Shaobo Han

Researcher

NEC Labs America

Biography

Shaobo Han is a researcher at NEC Laboratories America and part of the Optical Networking & Sensing team, working on the design and development of machine learning and signal processing techniques for real-world sensing applications. He is interested in adaptation of AI models to cope with limitations in data acquisition and variabilities in deployment environments. He received Ph.D. in Electrical and Computer Engineering and M.S. in Statistical Science from Duke University. His Ph.D. advisor was Lawrence Carin.

His research interest includes:

Probabilistic models and variational inference,
Transfer learning and domain adaptation,
Bayesian statistics,
Signal processing and sensing applications.

Selected Publications

Learning Transferable Reward for Query Object Localization with Policy Adaptation,
Tingfeng Li, Shaobo Han, Martin Renqiang Min and Dimitris N. Metaxas.
International Conference on Learning Representations (ICLR 2022),
[PDF] [OpenReview] [GitHub]
Provable Adaptation across Multiway Domains via Representation Learning,
Zhili Feng, Shaobo Han and Simon S. Du.
International Conference on Learning Representations (ICLR 2022),
[PDF] [OpenReview]
Automatic Fine-grained Localization of Utility Pole Landmarks on Distributed Acoustic Sensing Traces based on Bilinear ResNets,
You Lu, Yue Tian, Shaobo Han, Eric Cosatto, Sarper Ozharar, and Yangmin Ding.
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2021),
[Link]
VAE Learning via Stein Variational Gradient Descent,
Yunchen Pu, Zhe Gan, Ricardo Henao, Chunyuan Li, Shaobo Han and Lawrence Carin.
Neural Information Processing Systems (NIPS 2017),
[PDF] [Link]
Variational Gaussian Copula Inference,
Shaobo Han, Xuejun Liao, David B. Dunson, and Lawrence Carin.
International Conference on Artificial Intelligence and Statistics (AISTATS 2016),
[PDF] [Supplementary] [GitHub]
Alternating Minimization Algorithm with Automatic Relevance Determination for Transmission Tomography under Poisson Noise,
Yan Kaganovsky, Shaobo Han, Soysal Degirmenci, David G. Politte, David J. Brady, Joseph A. O'Sullivan, and Lawrence Carin.
SIAM Journal on Imaging Sciences (SIIMS), Vol. 8, No.3 (2015) 2087-2132
[PDF] [GitHub]
Dynamic Rank Factor Model for Text Streams,
Shaobo Han, Lin Du, Esther Salazar, and Lawrence Carin.
Neural Information Processing Systems (NIPS 2014),
[PDF] [Supplementary] [Link]
Hierarchical Infinite Divisibility for Multiscale Shrinkage,
Xin Yuan, Vinayak Rao, Shaobo Han, and Lawrence Carin.
IEEE Transactions on Signal Processing (IEEE-TSP), Vol. 62, No.17 (2014) 4363-4374
[PDF] [Supplementary] [Code]
Integrated Non-Factorized Variational Inference,
Shaobo Han, Xuejun Liao, and Lawrence Carin.
Neural Information Processing Systems (NIPS 2013),
[PDF] [Supplementary] [Link]
Cross-Domain Multitask Learning with Latent Probit Models,
Shaobo Han, Xuejun Liao, and Lawrence Carin.
International Conference on Machine Learning (ICML 2012),
[PDF]