Nam D. Nguyen | Stony Brook University

About

I am a Ph.D. candidate in Computer Science at Stony Brook University, and a research fellow in Daifeng Wang Laboratory in Waisman Center at University of Wisconsin-Madison. My current research interest is machine learning on graphs and manifolds, and their applications for understanding molecular mechanisms and improving genotype-phenotype predictions in complex biological systems. I earned my B.Eng. in Computer Science from Hanoi University of Science and Technology.
Personally, I am an avid readear and an amateur photographer.
[Resume]

Bioinformatics: computational genomics, single-cell multiomics, biological networks
Machine Learning: multiview learning, geometric deep learning, interpretable learning, statistical learning theory

[Dec 2020] I pass my thesis proposal defense on Interpretable Multiview Learning for Understanding Functional Multiomics.
[Nov 2020] I give an oral presentation in RECOMB RSG 2020.
[Nov 2020] Our paper on a generative model for revealing gene regulatory mechanisms is published in Bioinformatics.
[Oct 2020] Our paper on an interpretable deep architecture for genotype-phenotype interactions is published in Bioinformatics.

*equal contribution

ECMarker: interpretable machine learning model identifies gene expression biomarkers predicting clinical outcomes and reveals molecular mechanisms of human disease in early stages
Ting Jin*, Nam D. Nguyen*, Flaminia Talos, Daifeng Wang
Bioinformatics (2020), btaa935, https://doi.org/10.1093/bioinformatics/btaa935.
[PDF] [Code]
Varmole: a biologically drop-connect deep neural network model for prioritizing disease risk variants and genes
Nam D. Nguyen, Ting Jing, Daifeng Wang
Bioinformatics (2020), btaa866, https://doi.org/10.1093/bioinformatics/btaa866.
[PDF] [Code]
Multiview learning for understanding functional multiomics
Nam D. Nguyen, Daifeng Wang
PLOS Computational Biology 16.4 (2020), e1007677, https://doi.org/10.1371/journal.pcbi.1007677.
[PDF]
ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks
Nam D. Nguyen, Ian K. Blaby, Daifeng Wang
BMC Genomics 20.12 (2019), pp. 1–14, https://doi.org/10.1186/s12864-019-6329-2.
[PDF] [Code]

deepManReg: A deep manifold‐regularized learning model for improving phenotype prediction from multi‐modal data
Nam D. Nguyen, Jiawei Huang, Daifeng Wang
bioRxiv 2021.01.28.428715, https://doi.org/10.1101/2021.01.28.428715.
[PDF] [Code]

Varmole: a biologically drop-connect deep neural network model for prioritizing disease risk variants and genes
The 13th annual RECOMB/ISCB Conference on Regulatory & Systems Genomics with DREAM Challenges. RECOMB RSG DREAM 2020. [Slides]
Multiview learning for understanding functional multiomics
The 28th Conference on Intelligent Systems for Molecular Biology. ISMB 2020. [Slides] [Poster]
ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks
International Conference on Intelligent Biology and Medicine. ICIBM 2019.
Columbus, OH, USA, June 9-11, 2019 [Slides]