Zhe Gan

I am a Senior Researcher at Microsoft Cloud and AI, primarily working on generative models, vision plus NLP, natural language understanding and generation. I also have broad interests on various machine learning topics. I am serving as an Area Chair for NeurIPS 2019, and a Senior Program Committee (SPC) member for AAAI 2020. I can be reached at zhe.gan@microsoft.com.

I received my Ph.D. degree from Duke University in Spring 2018. Before that, I received my Master's and B.Sc. from Peking University in 2013 and 2010, respectively. My Ph.D. advisor is Lawrence Carin.

Research Highlights:

[2019/08] 4 papers got accepted to EMNLP. Topics include (i) BERT model compression, (ii) domain adaptation for MRC, (iii) domain adaptation for text style transfer, and (iv) image caption evaluation.

[2019/07] One paper got accepted to ICCV, using relation-aware graph attention for VQA.

[2019/06] Will serve as a Senior Program Committee (SPC) member for AAAI 2020.

[2019/05] One paper got accepted to ACL, obtaining state-of-the-art results in visual dialog.

[2019/02] 1 NAACL and 2 CVPR papers got accepted.

[2019/02] Will serve as an Area Chair for NeurIPS 2019.

[2019/01] One paper got accepted to ICLR 2019. It proposes the usage of optimal transport for improving Seq2Seq learning.

[2018/11] Our paper on visual story generation got accepted to AAAI 2019. The acceptance ratio this year is 1150/7095=16.2%.

[2018/09] Two papers got accepted to NIPS 2018. The first one proposes Adversarial Information Maximization (AIM) for conversational response generation. The second one proposes feature mover GAN for neural text generation. The acceptance ratio this year is 1011/4856=20.82%.

[2018/06] One paper got accepted to Machine Learning for Healthcare (MLHC) 2018. This new work learns to predict diagnoses from doctor notes using CNN models, which will be presented as a spotlight talk. The acceptance ratio is only 19.75%.

[2018/05] One paper got accepted to ICML 2018. JointGAN (this work) extends Triangle GAN (our NIPS 2017 paper) naturally, capable of learning a full joint distribution of multiple domains, and generating fake-fake paired samples.

[2018/02/20] PhD thesis defended.

[2018/02] One paper accepted to CVPR 2018. GAN plus attention results in our AttnGAN, generates realistic images on birds and COCO datasets. See the news coverage here: Drawing AI, among other places.

[2017/09] 4 papers got accepted to NIPS 2017; three of them are on deep generative models, including VAE and GAN variants; the other one is on deconvolutional paragraph representation learning. The acceptance ratio of NIPS 2017 is 678/3240= 20.93%.

[2017/05] I got two papers accepted to ICML this year. One proposes a new GAN model for text generation that handles the discrete nature of text inputs and uses adversarial feature matching, based on our NIPS workshop paper. The other proposes a new SG-MCMC algorithm. These are joint work with Yizhe.

[2017/03] Two papers on image captioning are accepted by CVPR 2017. One proposes semantic compositional network for visual captioning, and the other proposes StyleNet that can generate stylized captions.

[2016/12] Our NIPS workshop paper "Generating text via Adversarial Training" made some initial progress on training a generative adversarial network for generating realistic sentences.

[2016/09] The camera-ready version of our NIPS 2016 paper "Variational Autoencoder for Deep Learning of Images, Labels and Captions" is online now.