Tian Han

Assistant Professor

School: School of Engineering and Science

Department: Computer Science

Building: Gateway Center

Room: S248

Email: than6@stevens.edu

Website

Education
  • PhD (2019) University of California, Los Angeles (Statistics)
  • Other (2013) The Hong Kong University of Science and Technology (Computer Science)
Research

Unsupervised/Semi-supervised Learning, Probabilistic Generative Modeling, Explainable AI, Computer Vision.

Selected Publications
Conference Proceeding
  1. Pang, B.; Han, T.; Nijkamp, E.; Zhu, S.; Wu, Y. (2020). Learning Latent Space Energy-Based Prior Model. Advances in Neural Information Processing Systems (NeurIPS 2020).
  2. Nijkamp, E.; Pang, B.; Han, T.; Zhou, L.; Zhu, S.; Wu, Y. (2020). Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference. 16th European Conference on Computer Vision, ECCV 2020.
  3. Han, T.; Nijkamp, E.; Zhou, L.; Pang, B.; Zhu, S.; Wu, Y. (2020). Joint Training of Variational Auto-Encoder and Latent Energy-Based Model (pp. 7978--7987). Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020.
  4. Nijkamp, E.; Hill, M.; Han, T.; Zhu, S.; Wu, Y. (2020). On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models (pp. 5272--5280). The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020.
  5. Han, T.; Nijkamp, E.; Fang, X.; Hill, M.; Zhu, S.; Wu, Y. (2019). Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inferential Model. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019.
  6. Han, T.; Lu, Y.; Zhu, S.; Wu, Y. (2017). Alternating Back-Propagation for Generator Network. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17).
Conference Workshop Contribution
  1. Han, T.; Zhang, J.; Wu, Y. (2020). From em-projection to Variational Auto-Encoder. NeurIPS 2020 Workshop on Deep Learning through Information Geometry.
Journal Article
  1. Xing, X.; Gao, R.; Han, T.; Zhu, S.; Wu, Y. (2020). Deformable Generator Networks: Unsupervised Disentanglement of Appearance and Geometry. Transactions on Pattern Analysis and Machine Intelligence (PAMI). IEEE.
Courses

[CS559-B]: Machine Learning: fundamentals and applications --Fall 19, Spring 20, Fall 20, Fall 21
[CS515-A]: Fundamental of Computing --Spring 21