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.

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," accepted for publication in IEEE Trans. Signal Processing, 2015. [PDF]
  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