Bio:
Jo-Anne Ting graduated from the University of Waterloo, Canada in June 2003 with a Bachelor of Applied Sciences in Computer Engineering, Honors Co-op. She joined Stefan Schaal's Computational Learning and Motor Control Lab in the fall of 2003. In May 2005, she received a M.S. in Computer Science from the University of Southern California. She is currently working towards her Ph.D.

Her research interests include topics in statistical and machine learning, with a focus on real-world problems in robotics and neuroscience. She is particularly interested in developing fast robust automatic Bayesian methods for real-time learning on high-dimensional systems.

News:

• Our paper on Bayesian kernel shaping has been accepted at NIPS (9/2008).
• I am one of the co-organizers for the 2008 Women in Machine Learning workshop, along with Anna Koop, Luiza Antonie and our faculty advisor Joelle Pineau. The submission deadline for student abstracts is October 10, 2008.

Office:
Ronald Tutor Hall, Room 417
3710 S. McClintock Ave
Los Angeles, CA 90089-2900

Email:

Phone:
(213) 740 6717

Fax:
(213) 740 1510

Links:

• CV
  
• Thesis proposal (proposed on November 27, 2007)
  
• Publications
  
• Talks
  
• Software
  
• Personal

Publications:

Spatially Local Adaptive Kernels:

• Ting, J., Kalakrishnan, M., Vijayakumar, S., Schaal, S. (2008). Bayesian Kernel Shaping for Learning Control, Advances in Neural Information Processing Systems (NIPS 2008), to appear.

• Ting, J., D'Souza, A., Vijayakumar, S., Schaal, S. (2008). A Bayesian Approach to Empirical Local Linearization for Robotics, International Conference on Robotics and Automation (ICRA 2008), Pasadena, CA.
  [Details] [PDF]

• Ting, J., Schaal, S. (2008). Local Kernel Shaping for Function Approximation, Learning Workshop, Snowbird, April 2008, Poster.

• Ting, J., Schaal, S. (2007). Bayesian Nonparametric Regression with Local Models, Robotic Challenges for Machine Learning, NIPS 2007 Workshop, Poster.

Real-time Automatic Outlier Detection:

• Ting, J., Theodorou, E., Schaal, S. (2007). Learning an Outlier-Robust Kalman Filter, European Conference on Machine Learning (ECML 2007), Warsaw, Poland.
  [Details] [PDF] [Technical Report] (Please refer to the technical report for a more comprehensive version.)

• Ting, J., Theodorou, E., Schaal, S. (2007). A Kalman filter for robust outlier detection, IEEE International Conference on Intelligent Robotics Systems (IROS 2007), San Diego, CA.
  [Details] [PDF] [Slides]

• Ting, J., D'Souza, A., Schaal, S. (2007). Automatic Outlier Detection: A Bayesian Approach, International Conference on Robotics and Automation (ICRA 2007), Rome, Italy.
  [Details] [PDF] [Slides]

Parameter Identification in High-dimensional Regression:

• Ting, J., Mistry, M., Peters, J., Schaal, S.; Nakanishi, J. (2006). A Bayesian Approach to Nonlinear Parameter Identification for Rigid Body Dynamics, Robotics: Science and Systems (RSS 2006), Philadelphia, PA.
  [Details] [PDF]

• Ting, J., D'Souza, A., Schaal, S. (2006). Bayesian Regression with Input Noise for High Dimensional Data, International Conference on Machine Learning (ICML 2006), Pittsburgh, PA.
  [Details] [PDF] [Slides]

Variational Bayesian Least Squares (High-dimensional Linear Regression):

• Ting, J.;D'Souza, A.;Yamamoto, K.;Yoshioka, T.;Hoffman, D.;Kakei, S.;Sergio, L.;Kalaska, J.;Kawato, M.;Strick, P.;Schaal, S. (2008). Variational Bayesian least squares: An application to brain-machine interface data, Neural Networks: Special Issue on Neuroinformatics (accepted: 6/17/2008).

• Ting, J., D'Souza, A., Yamamoto, K., Yoshioka, T., Hoffman, D., Kakei, S., Sergio, L., Kalaska, J., Kawato, M., Strick, P., Schaal, S. (2007). Using variational Bayesian least squares for EMG data prediction from M1 and premotor cortex neural firing, Abstracts of the 37th Meeting of the Society of Neuroscience (SFN 2007), San Diego, CA.
  [Details] [Poster]

• Ting, J., D'Souza, A., Yamamoto, K., Yoshioka, T., Hoffman, D., Kakei, S., Sergio, L., Kalaska, J., Kawato, M., Strick, P., Schaal, S. (2005). Predicting EMG Data from M1 Neurons with Variational Bayesian Least Squares, in: Weiss, Y.; Schölkopf, B.; Platt, J. (eds.), Advances in Neural Information Processing Systems 18 (NIPS 2005), Cambridge, MA: MIT Press.
  [Details] [PDF]

• Ting, J., D'Souza, A., Schaal, S. (2004). Predicting EMG Activity from Neural Firing in M1 with Bayesian Backfitting, Proceedings of the 11th Joint Symposium of Neural Computation (JSNC 2004).
  [Details] [Poster]

Talks:

• Towards Automatic Bayesian Learning Methods, Ph.D. Dissertation proposal, University of Southern California, November 27, 2007.
  [Thesis proposal] [Slides]

• Automatic Bayesian Learning Methods, Grace Hopper Conference: Phd Forum, Orlando, October 19, 2007.
  [Slides]

• Bayesian Nonparametric Regression with Local Models, Machine Learning: Theory, Applications, Experiences, A Workshop for Women in Machine Learning, Orlando, October 17, 2007.
  [Slides]

• Learning an Outlier-Robust Kalman Filter, International Workshop on Knowledge Discovery from Ubiquitous Data Streams, Warsaw, Poland, September 17, 2007.
  [Slides]

• Towards Bayesian Black Box Learning Systems, University of Toronto Machine Learning Seminar, November 2006.
  

• Towards Bayesian Black Box Learning Systems, Machine Learning: Theory, Applications, Experiences, A Workshop for Women in Machine Learning, San Diego, October 4, 2006.
  [Abstract] [Slides]

• What Graduate School is all about: Goals and survival skills, Graduate Cohort 2006 Workshop, CRA-W, San Francisco, March 31, 2006 (with Anne Condon, UBC).
  [Slides]

Software:

• Variational Bayesian Least Squares
• Parameter Identification in Noisy High-dimensional Regression (updated 6/26/2008)
• Bayesian Parameter identification of Rigid Body Dynamic Models
• A Kalman filter for real-time automatic outlier detection
• Automatic outlier detection in linear regression