About Me


I am a Postdoctoral Research Associate at Computer Science & Artificial Intelligence Lab of Massachusetts Institute of Technology, working with Prof. Dina Katabi and Prof. Tommi Jaakkola. I obtained Ph.D degree in CSE department, Hong Kong University of Science and Technology. My supervisor was Prof. Dit-Yan Yeung. I was a visiting scholar working with Prof. Eric Xing's group in Machine Learning department of Carnegie Mellon University. I am also a Microsoft Fellow and received the Baidu Research Fellowship. Before my Ph.D, I got my BS degree from Shanghai Jiao Tong University, 2013 under the supervision of Prof. Wu-Jun Li.

Email: hoguewang AT gmail.com / hwang87 AT mit.edu

Research Interest


My research interest focuses on statistical machine learning and data mining. Currently, I mainly work on collaborative filtering, deep learning, probabilistic graphical models, and their applications in healthcare, recommender systems, text mining, and social network analysis.



  • A new project page for an ongoing survey on Bayesian Deep Learning is set up (08/30/19).

  • A new project page for our NPN paper is set up with both Matlab and PyTorch code (06/30/19).

  • We are organizaing the ICML 2019 Workshop on "Learning and Reasoning with Graph-Structured Representations" (15/03/19).

  • We are organizaing the CVPR 2019 Workshop on "Towards Causal, Explainable and Universal Medical Visual Diagnosis" (03/11/19).

  • Our paper, "Rethinking knowledge graph propagation for zero-shot learning", is accepted at CVPR (02/24/19).

  • Our work on Deep Bayesian Networks is reported by MIT News (01/25/19).

  • Our paper, "ProbGAN: Towards probabilistic GAN with theoretical guarantees", is accepted at ICLR (12/22/18).

  • Two of our papers, "Bidirectional inference networks: A class of deep Bayesian networks for health profiling" and "Recurrent Poisson process unit for speech recognition", are accepted at AAAI (11/01/18).

  • Our paper, "Deep learning for precipitation nowcasting: A benchmark and a new model", is accepted at NIPS (09/05/17).

  • A new project page for our CDL paper is set up with a brief list of CDL variants (06/12/17).

  • Our paper, "Relational deep learning: A deep latent variable model for link prediction", is accepted at AAAI (11/11/16).

  • Our survey/review paper on Bayesian deep learning, "Towards Bayesian deep learning: a framework and some existing methods", is accepted in TKDE (08/22/16).

  • Two of our papers, "Natural parameter networks: a class of probabilistic neural networks" and "Collaborative recurrent autoencoder: recommend while learning to fill in the blanks", are accepted at NIPS (08/15/16).

  • Give the talk "Bayesian deep learning for integrated intelligence: bridging the gap between perception and inference" at the Chinese University of Hong Kong (06/17/16). [slides]

  • Give the talk "Bayesian deep learning for integrated intelligence: bridging the gap between perception and inference" at the Baidu NLP Seminar (06/13/16). [slides]

  • We gave a talk about Bayesian Deep Learning at ACML (11/22/15). [slides]

  • Give a talk about Bayesian Deep Learning at MSRA (09/11/15) and Baidu (11/05/15). [slides]

  • Our paper "Convolutional LSTM network: A machine learning approach for precipitation nowcasting" is accepted at NIPS. (09/04/15) [pdf]

  • Our paper "Collaborative deep learning for recommender systems" is accepted at SIGKDD. (05/13/15) [pdf]

  • Give a talk about " Relational Stacked Denoising Autoencoder for Tag Recommendation" at HKUST-EPFL Workshop on Data Science. (12/02/14) [slides]

  • This homepage is set up. (11/18/14)

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