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


  1. 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)
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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


  • Digital Signal Processing, Spring 2014
  • Digital Logic Design, Fall 2012