Shaobo Han

Shaobo Han

Senior Researcher

NEC Labs America

Biography

Shaobo Han is a Senior Researcher at NEC Laboratories America, working on the design and development of machine learning and signal processing algorithmic solutions for real-world sensing applications. He is interested in generalizable AI methods that can cope with limitations in data acquisition and domain 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/deep generative models and structured variational inference,
  • Multitask learning and cross-domain generalization,
  • Bayesian statistics, sparsity, and latent variable models,
  • Signal processing and sensing applications.

News

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]
    Test-time adaptation, extrapolation from one-class
  • 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]
    Zero-shot domain adaptation, compositional generalization
  • 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]

Professional Experience

 
 
 
 
 
NEC Laboratories America
Senior Researcher
Jun 2023 – Present Princeton, NJ
 
 
 
 
 
NEC Laboratories America
Researcher
Jun 2019 – Jun 2023 Princeton, NJ
 
 
 
 
 
Duke Department of Statistical Science
Postdoctoral Associate
Oct 2016 – May 2019 Durham, NC
 
 
 
 
 
IBM T.J. Watson Research Center
Research Intern
Jun 2014 – Aug 2014 Yorktown Heights, NY

Professional Activities

Conference Program Committee Member

  • Neural Information Processing Systems (NeurIPS)
  • International Conference on Machine Learning (ICML)
  • International Conference on Learning Representations (ICLR)
  • International Conference on Artificial Intelligence and Statistics (AISTATS)
  • International Joint Conference on Artificial Intelligence (IJCAI)
  • AAAI Conference on Artificial Intelligence (AAAI)
  • Conference on Uncertainty in Artificial Intelligence (UAI)

Journal Reviewer

  • Journal of Machine Learning Research (JMLR)
  • Journal of the American Statistical Association (JASA)
  • Canadian Journal of Statistics (CJS)
  • IEEE Transactions on Signal Processing (TSP)
  • IEEE Transactions on Image Processing (TIP)
  • IEEE Transactions on Knowledge and Data Engineering (TKDE)
  • SIAM Journal on Imaging Sciences (SIIMS)
  • Optics Express (Optica)
  • Journal of Artificial Intelligence Research (JAIR)
  • Electronic Journal of Statistics (EJS)
  • Statistics and Computing (Springer)