Yizhe Zhang


Ph.D student (since Fall 2013)

on Machine Learning.

Advisor: Lawrence Carin

M.S. student (since Fall 2015)

on Statistical Science.

Advisors: David Dunson, Scott Schmidler and Katherine Heller

Duke University


yz196 (at) duke.edu

Linkedin Profile

information initiative at Duke (iiD), Gross Hall,

140 Science Dr.

Durham, NC 27708, United States.

Research interest

I am a final year Ph.D. student, working on deep generative models and Bayesian machine learning. I have particular interests in language modeling and spatio-temporal modeling. Other than this, I have broad interests in Markov Chain Monte Carlo (MCMC).

My interdisciplinary training in Machine learning, Physics and Biology encourages me to think differently. Recently, I am attempting to bring methodologies from Physics for solving sampling problems, and applying latest machine learning methodologies to unravel insights for large dataset.

I am currently looking for natural language modeling/general machine learning research scientist position.

In preparation

Deconvolutional sequence-level conversation training with externel embedding

Neural conversation model with consistent and diverse response with reinforcement learning and adversarial training


Recurrent generative adversarial nets

Generating image from scratch with recurrent GAN.

Conference and workshop publications

Deconvolutional Latent-Variable Model for Text Sequence Matching

Dinghan Shen, Yizhe Zhang, Ricardo Henao, Qinliang Su, Lawrence Carin.  —  AAAI 2018

A Flexible Probabilistic Framework for Learning to Predict Unseen Classes

Wenlin Wang, Piyush Rai, Yunchen Pu, Kai Fan, Yizhe Zhang, Ricardo Henao, Lawrence Carin.  —  AAAI 2018

Deconvolutional Paragraph Representation Learning [supplements] [code] [data]

Yizhe Zhang, Dinghan Shen, Guoyin Wang, Ricardo Henao, Zhe Gan, Lawrence Carin  —  NIPS 2017.

Triangle Generative Adversarial Networks

Zhe Gan, Liqun Chen, Weiyao Wang, Yunchen Pu, Yizhe Zhang, Lawrence Carin  —  NIPS 2017.

Stochastic Gradient Monomial Gamma Sampler [supplements]

Yizhe Zhang, Changyou Chen, Zhe Gan, Ricardo Henao, Lawrence Carin.  —  ICML 2017.

Adversarial Feature Matching for Text Generation [supplements] [code] [param] [data]

Yizhe Zhang, Zhe Gan, Kai Fan, Zhi Chen, Ricardo Henao, Lawrence Carin.  —  ICML 2017.

Towards Unifying Hamiltonian Monte Carlo and Slice Sampling [supplements]

Yizhe Zhang, Xiangyu Wang, Changyou Chen, Lawrence Carin.  —  NIPS 2016.

Distributed Bayesian Learning with Stochastic Gradient MCMC.

Changyou Chen, Nan Ding, Chunyuan Li, Yizhe Zhang, Lawrence Carin.  —  NIPS 2016.

Generating Text via Adversarial Training.

Yizhe Zhang, Zhe Gan, Lawrence Carin.  —  Workshop on Adversarial Training, NIPS, 2016.

Learning a Hybrid Architecture for Sequence Regression and Annotation. [supplements]

Yizhe Zhang, Ricardo Henao, Jianling Zhong, Lawrence Carin, Alexander Hartemink  —  AAAI 2016.

Bayesian Dictionary Learning with Gaussian Processes and Sigmoid Belief Networks.

Yizhe Zhang, Ricardo Henao, Chunyuan Li, Lawrence Carin.  —  IJCAI 2016.

Triply Stochastic Variational Inference for Non-linear Beta Process Factor Analysis.

Kai Fan, Yizhe Zhang, Lawrence Carin, Katherine Heller.  —  ICDM 2016.

Dynamic Poisson Factor Analysis

Yizhe Zhang, Ricardo Henao, Lawrence Carin.  —  ICDM 2016

Laplacian Hamiltonian Monte Carlo

Yizhe Zhang, Changyou Chen, Ricardo Henao, Lawrence Carin  —  ECML 2016.

Learning Dictionary with Spatial and Inter-dictionary Dependency.

Yizhe Zhang, Ricardo Henao, Chunyuan Li, Lawrence Carin.  —  Workshop on representation learning, NIPS, 2015.

Journal publications

MOST+: a Motif Finding Approach Combining Genomic Sequence and Heterogeneous Genome-wide Signatures. [Source code in C++]

Yizhe Zhang, Yupeng He and Chaochun Wei. BMC Genomics, 2015.

CRF-based Transcription Factor Binding Site Finding System. [Source code in C++]

Yupeng He, Yizhe Zhang, Guangyong Zheng and Chaochun Wei. BMC Genomics, 2012.

Composition-based Classification of Short Metagenomic Sequences Elucidates the Landscapes of Taxonomic and Functional Enrichment of Microorganisms.

Jiemeng Liu, Haifeng Wang, Hongxing Yang, Yizhe Zhang, Jinfeng Wang, Fangqing Zhao and Ji Qi. Nucleic Acids Research, 2012.

Other projects

Learning Infinite Mixture of Directed Acyclic Graphs

Understanding Regulatory Element Topic via Relational Topic Modeling


Teaching experiences

  • STA 561 Probabilistic Machine Learning

  • STA 571 Advanced Machine Learning

Professional services.



  • I am going to Microsoft Research for summer internship in 2017, working on chatbot system.

  • Teaching assistant for probabilistic machine learning CS571. Aug. 2016

  • Summer internship at NEC lab, working on sparse feature learning on deep models, Princeton, NJ. May. 2016

  • Joining a Probabilistic Programing (PP) project designed to perform inference for probabilistic models. (In Julia) Jan. 2016

  • Teaching assistant for advanced machine learning STA571 Jan. 2016