Journals

  • Practical model selection for prospective virtual screening
    Shengchao Liu*, Moayad Alnammi*, Spencer Ericksen, Andrew F. Voter, James L Keck, F. Michael Hoffmann, Scott A. Wildman, Anthony Gitter
    ACS, Journal of Chemical Information and Modeling 2018
    [PDF] [Code]

Conferences

  • N-Gram Graph: A Simple Unsupervised Representation for Molecules
    Shengchao Liu, Mehmet Furkan Demirel, Yingyu Liang
    Neural Information Processing Systems (NeurIPS), 2019. (Spotlight)
    [PDF] [Code]

  • Loss-Balanced Task Weighting to Reduce Negative Transfer in Multi-Task Learning
    Shengchao Liu, Yingyu Liang, Anthony Gitter
    AAAI-SA 2019
    [PDF] [Appendix] [Code] [Poster]

  • Atomo: Communication-efficient Learning via Atomic Sparsification
    Hongyi Wang*, Scott Sievert*, Shengchao Liu, Zachary Charles, Dimitris Papailiopoulos, Stephen Wright
    NeurIPS 2018
    [PDF] [Code]

Workshops

  • Bad Global Minima Exist and SGD Can Reach Them (Oral)
    Shengchao Liu, Dimitris Papailiopoulos, Dimitris Achlioptas
    Identifying and Understanding Deep Learning Phenomena, ICML 2019 Workshop
    [PDF][Code]

  • Learning Molecule Drug Function from Structure Representations with Deep Neural Networks or Random Forests
    Jesse G. Meyer, Shengchao Liu, Ian J. Miller, Anthony Gitter, Joshua J. Coon
    Great Lake Bioinformatics (GLBIO) 2019
    [PDF] [Code]

  • N-Gram Graph, A Novel Molecule Representation
    Shengchao Liu, Thevaa Chandereng, Yingyu Liang
    Machine Learning for Molecules and Materials, NeurIPS 2018 Workshop
    [PDF][Poster]

  • A Novel Molecule Structure Learning Method for Drug Discovery
    Shengchao Liu, Thevaa Chandereng
    Midwest Biopharamceutical Statistics Workshop, 2018
    [Poster]

Preprints and Manuscripts

  • Bad Global Minima Exist and SGD Can Reach Them
    Shengchao Liu, Dimitris Papailiopoulos, Dimitris Achlioptas
    [PDF] [Code]
  • Learning Molecule Drug Function from Structure Representations with Deep Neural Networks or Random Forests
    Jesse G. Meyer, Shengchao Liu, Ian J. Miller, Anthony Gitter, Joshua J. Coon
    [PDF] [Code]

  • N-Gram Graph, A Novel Molecule Representation
    Shengchao Liu, Thevaa Chandereng, Yingyu Liang
    [PDF] [Code]

Symposiums

  • Loss-Balanced Task Weighting to Reduce Negative Transfer in Multi-Task Learning
    Shengchao Liu, Yingyu Liang, Anthony Gitter
    Third Midwest Machine Learning Symposium, 2019
    [PDF]

  • N-Gram Graph, A Novel Molecule Representation
    Shengchao Liu, Thevaa Chandereng, Mehmet Furkan Demirel, Yingyu Liang
    Third Midwest Machine Learning Symposium, 2019
    [PDF]

  • A Tool for Simulation in Adaptive Bayesian Clinical Trial
    Thevaa Chandereng, Donald Musgrove, Shengchao Liu, Tarek Haddad, Rick Chappell
    Twenty-fifth Annual Biopharmaceutical Applied Statistics Symposium, 2018
    [Poster]

  • An Order Invariant Structure Learning Method for Molecule Classification
    Shengchao Liu, Thevaa Chandereng, Yingyu Liang
    Second Midwest Machine Learning Symposium, 2018
    [Poster]

  • Practical model selection for virtual chemical screening
    Shengchao Liu*, Moayad Alnammi*, Spencer Ericksen, Andrew F. Voter, James L Keck, F. Michael Hoffmann, Scott A. Wildman, Anthony Gitter
    Morgridge Institute for Research: Scientific Advisory Board, 2018
    [Poster]

  • Practical model selection for virtual chemical screening
    Shengchao Liu*, Moayad Alnammi*, Spencer Ericksen, Andrew F. Voter, James L Keck, F. Michael Hoffmann, Scott A. Wildman, Anthony Gitter
    Center for Predictive Computational Phenotyping, Third Annual Retreat, 2018
    [Poster]

  • Comprehensive Benchmarking for Label-Free Quantitative Proteomics
    Thevaa Chandereng*, Shengchao Liu*, John Denu, Anthony Gitter, James Dowell
    US HUPO 2018
    [Poster]

  • Scrutinizing Deep Learning: A Virtual Screening Case Study
    Shengchao Liu*, Moayad Alnammi*, Scott A. Wildman, Spencer Ericksen, Haozhen Wu, Andrew F. Voter, James L Keck, F. Michael Hoffmann, Anthony Gitter
    Morgridge Institute for Research: Scientific Advisory Board, 2017
    [Abstract] [Poster]

  • Scrutinizing Deep Learning: A Virtual Screening Case Study
    Shengchao Liu*, Moayad Alnammi*, Scott A. Wildman, Spencer Ericksen, Haozhen Wu, Andrew F. Voter, James L Keck, F. Michael Hoffmann, Anthony Gitter
    Center for Predictive Computational Phenotyping, Third Annual Retreat, 2017
    [Abstract] [Poster]

Presentations

Thesis

Awards and Honors

  • Travel Award, NeurIPS 2018
  • Travel Award, Midwest Biopharamceutical Statistics Workshop 2018

* indicates co-first authors.