Yanning Shen

Assistant Professor

Department of EECS, University of California, Irvine

Office: Engineering Hall 4215

Email: yannings@uci.edu

Google Scholar

Research Profile

My research interests span the areas of machine learning, data science, network science, and statistical signal processing; along with their applications in social, brain, gene-regulatory, environmental, financial and IoT systems.

I am looking for self-motivated Ph.D. students and visitors. Please contact me if you are interested.

News

  • 08/2019: I am very excited to join the Department of EECS at University of California, Irvine as an Assistant Professor! New Start!!
  • 07/2019: New paper "Topology Identification of Directed Graphs via Joint Diagonalization of Correlation Matrices" was submitted to IEEE Transactions on Signal and Information Processing over Networks.
  • 06/2019: Attended and presented at IEEE Data Science Workshop.
  • 06/2019: New paper "Graph-based Learning under Perturbations via Total Least-Squares" was submitted to IEEE Trans. on Signal Processing.
  • 04/2019: New paper "Scalable Learning with Privacy over Graphs'' was accepted by IEEE Data Science Workshop
  • 02/2019: Our paper on online learning adaptive to unknown dynamics "Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics" appeared at Journal of Machine Learning Research (JMLR). [pdf]
  • 02/2019: New paper "Online Graph-Adaptive Learning with Scalability and Privacy" was accepted by IEEE Trans. on Signal Processing. [pdf]
  • 02/2019: New paper "Semi-Blind Inference of Topologies and Dynamical Processes over Graphs" was accepted for publication at IEEE Trans. on Signal Processing. [pdf]
  • 02/2019: I was invited to give an oral presentation at ITA 2019 Graduation Day.
  • 12/2018: Defended my doctoral thesis "Online scalable learning adaptive to unknown dynamics and graphs".
  • 11/2018: Our tutorial proposal on "Learning Nonlinear and Dynamic Connectivity and Processes over Graphs" was accepted by ICASSP 2019. I will be a co-presenter with Prof. G. B. Giannakis!
  • 10/2018: I co-presented a tutorial talk on "Resilient and Scalable Interactive Learning" at MILCOM October 28-31, 2018, Los Angeles, USA.
  • 10/2018: Attended the Asilomar Conference on Signals, Systems, and Computers.
  • 09/2018 New paper "Nonlinear Structural Vector Autoregressive Models for Inferring Effective Brain Network Connectivity" was submitted to IEEE Trans. on Signal Processing. [pdf]
  • 08/2018: New paper "Online Graph-Adaptive Learning with Scalability and Privacy" was submitted to IEEE Trans. on Signal Processing. [pdf]
  • 08/2018: New paper on adaptive learning for IoT "Heterogeneous Online Learning for "Thing-Adaptive" Low-Latency Fog Computing in IoT" was accepted for publication at IEEE Internet-of-Things Journal. [pdf]
  • 06/2018: New paper "Topology Identification and Learning over Graphs: Accounting for Nonlinearities and Dynamics" was accepted for publication by Proceedings of the IEEE. [pdf]
  • 06/2018: New paper "Canonical Correlation Analysis of Datasets With a Common Source Graph" was accepted by IEEE Trans. on Signal Processing.
  • 05/2018: I was awarded the Doctoral Dissertation Fellowship of UMN for 2018-2019.
  • 04/2018: Attended Intl. Conf. on Acoust., Speech, and Sig. Process. in Calgary, Canada
  • 04/2018: Our paper on interactive learning in nonstationary environments was presented at AISTATS 2018.[pdf]
  • 12/2017: Our paper was in the finalist for best student paper award for CAMSAP 2017.
  • 11/2017: I was invited to attend and present at Rising Stars in EECS Workshop at Stanford University.
  • 10/2017: Our paper was in the finalist for best student paper award for Asilomar 2017.

Selected Publications (See Publications tab for the full list)

Summary: 15 accepted journal papers, 4 submitted journal papers, and 25 published conference papers.

Y. Shen, Geert Leus and G. B. Giannakis, “Online Graph-Adaptive Learning with Scalability and Privacy,” IEEE Transactions on Signal Processing, to appear 2019. [pdf]

Y. Shen, T. Chen, and G. B. Giannakis, "Random Feature-based Online Multi-kernel Learning in Non-stationary and Adversarial Environments," Journal of Machine Learning Research (JMLR), February 2019. [pdf]

G. B. Giannakis, Y. Shen, and G. V. Karanikolas, "Topology Identification and Learning over Graphs: Accounting for Nonlinearities and Dynamics," Proceedings of the IEEE, May 2018. [pdf]

Y. Shen, M. Mardani, and G. B. Giannakis, "Online Categorical Subspace Learning for Sketching Big Data with Misses," IEEE Transactions on Signal Processing, August 2017. [pdf]

Y. Shen, B. Baingana, and G. B. Giannakis, "Tensor Decompositions for Identifying Directed Graph Topologies and Tracking Dynamic Networks," IEEE Transactions on Signal Processing, July 2017.[pdf]

Y. Shen, B. Baingana, and G. B. Giannakis, "Kernel-based structural equation models for topology identification of directed networks," IEEE Trans. on Sig. Processing, May 2017.[pdf]

J. Fang, Y. Shen, H. Li and Pu Wang, ''Pattern-coupled sparse Bayesian learning for recovery of block-sparse signals,'' IEEE Transactions on Signal Processing, vol. 63, no. 2, pp. 360-372, January 2015. [pdf] (First student author)

Y. Shen, J. Fang, and H. Li, "Exact reconstruction analysis of log-sum minimization for compressed sensing," IEEE Signal Processing Letters, vol.20, no.12, pp.1223-1226, December 2013. [pdf]

Y. Shen , T. Chen and G. B. Giannakis, “Online Ensemble Multi-kernel Learning Adaptive to Non-stationary and Adversarial Environments,” Proc. of Intl. Conf. on Artificial Intelligence and Statistics (AISTATS), April, 2018.

Y. Shen, P. A. Traganitis, and G. B. Giannakis, "Nonlinear Dimensionality Reduction on Graphs," Proc. of CAMSAP Conf., Curacao, Dutch Antilles, Dec. 10-13, 2017. (Best Student Paper Finalist)

T. Chen Y. Shen, Q. Ling, and G. B. Giannakis, "Online Learning for "Thing-Adaptive" Fog Computing in IoT," Proc. of Asilomar Conf., Pacific Grove, CA, Oct. 29 - Nov. 1, 2017. (Best Student Paper Finalist)

Selected Awards (See Awards tab for the full list)

2018 Doctoral Dissertation Fellowship, UMN

2017 Rising Stars in EECS, Stanford University

2017 Best Student Paper Finalist, CAMSAP Conference

2017 Best Student Paper Finalist, Asilomar Conference

2014 Outstanding Master's Thesis Award of UESTC (top 1%)

2013 Outstanding graduate Student of UESTC (top 2%)

2012 Outstanding graduate Student of UESTC (top 2%)

2011 Outstanding Bachelor's Thesis Award of UESTC (top 1%)

2009 Second-Class Award in National English Contest

2006 First-Class Award in National Physics Olympic Competition