Email: first initial followed by last name at acm dot org
I'm currently Director of Data Science at Insikt, Inc., working on large-scale data-driven optimization problems throughout the organization.
Examples of some of the problems we're solving include real-time risk monitoring, call center optimization and fraud prevention, to name a few.
I was a Research Scientist at Bosch Research in Palo Alto, CA until 2013. Prior to that, I was an NSERC postdoctoral fellow at the University of British Columbia from 2010 to 2011, working with Nando de Freitas, Kevin Murphy, and Jim Little. I received my PhD in Computer Science from the University of Southern California in 2009, where I was advised by Stefan Schaal. I graduated with a BASc in Computer Engineering from the University of Waterloo in 2003. I also spent time at the University of Edinburgh in 2009, working with Sethu Vijayakumar.
- W.-L. Lu, J. Ting, K. Murphy, and J. Little. Learning to Track and Identify Players from Broadcast Sports Videos, IEEE Transactions on Pattern Analysis and Machine Intelligence 35(7): 1704-1716. [paper, supplementary videos]
- W.-L. Lu, J. Ting, K. Murphy, and J. Little. Identifying Players in Broadcast Sports Videos using Conditional Random Fields, IEEE Computer Vision and Pattern Recognition (CVPR). [paper]
- L. Bazzani, N. de Freitas, H. Larochelle, V. Murino, and J. Ting Learning Attentional Policies for Tracking and Recognition in Video with Deep Networks, International Conference on Machine Learning (ICML).
- J. Ting, A. D'Souza, and S. Schaal. Bayesian Robot System Identification with Input and Output Noise, Neural Networks, 24(1): 99-108. [preprint]
- H. P. Saal, J. Ting, and S. Vijayakumar. Active Sequential Learning with Tactile Feedback, International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR: W&CP 9: 677-684. [pdf]
- H. P. Saal, J. Ting, and S. Vijayakumar. Active Estimation of Object Dynamics Parameters with Tactile Sensors, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). [pdf]
- B. Chen, J. Ting, B. M. Marlin, and N. de Freitas. Deep Learning of Invariant Spatio-temporal Features from Video, NIPS Workshop on Deep Learning and Unsupervised Feature Learning, Oral talk.
- L. Bazzani, N. de Freitas, and J. Ting. Learning Attentional Mechanisms for Simultaneous Object Tracking and Recognition with Deep Networks, NIPS Workshop on Deep Learning and Unsupervised Feature Learning.
- J. Ting, A. D'Souza, S. Vijayakumar, and S. Schaal. Efficient Learning and Feature Selection in High-Dimensional Regression, Neural Computation, 22(4): 831-886. [preprint]
- J. Ting, S. Vijayakumar, and S. Schaal. Locally Weighted Regression for Control, Encyclopedia of Machine Learning (eds: Sammut, C. and Webb, G. I.), 613-624, Springer. [preprint]
- H. P. Saal, J. Ting, and S. Vijayakumar. Active Filtering for Robotic Tactile Learning, NIPS Workshop on Adaptive Sensing, Active Learning and Experimental Design: Theory, Methods and Applications, Poster.
- J. Ting, Bayesian Methods for Autonomous Learning Systems, Phd Thesis, Department of Computer Science, University of Southern California. [pdf]
- J. Ting, M. Kalakrishnan, S. Vijayakumar, and S. Schaal. Bayesian Kernel Shaping for Control, Advances in Neural Processing Systems (NIPS). [pdf] [appendix]
- J. Ting, A. D'Souza, S. Vijayakumar, and S. Schaal. A Bayesian Approach to Empirical Local Linearization for Robotics, International Conference on Robotics and Automation (ICRA). [pdf] [slides]
- J. Ting, A. D'Souza, K. Yamamoto, T. Yoshioka, D. Hoffman, S. Kakei, L. Sergio, J. Kalaska, M. Kawato, P. Strick, and S. Schaal. Variational Bayesian Least Squares: An Application to Brain-Machine Interface Data, Neural Networks: Special Issue on Neuroinformatics, 21(8), 1112-1131. [pdf]
- J. Ting, and S. Schaal. Local Kernel Shaping for Function Approximation, Learning Workshop, Snowbird, April 2008, Poster.
- J. Ting, E. Theodorou, and S. Schaal. Learning an Outlier-Robust Kalman Filter, European Conference on Machine Learning (ECML). [pdf]
- J. Ting, A. D'Souza, and S. Schaal. Automatic Outlier Detection: A Bayesian Approach, International Conference on Robotics and Automation (ICRA). [pdf] [slides]
- J. Ting, E. Theodorou, and S. Schaal. A Kalman filter for Robust Outlier Detection, IEEE International Conference on Intelligent Robotics Systems (IROS). [pdf] [slides]
- J. Ting, A. D'Souza, K. Yamamoto, T. Yoshioka, D. Hoffman, S. Kakei, L. Sergio, J. Kalaska, M. Kawato, P. Strick, and S. Schaal. 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). [poster]
- J. Ting and S. Schaal. Bayesian Nonparametric Regression with Local Models, NIPS Workshop on Robotic Challenges for Machine Learning, Poster.
- J. Ting, A. D'Souza, and S. Schaal. Bayesian Regression with Input Noise for High Dimensional Data, International Conference on Machine Learning (ICML). [pdf] [slides]
- J. Ting, M. Mistry, J. Peters, S. Schaal, and J. Nakanishi. A Bayesian Approach to Nonlinear Parameter Identification for Rigid Body Dynamics, Robotics: Science and Systems (RSS). [pdf]
- J. Ting, J., A. D'Souza, A., K. Yamamoto, T. Yoshioka, D. Hoffman, S. Kakei, L. Sergio, J. Kalaska, M. Kawato, P. Strick, and S. Schaal. Predicting EMG Data from M1 Neurons with Variational Bayesian Least Squares, Advances in Neural Information Processing Systems (NIPS). [pdf]
- J. Ting, A. D'Souza, and S. Schaal. Predicting EMG Activity from Neural Firing in M1 with Bayesian Backfitting, 11th Joint Symposium of Neural Computation (JSNC).
Variational Bayesian Least Squares:
A variational Bayesian algorithm performs efficient high-dimensional linear regression, handling large numbers of irrelevant and redundant dimensions in the input data. Good references include Ting, D'Souza, Vijayakumar & Schaal (2010) in Neural Computation and Ting & al. (Neural Networks: Neuroinformatics, 2008). Older conference proceedings include Ting & al. (2005) in NIPS and D'Souza, Vijayakumar & Schaal (2004) in ICML.
Bayesian Regression with Input Noise for High Dimensional Data:
A Bayesian treatment of factor analysis in joint-space that can accurately identify parameters in a high-dimensional linear regression problem when input data is noise-contaminated. An application to nonlinear parameter identification in Rigid Body Dynamics is described in Ting, Mistry, Peters, Schaal & Nakanishi (RSS 2006). A good reference is Ting, D'Souza & Schaal in Neural Networks (2010).
Real-time outlier detection:
A weighted least squares-like approach to outlier detection, where each data sample has a weight associated with it. This model treats the weights probabilistically and learn their optimal values, avoiding modeling with heuristic error functions, sampling or tuning of open parameters (such as a threshold parameter). Details on how to perform automatic outlier detection in linear regression can be found in the paper by Ting, D'Souza & Schaal (2007) in the ICRA proceedings. We can also incorporate this approach to a Kalman filter, allowing us to do real-time automatic outlier detection on streaming data. A good reference is Ting, Theodorou & Schaal (2007) in ECML.