Shaobo Han

Researcher

NEC Labs America

Biography

Shaobo Han is a researcher at NEC Laboratories America, working on the design and development of machine learning and signal processing techniques for real-world sensing applications. He is interested in the adaptation of AI models to cope with limitations in data acquisition and distributional shifts 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.

News

[2022/09] Our paper Using Global Existing Fiber Networks for Environmental Sensing" has been accepted by Proceedings of the IEEE.
[2022/07] Received Outstanding Performance Award 2022, Global Innovation Unit, NEC Corporation.
[2022/06] I will serve as a reviewer for ICLR 2023.
[2022/01] Two papers accepted to ICLR 2022. Topics include (i) test-time policy adaptation under ordinal reward, and (ii) zero-shot domain adaptation with multiway categorical domain descriptor.
[2021/06] Two AI-based fiber sensing solutions have been commercialized: NEC’s press release.

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]