transactions on Computational Biology and Bioinformatics (2022).
- GEFA: Early fusion approach in drug-target affinity prediction, Tri Minh Nguyen, Thin Nguyen, Thao Minh Le, Truyen Tran, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021.
- Attentional multilabel learning over graphs: A message passing approach, K Do, T Tran, T Nguyen, S Venkatesh, Machine Learning, 2019.
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