Guest Speaker Series: Robust Data Analytics Under Uncertainty

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Our Guest Speaker Series this month kicks off with a talk from Sheng Li about Robust Representations for Data Analytics under Uncertainty.


High-dimensional data are ubiquitous in real-world applications, arising in images, videos, documents, online transactions, biomedical measurements, etc. Although data analytics in high-dimensional space is generally intractable due to the “curse of dimensionality”, significant progress has been made by exploiting the low-dimensional manifolds in high-dimensional data. Extracting effective and compact feature representations from high-dimensional data becomes a critical problem in data science and machine learning.

Traditional data analytics methods, especially the statistical models, often make strong assumptions on the data distributions. However, real-world data might be contaminated by noise, or captured from multiple views. Such uncertainty would hinder the performance of data analytics. In this talk, I will describe some examples of my work in advancing the robust data analytics under uncertainty, including: 1) low-rank and sparse modeling for robust graph construction and subspace discovery; 2) an efficient bilinear projection approach for multi-view time series classification; and 3) applications on outlier detection, visual intelligence, and knowledge transfer. I will conclude this talk by describing my future research plans in the interdisciplinary field of data science.


Sheng Li is a Ph.D. candidate at the Northeastern University, Boston, MA. He has broad interests in data science and machine learning, including low-rank matrix recovery, multi-view learning, time series modeling, outlier detection, visual intelligence, and causal inference. He has published 40 papers at leading conferences and journals including IJCAI, KDD, SIGIR, CIKM, ICDM, SDM, ICCV, IEEE Trans. KDE, IEEE Trans. NNLS, IEEE Trans. IP, and IEEE Trans. CSVT. He received the best paper awards (or nominations) at SDM 2014, ICME 2014, and IEEE FG 2013. He co-presented two tutorials in IJCAI 2016 and CVPR 2016. He is the recipient of the 2015 NEU’s Outstanding Graduate Student Research Award. He has served on program committees for several major conferences such as IJCAI, AAAI, PAKDD, FG and DSAA.

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