Hanjun Dai

Georgia Tech, College of Computing

About Me

Aug 2014 - NOW





Aug 2018 - Dec 2018


May 2018 - Aug 2018


May 2017 - Aug 2017


July 2013 - Apr 2014

Contact

hanjundai AT gatech Dot edu

Biography

I'll be joining Google Brain as a research scientist.

I am a fifth 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

  • Particle Flow Bayes’ Rule
    Xinshi Chen, Hanjun Dai and Le Song.
    International Conference on Machine Learning (ICML) 2019
    [ arxiv ] [ Code ]

  • CompILE: Compositional Imitation Learning and Execution
    Thomas Kipf, Yujia Li, Hanjun Dai, Vinicius Zambaldi, Alvaro Sanchez-Gonzalez, Edward Grefenstette, Pushmeet Kohli and Peter Battaglia.
    International Conference on Machine Learning (ICML) 2019
    [ arxiv ]

  • Kernel Exponential Family Estimation via Doubly Dual Embedding
    Bo Dai*, Hanjun Dai*, Arthur Gretton, Le Song, Dale Schuurmans, Niao He (*Equal contributions).
    International Conference on Artificial Intelligence and Statistics (AISTATS) 2019,
    [ arxiv ] [ Code ]

  • Learning a Meta-Solver for Syntax-Guided Program Synthesis
    Xujie Si*, Yuan Yang*, Hanjun Dai, Mayur Naik, Le Song (*Equal contributions).
    International Conference on Learning Representations (ICLR) 2019,
    [ Paper ] [ Code ]

  • Learning Loop Invariants for Program Verification
    Xujie Si*, Hanjun Dai*, Mukund Raghothaman, Mayur Naik and Le Song (*Equal contributions).
    Advances in Neural Information Processing Systems (NIPS) 2018, Spotlight
    [ Paper ] [ Code ]

  • Coupled Variational Bayes via Optimization Embedding
    Bo Dai*, Hanjun Dai*, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao and Le Song (*Equal contributions).
    Advances in Neural Information Processing Systems (NIPS) 2018
    [ Paper ] [ Code ]

  • Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification
    Harsh Shrivastava, Eugene Bart, Bob Price, Hanjun Dai, Bo Dai and Srinivas Aluru.
    Advances in Neural Information Processing Systems (NIPS) 2018
    [ Paper ] [ Code ]

  • 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 ] [ Code ]

  • 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 ] [ Code ]

  • 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

Adversarial Attack on Graph Structured Data.
Cybersecurity Lecture Series (IISP, Gatech), Atlanta, March 2019.
[ Video ]

Learning Loop Invariants for Program Verification.
Advances in Neural Information Processing Systems (NIPS Spotlight), Montréal, Canada, Dec 2018.
[ Video ]

Learning with Structured Data.
Benevolent AI, London, Nov 2018.
[ Slides ]

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

Summer 2019, CS 3510, Design and analysis of algorithms

Spring 2019, CX 4240, Introduction to Computational Data Analysis

Fall 2015, CSE 6740, Computational Data Analysis

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

Service

Senior PC in AAAI 2019, Reviewer in: NeurIPS 2019, ICML 2019, AISTATS 2019, ICLR 2019, Pattern Recognition 2018, ICML 2018, KDD 2018, AAAI 2018, NIPS 2017, ICML 2017, KDD 2017, NIPS 2016, TKDD 2017

ICML 2016 Volunteer


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