Jo-Anne Ting, NSERC Postdoctoral Fellow, UBC

Jo-Anne Ting
NSERC Postdoctoral Fellow, University of British Columbia

PhD, Computer Science, University of Southern California, 2009
MS, Computer Science, University of Southern California, 2005
BASc, Computer Engineering, University of Waterloo, 2003

Contact:

Department of Computer Science
University of British Columbia
201-2366 Main Mall, Vancouver, BC, Canada, V6T 1Z4

Office: ICCS 109
Work: +1 (604) 827 5554
Email: first initial + last name at acm dot org

Publications:

2010:

Saal H.P., Ting J., and Vijayakumar S. Active Sequential Learning with Tactile Feedback, International Conference on Artificial Intelligence and Statistics (AISTATS 2010). [pdf]
Ting, J., D’Souza, A., Vijayakumar, S., and Schaal, S. Efficient Learning and Feature Selection in High-Dimensional Regression, Neural Computation, 22(4): 831-886. [preprint]

2009:

Saal H.P., Ting J., and Vijayakumar S. Active Filtering for Robotic Tactile Learning, NIPS 2009 Workshop on Adaptive Sensing, Active Learning and Experimental Design: Theory, Methods and Applications, Poster.
Ting, J. Bayesian Methods for Autonomous Learning Systems, Phd Thesis, Department of Computer Science, University of Southern California. [pdf]

2008:

Ting, J., Kalakrishnan, M., Vijayakumar, S., and Schaal, S. Bayesian Kernel Shaping for Control, Advances in Neural Processing Systems (NIPS 2008). [pdf] [appendix]
Ting, J., D’Souza, A., Yamamoto, K., Yoshioka, T., Hoffman, D., Kakei, S., Sergio, L., Kalaska, J., Kawato, M., Strick, P., and Schaal, S. Variational Bayesian least squares: An application to brain-machine interface data, Neural Networks: Special Issue on Neuroinformatics, 21(8), 1112-1131. [pdf]
Ting, J., D’Souza, A., Vijayakumar, S., and Schaal, S. A Bayesian Approach to Empirical Local Linearization for Robotics, International Conferences on Robotics and Automation (ICRA 2008), Pasadena, CA. [pdf] [slides]
Ting, J. and Schaal, S. Local Kernel Shaping for Function Approximation, Learning Workshop, Snowbird, April 2008, Poster.

2007:

Ting, J. and Schaal, S. Bayesian Nonparametric Regression with Local Models, NIPS 2007 Workshop on Robotic Challenges for Machine Learning, Poster.
Ting, J., D’Souza, A., Yamamoto, K., Yoshioka, T., Hoffman, D., Kakei, S., Sergio, L., Kalaska, J., Kawato, M., Strick, P., and Schaal, S. 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. [poster]
Ting, J., Theodorou, E., and Schaal, S. Learning an Outlier-Robust Kalman Filter, European Conference on Machine Learning (ECML 2007), Warsaw, Poland. [pdf] [workshop slides]
Ting, J., D’Souza, A., and Schaal, S. Automatic Outlier Detection: A Bayesian Approach, International Conference on Robotics and Automation (ICRA 2007), Rome, Italy. [pdf] [slides]
Ting, J., Theodorou, E., and Schaal, S. A Kalman filter for robust outlier detection, IEEE International Conference on Intelligent Robotics Systems (IROS 2007), San Diego, CA. [pdf] [slides]

2006:

Ting, J., D’Souza, A., and Schaal, S. Bayesian Regression with Input Noise for High Dimensional Data, International Conference on Machine Learning (ICML 2006), Pittsburgh, PA. [pdf] [slides]
Ting, J., Mistry, M., Peters, J., Schaal, S., and Nakanishi, J. A Bayesian Approach to Nonlinear Parameter Identification for Rigid Body Dynamics, Robotics: Science and Systems (RSS 2006), Philadelphia, PA. [pdf]

2005:

Ting, J., D’Souza, A., Yamamoto, K., Yoshioka, T., Hoffman, D., Kakei, S., Sergio, L., Kalaska, J., Kawato, M., Strick, P., and Schaal, S. Predicting EMG Data from M1 Neurons with Variational Bayesian Least Squares, Advances in Neural Information Processing Systems (NIPS 2005). [pdf]

2004:

Ting, J., D’Souza, A., and Schaal, S. Predicting EMG Activity from Neural Firing in M1 with Bayesian Backfitting, 11th Joint Symposium of Neural Computation (JSNC 2004).

Workshops Organized:

Workshop on Regression in Robotics (co-located with RSS 2009) [Video lectures]
Women in Machine Learning (co-located with NIPS 2008)

Links: