Tong Wu


Senior Machine Learning Engineer
Factset Research Systems Inc.
Email: tong.wu.ee@rutgers.edu

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Bio

I am a Senior Machine Learning Engineer at Factset. Before joining Factset, I completed my Ph.D. in Electrical and Computer Engineering from Rutgers University, where I was fortunate to work with Prof. Waheed Bajwa. I obtained my M.S. degree in Electrical Engineering from Duke University in 2011, and B.E. degree in Instrument Science and Engineering from Shanghai Jiao Tong University in 2009. In the past, I held internships at AT&T Research Lab, and conducted visiting research at Army Research Lab. My research interests include machine learning, geometrical methods for big data analytics, and computer vision.

Education

Ph.D. in Electrical and Computer Engineering, 2012 - 2017
Rutgers, The State University of New Jersey, Piscataway, NJ
Advisor: Prof. Waheed U. Bajwa
Thesis: Learning the nonlinear geometric structure of high-dimensional data: Models, algorithms, and applications
M.S. in Electrical and Computer Engineering, 2009 - 2011
Duke University, Durham, NC
Advisors: Prof. Robert Calderbank and Prof. Ingrid Daubechies
Thesis: Stylistic analysis of paintings using wavelets and probabilistic topic models
B.E. in Instrument Science and Engineering, 2005 - 2009
Shanghai Jiao Tong University, Shanghai, China

Publications

Journal Papers

Online tensor low-rank representation for streaming data clustering
T. Wu
IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 2, pp. 602-617, Feb. 2023.
[abs] [pdf]
Graph regularized low-rank representation for submodule clustering
T. Wu
Pattern Recognition, vol. 100, 107145, Apr. 2020.
[abs] [pdf]
A low tensor-rank representation approach for clustering of imaging data
T. Wu and W.U. Bajwa
IEEE Signal Processing Letters, vol. 25, no. 8, pp. 1196-1200, Aug. 2018.
[abs] [pdf] [code]
Learning the nonlinear geometry of high-dimensional data: Models and algorithms
T. Wu and W.U. Bajwa
IEEE Transactions on Signal Processing, vol. 63, no. 23, pp. 6229-6244, Dec. 2015.
[abs] [pdf] [published version] [code]

Conference Papers

Online tensor low-rank representation for streaming data
T. Wu
In Proc. IEEE 30th Int. Workshop on Machine Learning for Signal Processing, Espoo, Finland, 2020. (MLSP 2020)
[abs] [pdf]
Clustering-aware structure-constrained low-rank submodule clustering
T. Wu
In Proc. 53rd Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 3-6, 2019, pp. 1852-1856. (Asilomar 2019)
[abs] [pdf]
Human action attribute learning using low-rank representations
T. Wu, P. Gurram, R.M. Rao and W.U. Bajwa
In Proc. Signal Processing with Adaptive Sparse Structured Representations workshop, Lisbon, Portugal, Jun. 5-8, 2017. (SPARS 2017)
[abs] [pdf]
Distributed learning of human mobility patterns from cellular network data
T. Wu, R.M. Rustamov and C. Goodall
In Proc. 51st Annu. Conf. on Information Sciences and Systems, Baltimore, MD, Mar. 22-24, 2017. (CISS 2017)
[abs] [pdf] [code]
Clustering-aware structure-constrained low-rank representation model for learning human action attributes
T. Wu, P. Gurram, R.M. Rao and W.U. Bajwa
In Proc. IEEE Image, Video, and Multidimensional Signal Processing Workshop, Bordeaux, France, Jul. 11-12, 2016. (IVMSP 2016) (Best Student Paper Award)
[abs] [pdf]
Hierarchical union-of-subspaces model for human activity summarization
T. Wu, P. Gurram, R.M. Rao and W.U. Bajwa
In Proc. IEEE Int. Conf. on Computer Vision Workshop, Santiago, Chile, 2015, pp. 1053-1061. (ICCVW 2015)
[abs] [pdf]
Active dictionary learning for image representation
T. Wu, A.D. Sarwate and W.U. Bajwa
In Proc. SPIE Unmanned Systems Technology XVII, vol. 9468, Baltimore, MD, Apr. 21-23, 2015. (SPIE 2015)
[abs] [pdf]
Metric-constrained kernel union of subspaces
T. Wu and W.U. Bajwa
In Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, Brisbane, Australia, Apr. 19-24, 2015, pp. 5778-5782. (ICASSP 2015)
[abs] [pdf]
Subspace detection in a kernel space: The missing data case
T. Wu and W.U. Bajwa
In Proc. IEEE Workshop on Statistical Signal Processing, Gold Coast, Australia, Jun. 29-Jul. 2, 2014, pp. 93-96. (SSP 2014)
[abs] [pdf]
Revisiting robustness of the union-of-subspaces model for data-adaptive learning of nonlinear signal models
T. Wu and W.U. Bajwa
In Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, Florence, Italy, May 4-9, 2014, pp. 3390-3394. (ICASSP 2014)
[abs] [pdf]
Painting analysis using wavelets and probabilistic topic models
T. Wu, G. Polatkan, D. Steel, W. Brown, I. Daubechies and R. Calderbank
In Proc. IEEE Int. Conf. on Image Processing, Melbourne, Australia, Sep. 15-18, 2013, pp. 3264-3268. (ICIP 2013)
[abs] [pdf]

Technical Reports

Human action attribute learning from video data using low-rank representations
T. Wu, P. Gurram, R.M. Rao and W.U. Bajwa
Technical Report 2020-07-001, Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ, Jul. 2020.
[abs] [pdf]