About Me

I am a PhD student in the Department of Statistics at UCLA, advised by Prof. Ying Nian Wu.

My research interests lie in the general area of machine learning, particularly in generative modeling, representational learning, and unsupervised learning, and their applications in computer vision, natural language modeling.

Selected Publications (Google Scholar)

Learning Latent Space Energy-Based Prior Model
Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, and Ying Nian Wu.
The 34th Conference on Neural Information Processing Systems. NeurIPS 2020.

Generative Text Modeling through Short Run Inference
Bo Pang, Erik Nijkamp, Tian Han, and Ying Nian Wu.
The 16th Conference of the European Chapter of the Association for Computational Linguistics. EACL 2021.

Learning Multi-Layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference
Erik Nijkamp*, Bo Pang*, Linqi Zhou, Tian Han, Song-Chun Zhu, and Ying Nian Wu.
The 16th European Conference on Computer Vision. ECCV 2020.

Towards Holistic and Automatic Evaluation of Open-Domain Dialogue Generation
Bo Pang, Erik Nijkamp, Wenjuan Han, Linqi Zhou, Yixian Liu, and Kewei Tu.
The 58th Annual Meeting of the Association for Computational Linguistics ACL 2020.

Joint Training of Variational Auto-Encoder and Latent Energy-Based Model
Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, and Ying Nian Wu.
The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020. CVPR 2020.

Learning Latent Space Energy-Based Prior Model for Molecule Generation
Bo Pang, Tian Han, and Ying Nian Wu.
NeurIPS Workshop 2020.

Semi-supervised Learning by Latent Space Energy-Based Model of Symbol-Vector Coupling
Bo Pang, Erik Nijkamp, Jian Cui, Tian Han, and Ying Nian Wu.
NeurIPS Workshop 2020.