Tong Wu
Graduate student
Department of Electrical and Computer Engineering
Rutgers, The State University of New Jersey
Piscataway, NJ, 08854
Phone: 848-445-8554
Email: tong.wu.ee@rutgers.edu
About me
I am a 4th-year Ph.D. student at Rutgers University, working with Prof. Waheed Bajwa. Before coming to Rutgers, I obtained my M.S. degree in Electrical Engineering from Duke University in 2011. My advisors were Prof. Robert Calderbank and Prof. Ingrid Daubechies. I received my B.S. degree in Instrument Science and Engineering from Shanghai Jiao Tong University in 2009. More information about me can be found at my Linkedin page.
Research Interests
- High-dimensional data analysis
- Machine learning
- Statistical signal processing
- Active learning
Publications
- T. Wu and W. U. Bajwa, "Learning the nonlinear geometry of high-dimensional data: Models and algorithms," IEEE Trans. Signal Processing, vol. 63, no. 23, pp. 6229-6244, Dec. 2015. [PDF] (published version here)
- T. Wu, A. D. Sarwate and W. U. Bajwa, "Active dictionary learning for image representation," in Proc. SPIE Unmanned Systems Technology XVII, vol. 9468, Baltimore, MD, Apr. 21-23, 2015. [PDF]
- T. Wu and W. U. Bajwa, "Metric-constrained kernel union of subspaces," in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Brisbane, Australia, Apr. 19-24, 2015, pp. 5778-5782. [PDF]
- T. Wu and W. U. Bajwa, "Subspace detection in a kernel space: The missing data case," in Proc. IEEE Workshop on Statistical Signal Processing (SSP), Gold Coast, Australia, Jun. 29-Jul. 2, 2014, pp. 93-96. [PDF]
- T. Wu and W. U. Bajwa, "Revisiting robustness of the union-of-subspaces model for data-adaptive learning of nonlinear signal models," in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Florence, Italy, May 4-9, 2014, pp. 3390-3394. [PDF]
- T. Wu, G. Polatkan, D. Steel, W. Brown, I. Daubechies and R. Calderbank, "Painting analysis using wavelets and probabilistic topic models," in Proc. IEEE Int. Conf. on Image Processing (ICIP), Melbourne, Australia, Sep. 15-18, 2013, pp. 3264-3268. [PDF]
Selected Graduate Courses
Rutgers University
- Numerical Analysis
- Mathematical Analysis
- Stochastic Models in Operations Research
- Nonlinear Optimization
- Linear Algebra and Applications
- Digital Signals and Filters
Duke University
- Machine Learning
- Sensing Theory
- Information Theory
- Signal Detection and Extraction Theory
- Random Signals and Noise
Teaching
- Digital Signal Processing, Spring 2014
- Digital Logic Design, Fall 2012