Sep 2019 - NOW
Aug 2014 - Aug 2019
Aug 2018 - Dec 2018
May 2018 - Aug 2018
May 2017 - Aug 2017
July 2013 - Apr 2014
Research Scientist, Google Brain
working with Dale Schuurmans in Google Brain
Research Assistant, School of Computational Science & Engineering, Gatech
working with Le Song in Machine Learning Group
Intern,
DeepMind
worked with
Pushmeet Kohli in Deep Learning Team
Intern,
OpenAI
worked with
John Schulman in Games Team
Applied Scientist Intern,
Amazon AI
worked with
Zornitsa Kozareva and
Alexander Smola in Deep Learning Team
Research Intern,
Microsoft Research Asia
worked with
Taifeng Wang
and
Tie-Yan Liu in Data Science Team
Contact
hadai@google.com
I'm a research scientist in Google Brain.
I obtained my Ph.D. degree in CSE 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 ]
Proceeding
Scalable Deep Generative Modeling for Sparse Graphs
Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, Dale Schuurmans
International Conference on Machine Learning (ICML) 2020.
[ arxiv ]
[ Code ]
Energy-Based Processes for Exchangeable Data
Mengjiao Yang*, Bo Dai*, Hanjun Dai, Dale Schuurmans
International Conference on Machine Learning (ICML) 2020.
[ arxiv ]
[ Code ]
Learning To Stop While Learning To Predict
Xinshi Chen, Hanjun Dai, Yu Li, Xin Gao, Le Song
International Conference on Machine Learning (ICML) 2020.
[ arxiv ]
[ Code ]
Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search
Binghong Chen, Chengtao Li, Hanjun Dai, Le Song
International Conference on Machine Learning (ICML) 2020.
[ arxiv ]
[ Code ]
Code2Inv: A Deep Learning Framework for Program Verification
Xujie Si*, Aaditya Naik*, Hanjun Dai, Mayur Naik and Le Song.
International Conference on Computer-Aided Verification (CAV) 2020.
[ Paper ]
[ Project page ]
Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs
Elizabeth Dinella*, Hanjun Dai*, Ziyang Li, Mayur Naik, Le Song and Ke Wang.
International Conference on Learning Representations (ICLR) 2020.
Spotlight
[ Paper ]
[ Code ]
Retrosynthesis Prediction with Conditional Graph Logic Network
Hanjun Dai, Chengtao Li, Connor Coley, Bo Dai and Le Song.
Advances in Neural Information Processing Systems (NeurIPS) 2019
[ arxiv ]
[ Code ]
Learning Transferable Graph Exploration
Hanjun Dai, Yujia Li, Chenglong Wang, Rishabh Singh, Po-Sen Huang and Pushmeet Kohli.
Advances in Neural Information Processing Systems (NeurIPS) 2019
[ arxiv ]
Exponential Family Estimation via Adversarial Dynamics Embedding
Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song and Dale Schuurmans.
Advances in Neural Information Processing Systems (NeurIPS) 2019
[ Paper ]
[ arxiv ]
[ Code ]
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 ]
[ Code ]
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 ] [ Code ]
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).
[ arxiv ] [ Code ]
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 ]
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]
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
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 2019-2020, Reviewer in: NeurIPS 2016-2020, ICML 2017-2020, UAI 2020, AISTATS 2019, ICLR 2019-2020, Pattern Recognition 2018, KDD 2018-2020, AAAI 2018, KDD 2017, TKDD 2017
ICML 2016 Volunteer
The webpage template is kindly provided by Nan Du and Yingyu Liang