I am a PhD student in the Department of Computer Science at Stony Brook University advised by Prof. Steven Skiena. My Curriculum Vitae is available.

I will join Google Brain Tokyo for a research position in July 2019 after graduation.

My research interests lie in representation learning and generative models , as well as their applications in natural language processing, knowledge base modeling, social network modeling, image generation and bioinformatics. Details can be found in publications.

Online Showcase I make some interesting things in the show case that can be accessible from the web. Check it out!
( * = Joint work with friends) * Crypko Next generation crypko-collectibles powered by deep generative models and consensus-based smart contracts from blockchain that attracts attention from both machine learning and blockchain communities. * MakeGirlsMoe High-quality Anime Characters Generation with Generative Adversarial Networks in Browser that attracts both community and academic attention. Deep GAN (Generative Adversarial Network) in Browser A deep GAN that generates (or dreams) images completely in browser . Pre-trained high resolution realistic image generators now run on web browser that supports WebGL, with the help of web-runnable TensorFlow.js model, Generation with Symantic Enforcement This is a Neural based five-syllable Chinese quatrain generator, faithfully following rhyme in even-numbered verses and strict patterning of tonal alternation. It is based on ideas from our Paper in ICLR 2018, RNN Language Model An PyTorch based Recurrent Neural Network Language Model. See demo for Generating Jin Yong's Text / 金庸文字生成器 and AKB-style Lyrics / 秋○康歌詞ジェネレーター . Timeline Magenta, Google Brain: Intern as a Researcher Summer 2018 Research on the latent space of generative models Facebook SearchNLP: Intern doing Software Engineering Summer 2017 Researches on Web Query Parsing and Natural Language Interface to Database System, both with Haixun Wang Google Brain: Intern as a Researcher Summer and Fall 2016 Research on representation learning with Stephan Gouws Google: Intern doing Software Engineering Summer 2015 Profiling and analysis for user-generated activity Stony Brook University: Computer Science PhD Student 2014 - Now Advised by Prof. Steven Skiena Microsoft Research Asia: Research Intern Feb. 2013 - Dec. 2013 Learning semantic vector representation from large knowledge base with Haixun Wang
Concept level semantic analysis Fudan University: B.Sc., Computer Science and Technology 2010 - 2014 Advised by Prof. Yanghua Xiao Publications / Working Papers A Transfer-Learnable Natural Language Interface for Databases
Wenlu Wang, Yingtao Tian , Hongyu Xiong, Haixun Wang, Wei-Shinn Ku
[paper]
Working Paper Learning to Represent Bilingual Dictionaries
Muhao Chen*, Yingtao Tian *, Haochen Chen, Kai-Wei Chang, Steven Skiena, Carlo Zaniolo (* Equal Contribution)
[paper]
Submitted to 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2019) Enhanced Network Embeddings via Exploiting Edge Attributes
Haochen Chen, Xiaofei Sun, Yingtao Tian , Muhao Chen, Bryan Perozzi and Steven Skiena
[paper]
In Proceedings of the International Conference on Information and Knowledge Management (CIKM) 2018 Social Relation Inference via Label Propagation
Yingtao Tian , Haochen Chen, Bryan Perozzi, Muhao Chen, Xiaofei Sun, Steven Skiena
[paper]
To appear in the 41st European Conference on Information Retrieval (ECIR 2019)
Accepted in 14th International Workshop on Mining and Learning with Graphs DeepAnnotator: Genome Annotation with Deep Learning
Mohammad Ruhul Amin, Alisa Yurovsky, Yingtao Tian , Steven Skiena
[paper]
In Proceedings of the 9th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB), 2018. Simple Neologism Based Domain Independent Models to Predict Year of Authorship
Vivek Kulkarni, Yingtao Tian , Parth Dandiwala, Steven Skiena
[paper]
In Proceedings of the 27th International Conference on Computational Linguistics (COLING), 2018. Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment
Muhao Chen, Yingtao Tian , Kai-Wei Chang, Steven Skiena, Carlo Zaniolo
[paper]
In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), 2018. Syntax-Directed Variational Autoencoder for Structured Data
Hanjun Dai*, Yingtao Tian * , Bo Dai, Steven Skiena, Le Song (* Equal Contribution)
[paper]
In Proceedings of the International Conference on Learning Representations (ICLR), 2018.
Best paper award, NIPS 2017 workshop for Machine Learning for Molecules and Materials. Embedding-based Relation Prediction for Ontology Population
Muhao Chen, Yingtao Tian, Xuelu Chen, Zijun Xue, Carlo Zaniolo
[paper]
In Proceedings of the 17th SIAM International Conference on Data Mining (SDM), SIAM 2018. Towards the Automatic Anime Characters Creation with Generative Adversarial Networks
Yanghua Jin, Jiakai Zhang, Minjun Li, Yingtao Tian, Huachun Zhu, Zhihao Fang
[paper] [web demo]
Accepted and presented in spotlight, NIPS 2017 workshop for Machine Learning for Creativity and Design. Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment
Muhao Chen, Yingtao Tian, Mohan Yang, Carlo Zaniolo
[paper] [code] [data]
In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), IJCAI/AAAI Press 2017. Patents Training and/or Utilizing Recurrent Neural Network Model to Determine Subsequent Source(s) for Electronic Resource Interaction (Patent in submission) 2017 Professional Services Reviewer LREC 2018, COLING 2017 Honors and Awards The Best Presentation Award, Graduate Research Conference, Computer Science Department, Stony Brook University, 2015 27th place, 35th Annual World Final of the ACM-ICPC, 2011 Gold Medal, ACM-ICPC Asia Chengdu Regional Contest, 2011 Championship and Gold medal, ACM-ICPC Asia Amritapri Regional Contest, 2010