Welcome

Bo DAI

Computational Science and Engineering

College of Computing

Georgia Institute of Technology

Email: bodai AT gatech.edu

Google Scholar

Short Bio

I am currently a Ph.D. candidate in Computational Science and Engineering at Georgia Tech, supervised by Prof. Le Song. My principal research interests lie on core machine learning methodology for large-scale structured data. Recently, I am focusing on developing effective statistical models and efficient algorithms for learning from a massive volume of complex, structured, uncertain and high-dimensional data, e.g., distributions, structures, dynamics, and so on.

My recent work includes

  • Reinforcement learning: design effective algorithms for exploiting the recursive structure in the dynamics.
  • Large-scale nonparametric machine learning: develop efficient algorithms for machine learning methods, especially nonparametric methods, to handle hundreds of millions of data.
  • Structured input and output: build effective models for capturing the structures information in input and output, e.g., binaries, sequences, trees, and graphs.

News

  • 2018/05: Three papers have been accepted to ICML2018.
  • 2018/05: One paper has been accepted to UAI2018.
  • 2018/03: Talk at UIUC
  • 2018/03: Talk at UChicago.
  • 2018/03: Talk at UIC.
  • 2018/03: Talk at UNC.
  • 2018/03: Talk at Nvidia.
  • 2018/03: One paper has been accepted to IJCAI-ECAI2018.
  • 2018/02: Talk at Google Research, NYC.
  • 2018/02: One papers has been accepted to CVPR2018.
  • 2018/02: Talk at MSR, NYC.
  • 2018/02: Talk at MSR, Redmond.
  • 2018/01: Two papers have been accepted to ICLR2018.
  • 2017/12: One paper has been accepted to AISTATS2018.
  • 2017/12: Our paper, "Syntax-Directed Variational Autoencoder for Molecule Generation", won the Best Paper Award in NIPS2017 Machine Learning for Molecules and Materials workshop .
  • 2017/09: One paper has been accepted to NIPS2017.
  • 2017/05: Two papers have been accepted to ICML2017.
  • 2017/05: Start my internship at Microsoft Research, Redmond, with Lin Xiao, Lihong Li, and Jianshu Chen.
  • 2017/02: One paper has been accepted to ICLR2017.
  • 2016/12: One paper has been accepted to AISTATS2017.
  • 2016/05: Start my internship at Google Research, NYC, with Sanjiv Kumar and Ruiqi Guo.
  • 2016/05: Our paper, "Provable Bayesian Inference via Particle Mirror Descent", won the AISTATS2016 Best Student Paper Award.
  • 2016/04: One paper has been accepted to ICML2016.
  • 2016/01: One paper has been accepted to AISTATS2016.
  • 2015/11: Thank Adobe for providing me travel grants to NIPS!
  • 2015/11: We present our paper, "Provable Bayesian Inference via Particle Mirror Descent", on NIPS2015 workshop "Advances in Approximate Bayesian Inference" and "Scalable Monte Carlo Methods for Bayesian Analysis of Big Data".
  • 2014/09: One paper has been accepted to NIPS2014.
  • 2014/04: Two papers have been accepted to ICML2014.
  • 2014/02: One paper has been accepted to Neural Computation.