I am Programme Head, Precision Medicine and Scientist in the Deep Learning and Healthcare departments at the Institute for Infocomm Research (I2R), A*STAR. I develop deep learning algorithms that can learn from less labeled data, inspired by applications in healthcare and medicine where collecting large, well-annotated datasets is often time and cost-prohibitive due to the need for careful expert labeling.

I earned a BS ('08), MS ('12) and PhD ('17) in Computer Science from Stanford, where I had the privilege to work with Professors Anshul Kundaje, Daphne Koller, Andrew Ng and Serafim Batzoglou on machine learning and deep learning for biology. I took a year off during my PhD to work for Counsyl, where I developed software (variant calling and LIMS integration) for clinically validated sequencing-based genetic tests.

Publications

  • Semi-supervised Audio Classification with Consistency-Based Regularization

    INTERSPEECH (2019)

    Kangkang Lu, Chuan-Sheng Foo, Kah Kuan Teh, Tran Huy Dat, Vijay Ramaseshan Chandrasekhar

    [paper (coming soon)]
  • TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks

    ACM/IEEE International Symposium on Low Power Electronics and Design (2019)

    Lile Cai, Anne-Maelle Barneche, Arthur Herbout, Chuan Sheng Foo, Jie Lin, Vijay Ramaseshan Chandrasekhar and Mohamed M. Sabry

    [paper (coming soon)]
  • MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Object Detectors

    IEEE Conference on Computer Vision and Pattern Recognition (2019)

    Lile Cai, Bin Zhao, Zhe Wang, Jie Lin, Chuan Sheng Foo, Mohamed Sabry Aly, Vijay Chandrasekhar

    [paper]
  • Encoding Knowledge Graph with Graph CNN for Question Answering

    International Conference on Learning Representations Workshop on Representation Learning on Graphs and Manifolds (2019)

    Leo Laugier*, Anran Wang*, Chuan-Sheng Foo, Theo Guenais and Vijay Chandrasekhar

    (* equal contribution)

    [paper]
  • Optimistic mirror descent in saddle-point problems: Going the extra(-gradient) mile

    International Conference on Learning Representations (2019)

    Panayotis Mertikopoulos, Bruno Lecouat, Houssam Zenati, Chuan-Sheng Foo, Vijay Chandrasekhar, Georgios Piliouras

    [paper, code (coming soon)]
  • The Unusual Effectiveness of Averaging in GAN Training

    International Conference on Learning Representations (2019)

    Yasin Yazıcı, Chuan-Sheng Foo, Stefan Winkler, Kim-Hui Yap, Georgios Piliouras, Vijay Chandrasekhar

    [paper, code]
  • Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images

    NeurIPS Workshop on Machine Learning for Health (2018)

    Bruno Lecouat, Ken Chang, Chuan-Sheng Foo, Balagopal Unnikrishnan, James Brown, Houssam Zenati, Andrew Beers, Vijay Chandrasekhar, Pavitra Krishnaswamy and Jayashree Kalpathy-Cramer

    [paper, code (coming soon)]
  • Predicting thermoelectric properties from crystal graphs and material descriptors – first application for functional materials

    NeurIPS Workshop on Machine Learning for Molecules and Materials (2018)

    Leo Laugier, Daniil Bash, Jose Recatala, Hong Kuan Ng, Savitha Ramasamy, Chuan-Sheng Foo, Vijay R. Chandrasekhar, Kedar Hippalgaonkar

    [paper]
  • Adversarially Learned Anomaly Detection

    IEEE International Conference on Data Mining (2018)

    Houssam Zenati*, Manon Romain*, Chuan-Sheng Foo*, Bruno Lecouat, Vijay Chandrasekhar

    (* equal contribution)

    (Full Paper)

    [paper, arXiv, code]
  • Semi-Supervised Learning With GANs: Revisiting Manifold Regularization

    International Conference on Learning Representations, Workshop Track (2018)

    Bruno Lecouat*, Chuan Sheng Foo*, Houssam Zenati, Vijay Chandrasekhar (* equal contribution)

    [paper, arXiv, code]
  • Learning RNA secondary structure (only) from structure probing data

    ICML Workshop on Computational Biology (2017)

    Chuan-Sheng Foo*, Cristina Pop* (* equal contribution)

    [paper (bioRxiv)]
  • The C2H2-ZF transcription factor Zfp335 recognizes two consensus motifs using separate zinc finger arrays

    Genes & Development 30: 1509-1514 (2016)

    Brenda Yuyuan Han, Chuan-Sheng Foo, Shuang Wu and Jason G. Cyster

    [paper]
  • Zinc finger protein Zfp335 is required for the formation of the naïve T cell compartment

    eLife 3: e03549 (2014)

    Brenda Y. Han, Shuang Wu, Chuan-Sheng Foo, Robert M. Horton, Craig N. Jenne, Susan R. Watson, Belinda Whittle, Chris C. Goodnow, and Jason G. Cyster

    [paper]
  • A majorization-minimization algorithm for (multiple) hyperparameter learning

    Proceedings of the 26th International Conference on Machine Learning, 321–328 (2009)

    Chuan-Sheng Foo, Chuong B. Do and Andrew Y. Ng

    [pdf, slides]
  • Proximal regularization for online and batch learning

    Proceedings of the 26th International Conference on Machine Learning, 257–264 (2009)

    Chuong B. Do, Quoc V. Le and Chuan-Sheng Foo

    [pdf, code]
  • Searching for rising stars in bibliography networks

    Proceedings of the 14th International Conference on Database Systems for Advanced Applications (2009)

    Xiao-Li Li, Chuan-Sheng Foo, Kar Leong Tew, See-Kiong Ng

    [pdf]
  • A max-margin model for efficient simultaneous alignment and folding of RNA sequences

    Bioinformatics, 24(13):i68–76 (2008)

    Chuong B. Do, Chuan-Sheng Foo and Serafim Batzoglou

    [pdf, code]
  • Efficient multiple hyperparameter learning for log-linear models

    Advances in Neural Information Processing Systems 20:377–384 (2007)

    Chuong B. Do, Chuan-Sheng Foo and Andrew Y. Ng

    [pdf]
  • Discovering protein complexes in dense reliable neighborhoods of protein interaction networks

    Computational Systems Bioinformatics: CSB 2007 Conference Proceedings, 6:157–68 (2007)

    Xiaoli Li, Chuan-Sheng Foo and See-Kiong Ng

    [pdf, website]
  • Interaction graph mining for protein complexes using local clique merging

    [ Best Paper Award, GIW 2005 ]

    Genome Informatics, 16(2):260–9 (2005)

    Xiaoli Li, Soon-Heng Tan, Chuan-Sheng Foo and See-Kiong Ng

    [pdf]

Preprints

  • Manifold regularization with GANs for semi-supervised learning

    Bruno Lecouat*, Chuan-Sheng Foo*, Houssam Zenati, Vijay Chandrasekhar (* equal contribution)

    [paper]
  • Efficient GAN-Based Anomaly Detection

    Houssam Zenati*, Chuan Sheng Foo*, Bruno Lecouat, Gaurav Manek, Vijay Chandrasekhar

    (* equal-contribution)

    [paper]

Conference Abstracts

  • Deep learning the relationship between chromatin architecture, chromatin state, and transcription factor binding [ Platform Talk ]

    65th Annual Meeting of The American Society of Human Genetics

    Anshul Kundaje, Chuan-Sheng Foo, Johnny Israeli, Avanti Shrikumar, Jason Buenrostro, Alicia Schep and William Greenleaf

  • ATAC-seq is predictive of chromatin state

    2015 Cold Spring Harbor Laboratory meeting on Systems Biology: Global Regulation of Gene Expression

    Chuan-Sheng Foo, Sarah Denny, Jason Buenrostro, William Greenleaf, Anshul Kundaje

  • ATAC-seq is predictive of chromatin state [ Oral Presentation ]

    2014 RECOMB/ISCB Conference on Regulatory and Systems Genomics, with DREAM Challenges and Cytoscape Workshops

    Chuan-Sheng Foo, Sarah Denny, Jason Buenrostro, William Greenleaf, Anshul Kundaje

  • Ab-initio identification of chromatin states from chromatin accessibility data with CASCADE

    2014 Cold Spring Harbor Laboratory meeting on The Biology of Genomes

    Chuan-Sheng Foo*, Sarah K. Denny*, Margaret X. Fu*, Jason D. Buenrostro, Anshul B. Kundaje and William J. Greenleaf (*equal contribution)

  • Validation of and initial experience with a next-generation sequencing-based 98-gene expanded carrier screening assay

    Association for Molecular Pathology 2013 Annual Meeting

    Hyun-Seok Peter Kang, Jared Maguire, Jonathan Tritt, Chuan-Sheng Foo, Alexander Scott Patterson and Eric A. Evans

  • Gene expression deconvolution

    Biomedical Computation at Stanford 2010

    Chuan-Sheng Foo, Shai S. Shen-Orr and Daphne Koller

Honors

  • Computing Research Association Outstanding Undergraduate Researcher Award (2008)
  • UCSD Data Mining Contest: 1st, 2nd (2006), 3rd (2007), 2nd (2008)
  • Top 200 ranking on the Putnam Mathematics Competition (2005)
  • President's Award for Academic Achievement in the Freshman Year (2006)
  • Best Paper Award at the 16th International Conference on Genome Informatics (2005)
  • A*STAR National Science Scholarship (2002)
  • Bronze medal at the 33rd International Physics Olympiad (2002)