Hanjun Dai

Georgia Tech, College of Computing

About Me

Aug 2014 - NOW





May 2018 - Aug 2018


May 2017 - Aug 2017


May 2016 - Aug 2016


July 2013 - Apr 2014

Contact

hanjundai AT gatech Dot edu

Biography

I am a fourth year Ph.D. student in Computer Science in Georgia Institute of Technology. My advisor is Prof. Le Song.

I received my B.S. in Computer Science, Fudan University in 2014. My advisor is Prof. Junping Zhang.

[ Google Scholar ] [ Github ]

Publications

Proceeding

  • Learning Steady-States of Iterative Algorithms over Graphs
    Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alexander Smola and Le Song.
    International Conference on Machine Learning (ICML) 2018
    [ Paper ]

  • Adversarial Attack on Graph Structured Data
    Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song.
    International Conference on Machine Learning (ICML) 2018
    [ arxiv ] [ Code ]

  • Syntax-Directed Variational Autoencoder for Structured Data.
    Hanjun Dai*, Yingtao Tian*, Bo Dai, Steven Skiena and Le Song (*Equal contributions).
    International Conference on Learning Representations (ICLR) 2018
    [ arxiv ] [ Code ]

  • Variational Reasoning for Question Answering with Knowledge Graph.
    Yuyu Zhang*, Hanjun Dai*, Zornitsa Kozareva, Alexander Smola and Le Song (*Equal contributions).
    AAAI Conference on Artificial Intelligence (AAAI) 2018. Oral
    [ arxiv ]

  • Learning Combinatorial Optimization Algorithms over Graphs
    Hanjun Dai*, Elias B. Khalil*, Yuyu Zhang, Bistra Dilkina and Le Song (*Equal contributions).
    Advances in Neural Information Processing Systems (NIPS) 2017. Spotlight
    [ arxiv ] [ Code ]

  • Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
    Rakshit Trivedi, Hanjun Dai, Yichen Wang and Le Song.
    International Conference on Machine Learning (ICML) 2017
    [ arxiv ] [ Code ]

  • Recurrent Hidden Semi-Markov Model.
    Hanjun Dai, Bo Dai, Yan-Ming Zhang, Shuang Li and Le Song.
    International Conference on Learning Representations (ICLR) 2017
    [ Paper ] [ Code ]

  • Recurrent Coevolutionary Feature Embedding Processes for Recommendation
    Hanjun Dai*, Yichen Wang*, Rakshit Trivedi and Le Song (*Equal contributions)
    Recsys Workshop on Deep Learning for Recommender Systems (DLRS), 2016. Best Paper
    [ Paper ]

  • Recurrent Marked Temporal Point Processes: Embedding Event History to Vector.
    Nan Du, Hanjun Dai, Rakshit Trivedi, Utkarsh Upadhyay, Manuel Gomez-Rodriguez and Le Song.
    ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) 2016.
    [ Paper ] [ Code ]

  • Discriminateive Embeddings of Latent Variable Models for Structured Data.
    Hanjun Dai, Bo Dai and Le Song.
    International Conference on Machine Learning (ICML) 2016.
    [ arxiv ] [ Code ]

  • Provable Bayesian Inference via Particle Mirror Descent.
    Bo Dai, Niao He, Hanjun Dai and Le Song.
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2016. Best Student Paper
    [ Paper ]

  • M-Statistic for Kernel Change-Point Detection.
    Shuang Li, Yao Xie, Hanjun Dai and Le Song.
    Neural Information Processing Systems (NIPS), 2015.
    [ Paper ]

  • Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks.
    Yuyu Zhang, Hanjun Dai, Chang Xu, Taifeng Wang, Jiang Bian and Tie-Yan Liu.
    AAAI Conference on Artificial Intelligence (AAAI), 2014.
    [ Paper ]

  • A Scalable Probabilistic Model for Learning Multi-Prototype Word Embedding
    Fei Tian, Hanjun Dai, Jiang Bian, Bin Gao, Rui Zhang and Tie-Yan Liu.
    International Conference on Computational Linguistics (COLING), 2014.
    [ Paper ]

Journal

  • Deep Coevolutionary Network: A Generic Embedding Framework for Temporally Evolving Graphs
    Hanjun Dai*, Yichen Wang*, Rakshit Trivedi and Le Song (*Equal contributions).
    ACM Transactions on Knowledge Discovery from Data (TKDD), 2017. under review
    [ arxiv ]

  • Material Structure-property Linkages Using Three-dimensional Convolutional Neural Networks.
    Ahmet Cecen, Hanjun Dai, Yuksel C. Yabansu, Surya R. Kalidindi and Le Song.
    Acta Materialia, 2017
    [ Paper ]

  • Sequence2Vec: A novel embedding approach for modeling transcription factor binding affinity landscape.
    Hanjun Dai*, Ramzan Umarov*, Hiroyuki Kuwahara, Yu Li, Le Song and Xin Gao(*Equal contributions).
    Bioinformatics, 2017, 1-9, DOI: 10.1093/bioinformatics/btx480
    [ Paper ] [ Supplement ] [ Code ]

  • KNET: A General Framework for Learning Word Embedding Using Morphological Knowledge.
    Qing Cui, Bin Gao, Jiang Bian, Siyu Qiu, Hanjun Dai and Tie-Yan Liu.
    ACM Transactions on Information Systems (TOIS), 2015.
    [ Paper ]

Workshop

  • Syntax-Directed Variational Autoencoder for Molecule Generation
    Hanjun Dai*, Yingtao Tian*, Bo Dai, Steven Skiena and Le Song (*Equal contributions)
    NIPS 2017 Workshop on Machine Learning for Molecules and Materials, 2017. Best Paper
    [ Paper ]

Software

GraphNN a general purpose deep neural network library with special design for structured data and dynamic computational graph. It comes with the unified CPU/GPU API. With the low-level support from Intel MKL and Cuda, it is very efficient.

[ Code][ Documentation]

Selected Awards

1st place in ByteCup International Machine Learning Competition 2016

Ranked 15 in ACM-ICPC World Finals 2015

Runner-up in ACM-ICPC 2014 Southeast USA Reginal and ACM-ICPC 2010 Dhaka Site

Chun-Tsung Scholar (established by Nobel Prize laureate, Tsung-Dao Lee)

CSC-IBM Excellence Scholarship 2013

National Scholarship, Fudan University, 2012

Activities

Invited Talks

Syntax-directed Variational Autoencoder for Molecule Generation.
Machine Learning for Molecules and Materials (NIPS Workshop Spotlight), Long Beach, Dec 2017.
[ pdf ]

Learning Combinatorial Optimization Algorithms over Graphs.
Advances in Neural Information Processing Systems (NIPS Spotlight), Long Beach, Dec 2017.
[ Video ]

Learning Combinatorial Optimization Algorithms over Graphs.
HotCSE Seminar (HotCSE), Atlanta, Nov 2017.
[ Abstract ] [ Slides ]

Graph Representation Learning with Deep Embedding Approach.
Machine Learning Conference (MLConf), Atlanta, Sep 2017.
[ Abstract ] [ Reference ] [ Video ] [ Slides ]

Discriminateive Embeddings of Latent Variable Models for Structured Data.
International Conference on Machine Learning (ICML), New York, June 2016.
[ TechTalks ]

Teaching

Fall 2015, CSE 6740, Computational Data Analysis

2015, Coach Assistant in Georgia Tech Programming Team [Link]

Service

Reviewer in: Pattern Recognition 2018, ICML 2018, KDD 2018, AAAI 2018, NIPS 2017, ICML 2017, KDD 2017, NIPS 2016, TKDD 2017

ICML 2016 Volunteer

Skills

Programming

C/C++    Python    Matlab  R   Java   Scala   C#   SQL  

HPC

MPI  OpenMP  CUDA  Spark  Hadoop


The webpage template is kindly provided by Nan Du and Yingyu Liang