I am a PhD student in the
Department of Computer Science
at Stony Brook University advised by
Prof. Steven Skiena.
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.
My CV is also available.
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
.
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
Learning to Represent Bilingual Dictionaries
Muhao Chen*, Yingtao Tian *, Haochen Chen, Kai-Wei Chang, Steven Skiena, Carlo Zaniolo (* Equal Contribution)
Submitted to Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)
A Transfer-Learnable Natural Language Interface for Databases
Wenlu Wang, Yingtao Tian , Hongyu Xiong, Haixun Wang, Wei-Shinn Ku
Submitted to Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)
Enhanced Network Embeddings via Exploiting Edge Attributes
Haochen Chen, Xiaofei Sun, Yingtao Tian , Muhao Chen, Bryan Perozzi and Steven Skiena
To appear in 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
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
To Appear in 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
To Appear in 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
To Appear in 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)
To Appear in 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
To Appear in 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.
Training and/or Utilizing Recurrent Neural Network Model to Determine Subsequent Source(s) for Electronic Resource Interaction
(Patent in submission)
2017
Reviewer
LREC 2018, COLING 2017
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