Binghong Chen

PhD Student, ML@GT

binghong [AT] gatech.edu

Bio

I am a Ph.D. student in the Machine Learning Center at Georgia Tech (ML@GT) advised by Le Song. My research interests lie in neurally-guided search algorithms, neural symbolic learning and Reinforcement Learning. I am working/worked on developing learning-based algorithms for drug design, retrosynthesis of molecules/polymers, SAT/SMT solving, theorem proving, neural symbolic learning, and path planning.

Publications

Molecule Optimization by Explainable Evolution

Binghong Chen, Tianzhe Wang, Chengtao Li, Hanjun Dai, Le Song

International Conference on Learning Representations (ICLR) 2021

ARBITRAR: User-Guided API Misuse Detection

Ziyang Li, Aravind Machiry, Binghong Chen, Ke Wang, Mayur Naik, Le Song

IEEE Symposium on Security and Privacy (IEEE S&P) 2021

Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search

Binghong Chen, Chengtao Li, Hanjun Dai, Le Song

International Conference on Machine Learning (ICML) 2020

Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees

Binghong Chen, Bo Dai, Qinjie Lin, Guo Ye, Han Liu, Le Song

International Conference on Learning Representations (ICLR) 2020 spotlight

GLAD: Learning Sparse Graph Recovery

Harsh Shrivastava, Xinshi Chen, Binghong Chen, Guanghui Lan, Srinivas Aluru, Le Song

International Conference on Learning Representations (ICLR) 2020

PolyRetro: Few-shot Polymer Retrosynthesis via Domain Adaptation

Binghong Chen, Chengtao Li, Hanjun Dai, Rampi Ramprasad, Le Song

under review at International Conference on Machine Learning (ICML) 2021

Differentiable End-to-End Program Executor for Sample and Computationally Efficient VQA

Karan Samel, Zelin Zhao, Kuan Wang, Robin Luo, Binghong Chen, Le Song

under review at International Conference on Machine Learning (ICML) 2021

Scallop: Combining Neural Perception and Deductive Reasoning for VQA

Jiani Huang, Binghong Chen, Xujie Si, Karan Samel, Le Song, Mayur Naik

under review at International Conference on Machine Learning (ICML) 2021

Molecule Optimization by Explainable Evolution

Binghong Chen, Tianzhe Wang, Chengtao Li, Hanjun Dai, Le Song

International Conference on Learning Representations (ICLR) 2021

ARBITRAR: User-Guided API Misuse Detection

Ziyang Li, Aravind Machiry, Binghong Chen, Ke Wang, Mayur Naik, Le Song

IEEE Symposium on Security and Privacy (IEEE S&P) 2021

Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search

Binghong Chen, Chengtao Li, Hanjun Dai, Le Song

International Conference on Machine Learning (ICML) 2020

Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees

Binghong Chen, Bo Dai, Qinjie Lin, Guo Ye, Han Liu, Le Song

International Conference on Learning Representations (ICLR) 2020 spotlight

GLAD: Learning Sparse Graph Recovery

Harsh Shrivastava, Xinshi Chen, Binghong Chen, Guanghui Lan, Srinivas Aluru, Le Song

International Conference on Learning Representations (ICLR) 2020

PolyRetro: Few-shot Polymer Retrosynthesis via Domain Adaptation

Binghong Chen, Chengtao Li, Hanjun Dai, Rampi Ramprasad, Le Song

under review at International Conference on Machine Learning (ICML) 2021

Differentiable End-to-End Program Executor for Sample and Computationally Efficient VQA

Karan Samel, Zelin Zhao, Kuan Wang, Robin Luo, Binghong Chen, Le Song

under review at International Conference on Machine Learning (ICML) 2021

Learning Neural Retrosynthetic Planning

Binghong Chen, Chengtao Li, Hanjun Dai, Le Song

AI Powered Drug Discovery and Manufacturing Conference (AIDM) 2020

Learning Retrosynthetic Planning with Chemical Reasoning

Binghong Chen, Chengtao Li, Hanjun Dai, Le Song

Bridge Between Perception and Reasoning: GNN and Beyond Workshop (ICML) 2020 spotlight

Learning to Plan via Neural Exploration-Exploitation Trees

Binghong Chen, Bo Dai, Le Song

Learning Transferable Skills Workshop (NeurIPS) 2019

Scallop: Combining Neural Perception and Deductive Reasoning for VQA

Jiani Huang, Binghong Chen, Xujie Si, Karan Samel, Le Song, Mayur Naik

under review at International Conference on Machine Learning (ICML) 2021

Speeding up Computational Morphogenesis with Online Neural Synthetic Gradients

Yuyu Zhang, Heng Chi, Binghong Chen, Tsz Ling Elaine T., Lucia M., Le Song, Glaucio H. P.

under review at International Joint Conference on Neural Networks (IJCNN) 2021

Selected Projects

Learning Retrosynthetic Planning
Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search
Neurally-guided Path Planning
Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees

Vitæ

Full Resume in PDF.

  • Google Research 2021 - now
    Research Intern
  • Machine Learning Group @ GT 2017 - now
    Research Assistant
    Advised by Le Song
  • Georgia Institute of Technology 2017 - now
    Ph.D. Student
    Machine Learning Center
  • Carnegie Mellon University Summer 2016
    Research Intern
    Advised by Eric P. Xing
  • Tsinghua SAIL Group 2014-2017
    Undergraduate Research Assistant
    Advised by Jun Zhu
  • Tsinghua University 2013 - 2017
    B.Eng. Student
    Department of Computer Science

Services

Program committee (reviewer):
NeurIPS 2021/2020, ICML 2021/2020, ICLR 2021/2020, AISTATS 2021, IJCAI 2021, AAAI 2021/2020/2019

Awards

  • Outstanding Graduate, Tsinghua University, 2017
  • National Scholarship (<2%), Ministry of Education of China, 2016
  • Scholarship of Academic Excellence, Tsinghua University, 2015/2014
  • Silver Medal (<0.02%), China Mathematics Olympiad, 2013

Acknowledgement

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