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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

• 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).

• 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.

• 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.