The Artificial Intelligence Group at UCSD engages in a wide range of theoretical and experimental research. Areas of particular strength include machine learning, probabilistic inference, neural computation, and cognitive modeling. Within these areas, students and faculty also pursue real-world applications to problems in computer vision, speech and audio processing, information retrieval, bioinformatics, brain-computer interfaces, and computer systems and networking. The Artificial Intelligence Group is part of a larger campus-wide effort in Computational Statistics and Machine Learning (COSMAL). Interdisciplinary collaborations are strongly supported and encouraged.

• Kamalika Chaudhuri

Recent alumni

• Lawrence Cayton (Ph.D. 2009) → Max Planck Institute
• Vincent Rabaud (Ph.D. 2009) → VideoSurf
• Antoni Chan (Ph.D. 2008) → City University of Hong Kong
• Dashan Gao (Ph.D. 2008) → General Electric Global Research
• Neel Joshi (Ph.D. 2008) → Microsoft Research
• Andrew Rabinovich (Ph.D. 2008) → Google
• Douglas Turnbull (Ph.D. 2008) → Swarthmore College
• Kristin Branson (Ph.D. 2007) → Caltech
• Piotr Dollár (Ph.D. 2007) → Caltech
• Sameer Agarwal (Ph.D. 2007) → University of Washington
• Eric Wiewiora (Ph.D. 2007) → University of Alberta
• Lingyun Zhang (Ph.D. 2007) → ID Analytics


• Janet Hsiao (postdoc 2006-2008) → University of Hong Kong
• Tim Marks (postdoc 2006-2008) → MERL
• Claire Monteleoni (postdoc 2006-2008) → CCLS, Columbia University
• Sanmay Das (postdoc 2006-2007) → Rensselaer Polytechnic Institute

Recent Publications

• C.-C. Cheng, F. Sha, and L. K. Saul. Large margin feature adaptation for automatic speech recognition. In Proceedings of the IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU-09). Merano, Italy. December 2009.

• S. Cheamanunkul, E. Ettinger, M. Jacobsen, P. Lai, and Y. Freund. Detecting, tracking and interacting with people in a public space. In Proceedings of the 11th International Conference on Multimodal Interfaces and the 6th Workshop on Machine Learning for Multimodal Interaction (ICMI-MLMI-09). Boston, MA. November 2009.

• L. Barringon, R. Oda, and G. Lanckriet. Smarter than genius? Human evaluation of music recommender systems. In Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR-09). Kobe, Japan. October 2009.

• D. J. Hu and L. K. Saul. A probabilistic model of unsupervised learning for musical-key profiles. In Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR-09). Kobe, Japan. October 2009.

• B. McFee and G. Lanckriet. Heterogeneous embedding for subjective artist similarity. In Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR-09). Kobe, Japan. October 2009.

• C.-C. Cheng, F. Sha, and L. K. Saul. A fast online algorithm for large margin training of continuous-density hidden Markov models. In Proceedings of the 10th Annual Conference of the International Speech Communication Association (Interspeech-09). Brighton, UK. September 2009.

• D. Turnbull, L. Barrington, M. Yazdani, and G. Lanckriet. Combining audio content and social context for semantic music discovery. In Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-09). Boston, MA. July 2009.

• S. Dasgupta and Y. Freund. Random projection trees for vector quantization. IEEE Transactions on Information Theory 55(7):3229-3242. July 2009.

• S. Russell and L. K. Saul. The ultimate pilot program. Communications of the ACM 52(7):96. July 2009.

• L. Barrington, D. Turnbull, D. O'Malley, and G. Lanckriet. User-centered design of a social game to tag music. In Proceedings of the Human Computation Workshop (HCOMP-09). Paris, France. July 2009.

• J. Ma, L. K. Saul, S. Savage, and G. M. Voelker. Beyond blacklists: learning to detect malicious web sites from suspicious URLs. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-09). Paris, France. July 2009.

• P. Faymonville, K. Wang, J. Miller, and S. Belongie. CAPTCHA-based image labeling on the Soylent Grid. In Proceedings of the Human Computation Workshop (HCOMP-09). Paris, France. June 2009.

• A. B. Chan, M. Morrow, and N. Vasconcelos. Analysis of crowded scenes using holistic properties. In Proceedings of the 11th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS-09). Miami, FL. June 2009.

• A. Rabinovich and S. Belongie. Scenes versus objects: a comparative study of two approaches to context-based recognition. In Proceedings of the International Workshop on Visual Scene Understanding (ViSU-09). Miami, FL. June 2009.

• B. Babenko, M.-H. Yang, and S. Belongie. Visual tracking with online multiple instance learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-09). Miami, FL. June 2009.

• N. J. Butko and J. R. Movellan. Optimal scanning for faster object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-09). Miami, FL. June 2009.

• A. B. Chan and N. Vasconcelos. Variational layered dynamic textures. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-09). Miami, FL. June 2009.

• N. Joshi, L. C. Zitnick, A. Szeliski, and D. J. Kriegman. Image deblurring and denoising using color priors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-09). Miami, FL. June 2009.

• V. Rabaud and S. Belongie. Linear embeddings in non-rigid structure from motion. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-09). Miami, FL. June 2009.

• R. Alterovitz, A. Arvey, S. Sankararaman, C. Dallett, Y. Freund, and K. Sjolander. ResBoost: characterizing and predicting catalytic residues in enzymes. BMC Bioinformatics 10:197. June 2009.

• D. Hsu, S. M. Kakade, and T. Zhang. A spectral algorithm for learning hidden Markov models. In Proceedings of the 22nd Annual Conference on Learning Theory (COLT-09). Montreal, Canada. June 2009.

• S. Kpotufe. Escaping the curse of dimensionality with a tree-based regressor. In Proceedings of the 22nd Annual Conference on Learning Theory (COLT-09). Montreal, Canada. June 2009.

• N. A. Verma, S. Kpotufe, and S. Dasgupta. Which spatial partition trees are adaptive to intrinsic dimension? In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI-09). Montreal, Canada. June 2009.

• A. Beygelzimer, S. Dasgupta, and J. Langford. Importance-weighted active learning. In Proceedings of the 26th International Conference on Machine Learning (ICML-09). Montreal, Canada. June 2009.

• K. Chaudhuri, S. Kakade, K. Livescu, and K. Sridharan. Multiview clustering via canonical correlation analysis. In Proceedings of the 26th International Conference on Machine Learning (ICML-09). Montreal, Canada. June 2009.

• C.-C. Cheng, F. Sha, and L. K. Saul. Matrix updates for perceptron training of continuous-density hidden Markov models. In Proceedings of the 26th International Conference on Machine Learning (ICML-09). Montreal, Canada. June 2009.

• Y. Cho and L. K. Saul. Learning dictionaries of stable autoregressive models for audio scene analysis. In Proceedings of the 26th International Conference on Machine Learning (ICML-09). Montreal, Canada. June 2009.

• G. Doyle and C. Elkan. Accounting for word burstiness in topic models. In Proceedings of the 26th International Conference on Machine Learning (ICML-09). Montreal, Canada. June 2009.

• J. Ma, L. K. Saul, S. Savage, and G. M. Voelker. Identifying suspicious URLs: an application of large-scale online learning. In Proceedings of the 26th International Conference on Machine Learning (ICML-09). Montreal, Canada. June 2009.

• B. McFee and G. Lanckriet. Partial order embedding with multiple kernels. In Proceedings of the 26th International Conference on Machine Learning (ICML-09). Montreal, Canada. June 2009.

• D. Putthividya, H. Attias, and S. Nagarajan. Independent factor topic models. In Proceedings of the 26th International Conference on Machine Learning (ICML-09). Montreal, Canada. June 2009.

• N. Rasiwasia and N. Vasconcelos. Holistic context modeling using semantic co-occurrences. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-09). Miami, FL. June 2009.

• A. B. Chan and N. Vasconcelos. Variational layered dynamic textures. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-09). Miami, FL. June 2009.

• A. B. Chan, M. Morrow, and N. Vasconcelos. Analysis of crowded scenes using holistic properties. In Proceedings of the 11th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS-09). Miami, FL. June 2009.

• T. Wu, N. J. Butko, P. Ruvulo, M. S. Bartlett, and J. R. Movellan. Learning to make facial expressions. In Proceedings of the International Conference on Developmental Learning (ICDL-09). Shanghai, China. June 2009.

• D. Gao, S. Han, and N. Vasconcelos. Discriminant saliency, the detection of suspicious coincidences, and applications to visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(6):989-1005. June 2009.

• [Publications Archive]

Affiliations

	CalIT2	Computational Statistics and Machine Learning Group
	Computer Audition Lab	Computer Vision Laboratory
	Institute for Neural Computation	Laboratory for Machine Learning and Signal Processing
	Machine Perception Lab	Statistical Visual Computing Lab
	Temporal Dynamics of Learning Center	Vision and Learning in Humans and Machines (IGERT)