Curriculum Vitae

• Primary: Machine Learning
• Secondary: Computer Vision, Time Series Analysis, Computational Biology, Behavior Modeling, Human-Computer Interaction

• Citizenship: Canadian with H1b visa status

• Columbia University, Computer Science, Assistant Professor (07/02 - )
• Columbia University, Computer Science, Lecturer (01/02 - 07/02)
• MIT Media Laboratory, Research Assistant (9/98 - 12/01)
• MIT Media Laboratory, Research Assistant (7/96 - 6/98)
• McGill Center for Intelligent Machines, Research Assistant (7/94 - 6/96)
• Farabi Research Inc., Software Engineer (12/92 - 9/93)

• Massachusetts Institute of Technology, PhD, 2002 (Advisors: A. Pentland and T. Jaakkola)
• Massachusetts Institute of Technology, MSc, 1998 (Advisor: A. Pentland)
• McGill University, BEng, 1996 (Advisor: M. Levine)

• T. Jebara. Machine Learning: Discriminative and Generative. Kluwer Academic (Springer) 2004. ISBN 1-4020-7647-9.

• T. Jebara, Y. Song and K. Thadani. Density Estimation under Independent Similarly Distributed Sampling Assumptions. Neural Information Processing Systems (NIPS), December 2007. Spotlight Poster, Acceptance Rate [10%].
• A. Howard and T. Jebara. Learning Monotonic Transformations for Classification. Neural Information Processing Systems (NIPS), December 2007. Spotlight Poster, Acceptance Rate [10%].
• T. Jebara, Y. Song and K. Thadani. Spectral Clustering and Embedding with Hidden Markov Models. European Conference on Machine Learning (ECML), September 2007. Talk, Acceptance Rate [9%].
• P. Shivaswamy and T. Jebara. Ellipsoidal Kernel Machines. Artificial Intelligence and Statistics (AISTATS), March 2007. Talk, Acceptance Rate [13%].
• B. Huang and T. Jebara. Loopy Belief Propagation for Bipartite Maximum Weight b-Matching. Artificial Intelligence and Statistics (AISTATS), March 2007. Talk, Acceptance Rate [13%].
• R. Kondor, A. Howard and T. Jebara. Multi-object tracking with representations of the symmetric group. Artificial Intelligence and Statistics (AISTATS), March 2007. Poster, Acceptance Rate [50%].
• B. Shaw and T. Jebara. Minimum Volume Embedding. Artificial Intelligence and Statistics (AISTATS), March 2007. Poster, Acceptance Rate [50%] .
• R. Kondor and T. Jebara. Gaussian and Wishart Hyperkernels. Neural Information Processing Systems (NIPS), December 2006. Poster, Acceptance Rate [24%].
• M. Mandel, D. Ellis and T. Jebara. An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments. Neural Information Processing Systems (NIPS), December 2006. Poster, Acceptance Rate [24%].
• T. Jebara and V. Shchogolev. B-Matching for Spectral Clustering. European Conference on Machine Learning (ECML), September 2006. Poster, Acceptance Rate [21%].
• D. Lewis, T. Jebara and W. Noble. Nonstationary Kernel Combination. International Conference on Machine Learning (ICML), June 2006. Talk, Acceptance Rate [20%].
• P. Shivaswamy and T. Jebara. Permutation Invariant SVMs. International Conference on Machine Learning (ICML), June 2006. Talk, Acceptance Rate [20%].
• A. Howard and T. Jebara. Dynamical Systems Trees, Uncertainty in Artificial Intelligence (UAI), July 2004. Spotlight Poster, Acceptance Rate [30% ].
• T. Jebara. Kernelizing Sorting, Permutation and Alignment for Minimum Volume PCA. Conference on Learning Theory (COLT), July 2004. Talk, Acceptance Rate [25%].
• T. Jebara. Multi-Task Feature and Kernel Selection for SVMs. International Conference on Machine Learning (ICML), July 2004. Talk, Acceptance Rate [32%].
• R. Pelossof, A. Miller, P. Allen and T. Jebara. An SVM Learning Approach to Robotic Grasping. International Conference on Robotics and Automation (ICRA), April 2004. Talk, Acceptance Rate [58%].
• T. Jebara. Images as Bags of Pixels. International Conference on Computer Vision (ICCV), October 2003. Poster, Acceptance Rate [16%].
• T. Jebara and R. Kondor. Bhattacharyya and Expected Likelihood Kernels. Conference on Learning Theory (COLT), August 2003. Talk, Acceptance Rate [28%].
• R. Kondor and T. Jebara. A Kernel between Sets of Vectors. International Conference on Machine Learning (ICML), August 2003. Best Student Paper Award. Talk, Acceptance Rate [32%].
• T. Jebara. Convex Invariance Learning. Artificial Intelligence and Statistics (AISTATS), January 2003. Talk, Acceptance Rate [15%].
• T. Jebara and A. Pentland. Statistical Imitative Learning from Perceptual Data. In International Conference on Development and Learning (ICDL), 2002. Talk, Acceptance Rate [50%].
• T. Jebara and A. Pentland. On Reversing Jensen's Inequality. In Neural Information Processing Systems 13 (NIPS), 2000. Poster, Acceptance Rate [30%].
• T. Jebara and T. Jaakkola. Feature Selection and Dualities in Maximum Entropy Discrimination. In 16th Conference on Uncertainty in Artificial Intelligence (UAI), 2000. Poster, Acceptance Rate [36%].
• T. Jaakkola, M. Meila and T. Jebara. Maximum Entropy Discrimination. In Neural Information Processing Systems 12 (NIPS), 1999. Talk, Acceptance Rate [4%].
• T. Choudhury, B. Clarkson, T. Jebara and A. Pentland. Multimodal Person Recognition using Unconstrained Audio and Video in Second Conference on Audio- and Video-based Biometric Person Authentication (AVBPA), 1999. Talk.
• T. Jebara and A. Pentland. Action Reaction Learning: Automatic Visual Analysis and Synthesis of Interactive Behaviour. International Conference on Computer Vision Systems (ICVS), 1999. Talk.
• B. Schiele, N. Oliver, T. Jebara and Alex Pentland. An Interactive Computer Vision System, DyPERS: Dynamic Personal Enhanced Reality System. International Conference on Computer Vision Systems (ICVS), 1999. Talk.
• B. Moghaddam, T. Jebara and A. Pentland. Bayesian Modeling of Facial Similarity. In Neural Information Processing Systems 11 (NIPS), 1998. Poster, Acceptance Rate [31%].
• T. Jebara and A. Pentland. Maximum Conditional Likelihood via Bound Maximization and the CEM Algorithm. In Neural Information Processing Systems 11 (NIPS), 1998. Poster, Acceptance Rate [31%].
• B. Moghaddam, T. Jebara and A. Pentland. Efficient MAP / ML Similarity Matching for Visual Recognition. In 14th Int'l Conference on Pattern Recognition (ICPR), 1998. Talk, Acceptance Rate [63%].
• T. Jebara, K. Russell and A. Pentland. Mixtures of Eigenfeatures for Real-Time Structure from Texture. In Proceedings of the International Conference on Computer Vision (ICCV), 1998. Talk, Acceptance Rate [7%].
• T. Jebara, C. Eyster, J. Weaver, T. Starner and A. Pentland. Stochasticks: Augmenting the Billiards Experience with Probabilistic Vision and Wearable Computers. In Proceedings of the International Symposium on Wearable Computers (ISWC), 1997. Talk, Acceptance Rate [18%].
• T. Jebara and A. Pentland. Parametrized Structure from Motion for 3D Adaptive Feedback Tracking of Faces. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1997. Talk, Acceptance Rate [11%].

• T. Jebara, V. Shchogolev and R. Kondor. B-Matching for Identifying Authorship from Text, Journal of Intelligence Community Research and Development, December 2006.
• D. Lewis, T. Jebara and W. Noble. Support Vector Machine Learning from Heterogeneous Data: an Empirical Analysis Using Protein Sequence and Structure, Bioinformatics, 22(22):2753-2760, 15 November 2006.
• G. Deak, M. Bartlett and T. Jebara. Understanding the Development of Social Agents: New Trends in Integrative Cognitive Science, Neurocomputing Journal, ICDL Special Issue, 2007.
• K. Nishino, S. Nayar and T. Jebara. Clustered Blockwise PCA for Representing Visual Data. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 10, p. 1675, October 2005.
• C.Y. Ro, I.K. Toumpoulis, R.C. Ashton, T. Jebara, C. Schulman, G.J. Todd, J.J. Derose and J.J. McGinty. The LapSim: a learning environment for both experts and novices. Studies in Health Technology and Informatics, Medecine Meets Virtual Reality MMVR 13, Volume 111, p. 414-417, 2005.
• C.Y. Ro, I.K. Toumpoulis, R.C. Ahston, C. Imielinska, C., T. Jebara, S.H. Shin, J.D. Zipkin, J.J McGinty, G.J. Todd, J.J. DeRose. A Novel Drill Set for the Enhancement and Assessment of Robotic Surgical Performance. Studies in Health Technology and Informatics, Medecine Meets Virtual Reality MMVR 13, Volume 111, pp. 418-421, 2005.
• T. Jebara, R. Kondor and A. Howard. Probability Product Kernels. Journal of Machine Learning Research, Special Topic on Learning Theory, Volume 5 (Jul): 819-844, July 2004.
• B. Schiele, T. Jebara and N. Oliver. Sensory Augmented Computing: Wearing the Museum's Guide. IEEE Micro 21 (3), May 2001.
• B. Moghaddam, T. Jebara and A. Pentland. Bayesian Face Recognition. Pattern Recognition, Vol. 33, No. 11, Pergamon Press, November 2000. Honorable Mention Award from the Pattern Recognition Society.
• T. Jebara, A. Azarbayejani and A. Pentland. 3D Structure from 2D Motion. In IEEE Signal Processing, May 1999, Vol. 16. No. 3.

• S. Andrews and T. Jebara. Structured Network Learning. Workshop on Learning to Compare Examples, NIPS 2006. Talk, Acceptance Rate [50%].
• T. Jebara, Y. Ivanov, A. Rahimi and A. Pentland. Tracking Conversational Context for Machine Mediation of Human Discourse. American Association for Artificial Intelligence Fall Sypmosium (AAAI), 2000.
• J. Strom, T. Jebara, S. Basu and A. Pentland. Real Time Tracking and Modeling of Faces: An EKF-based Analysis by Synthesis Approach Appears in: Proceedings of the Modelling People Workshop at ICCV, 1999.
• T. Jebara, B. Schiele, N. Oliver and A. Pentland. Dynamic Personal Enhanced Reality System. In Proceedings of the 1998 Image Understanding Workshop, 1998.
• T. Starner, B. Schiele, B. Rhodes, T. Jebara, N. Oliver, J. Weaver and A. Pentland. Augmented Realities Integrating User and Physical Models. In Workshop on Augmented Reality, 1998.
• T. Jebara and A. Pentland. Action Reaction Learning: Analysis and Synthesis of Human Behaviour. In IEEE Workshop on the Interpretation of Visual Motion in conjunction with IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1998.

• T. Starner, B. Schiele, B. Rhodes, T. Jebara, N. Oliver, J. Weaver and A. Pentland. Augmented Realities Integrating User and Physical Models. In Augmented Reality: Placing Artificial Objects in Real Scenes, R. Behringer, G. Klinker, G. J. Klinter and D. Mizell (editors), A. K. Peters Ltd., pp. 73-79, December, 1999.

• T. Jebara, B. Shaw and A. Howard. Optimizing Eigen-Gaps and Spectral Functions using Iterated SDP. Learning Workshop (Talk), March 2007.
• M. Mandel, D. Ellis and T. Jebara. Building a Binaural Source Separator. Workshop on Advances in Models for Acoustic Processing (Poster), NIPS 2006.
• S. Andrews and T. Jebara. Predicting the edges of a network. New York Academy of Sciences, Machine Learning Symposium (Poster), October 2006.
• A. Howard and T. Jebara. Isotonic Support Vector Machines. New York Academy of Sciences, Machine Learning Symposium (Poster), October 2006.
• B. Huang and T. Jebara. Loopy Belief Propagation for Bipartite Maximum Weight B-Matching. New York Academy of Sciences, Machine Learning Symposium (Poster), October 2006.
• T. Jebara, B. Shaw and V. Shchogolev. B-Matching for Embedding. Snowbird Machine Learning Workshop (Poster), April 2006.
• C.Y. Ro, J.J. McGinty, J.J. DeRose, I.K. Toumpoulis, C. Imielinska, T. Jebara, S.H. Shin, H.L. Chughtai, G.J. Todd, R.C. Ashton, A Novel Drill Set Allows Assessment of Robotic Surgical Performance, The Society of American Gastrointestinal and Endsocopic Surgeons Annual Meeting, SAGES, April 2005.
• C.Y. Ro, J.J. DeRose, R.C. Ashton, T. Jebara, A. Burra, S.H. Shin, H.L. Chughtai, G.J. Todd and J.J. McGinty. The Impact of Haptic Expectations on Initial Lapsim Performance: Prior Laparoscopic Experience Does Not Predict Performance, The Society of American Gastrointestinal and Endsocopic Surgeons Annual Meeting, SAGES, April 2005.
• R.C. Ashton, I.K. Toumpoulis, S. Kancherla, K. McGinnis, L. Withers, C. Connery and T. Jebara. Novel Method of Indvidual Cancer Risk Prediction Analysis for Indeterminate Pulmonary Nodules, American College of CHEST Physicians, October 2004.
• T. Jebara and Y. Bengio. Orbit Learning using Convex Optimization. Snowbird Machine Learning Workshop (Poster), April 2004.
• R. Kondor, T. Jebara, G. Csanyi and S. Ahnert. Learning from Derivatives and other Linear Functionals. Snowbird Machine Learning Workshop (Talk), April 2004.
• T. Jebara and T. Jaakkola. Multi-Task SVM Feature Selection. Snowbird Machine Learning Workshop (Poster), April 2002.
• T. Jebara and A. Pentland. Latent Discriminative Learning. Snowbird Machine Learning Workshop (Talk), April 2001.
• D. Roy, M. Hlavac, M. Umaschi, T. Jebara, J. Cassell and A. Pentland. Toco the Toucan: A Synthetic Character Guided by Perception, Emotion and Story. Visual Proceedings of SIGGRAPH, pg. 66, 1997.

• G. Deak, M. Bartlett and T. Jebara, Eds. Neurocomputing Special Issue on the International Conference on Development and Learning, 2007.
• J. Triesch and T. Jebara, Eds. Proceedings of the 2004 International Conference on Development and Learning, ICDL, UCSD Institute for Neural Computation, ISBN 0-615-12704-5, 2004.

• T. Jebara, Tree Structure Distributions, Laplacians and Graph Manifolds, CIAR Neural Computation and Adaptive Perception Meeting, April 2005.
• T. Jebara. Learning to Imitate using Wearable Audio-Visual Sensors. NIPS 2004 Workshop on Multimodal Signal Processing, December 2004.
• T. Jebara. Large Margin Latent Graphical Models. NIPS 2004 Workshop Graphical Models and Kernels, December 2004.
• A. J. Smola, R. I. Kondor, S. V. N. Vishwanathan and T. Jebara Semidefinite Relaxations for MAP Estimation in Exponential Families NIPS 2004 Workshop Graphical Models and Kernels, December 2004.
• T. Jebara, Kernels between Distributions and Sets. MS-IMS-SIAM Conference on Machine Learning, Statistics and Discovery, 2003.
• T. Jebara and R. Kondor. Probability Product Kernels. Workshop on Advances in Machine Learning, 2003.
• T. Jebara. Alternating Projection for Independent Component Analysis. Neural Information Processing Systems 2002 Workshop on Independent Component Analysis and Beyond, 2002.
• T. Jebara. Convex Invariance Learning. Neural Information Processing Systems 2002 Workshop on Spectral Methods in Dimensionality Reduction, Clustering and Classification, 2002.
• T. Jebara and A. Pentland. Action Reaction Learning for Predicting Interactive Behaviour. British Machine Vision Association Workshop on Understanding Visual Behaviour, 2001.
• A. Pentland, T. Jebara, B. Clarkson and S. Basu. Learning Techniques in Audiovisual Information Processing. 15th International Conference on Pattern Recognition Tutorial Session, (ICPR 15), 2000.
• T. Jaakkola, M. Meila and T. Jebara. Maximum Entropy Discrimination for Missing Data. In Workshop on Using Unlabeled Data for Supervised Learning in Neural Information Processing Systems 12 (NIPS), 1999.
• T. Jebara and A. Pentland. Conditional vs Joint Likelihoods and Densities. Workshop on Combining Supervised and Unsupervised Learning in conjunction with Neural Information Processing Systems 11 (NIPS), 1998.

• T. Jebara. Ordered Data Compression System and Methods , United States Patent Application 20050265618 Assignee Name and Address: The Trustees of Columbia University in the City of New York. Serial No.: 11/132078. Series Code: 11. Filed: May 18, 2005. U.S. Current Class: 382/243.

• I.R. Kondor, G. Csanyi, S.E. Ahnert and T. Jebara. Multi Facet Learning in Hilbert Spaces. Columbia University, Computer Science Technical Report. CUCS-054-05. 2005.
• T. Jebara and P. Long. Tree Dependent Identically Distributed Learning. Columbia University, Computer Science Technical Report. CUCS-050-05. 2005.
• A. Howard and T. Jebara. Square Root Propagation. Columbia University, Computer Science Technical Report. CUCS-040-05. 2005.
• T. Jebara and A. Howard. Dynamical Systems Trees. Columbia University, Computer Science Technical Report. CUCS-028-03. 2003.

• T. Jebara. Discriminative, Generative and Imitative Learning. PhD Thesis, Massachusetts Institute of Technology, 2001.
• T. Jebara. Action-Reaction Learning: Analysis and Synthesis of Human Behaviour. Master's Thesis, Massachusetts Institute of Technology, 1998.
• T. Jebara. 3D Pose Estimation and Normalization for Face Recognition. Bachelor's Thesis, McGill University, 1996.

• Sense Networks Inc., Lead of Technical Advisory Board

• Action Editor, Machine Learning Journal, 2007-present.
• Columbia Machine Learning Laboratory, Director, 2002-present.
• Columbia Vision and Graphics Center, Principal Investigator PI, 2002-present.
• Program Committee Member, International Conference on Machine Learning, 2007.
• Program Committee Member, Computer Vision and Pattern Recognition, 2007.
• Program Committee Member, Artificial Intelligence and Statistics, 2007.
• DARPA Computer Science Futures Study Panelist, 2007.
• Steering Committee, NYAS Machine Learning Symposium, October 2006.
• National Science Foundation Panelist, May 2006
• Program Committee Member, Uncertainty in Artificial Intelligence, 2006.
• Program Committee Member, International Conference on Machine Learning, 2006.
• Program Committee Member, Beyond Patches, Computer Vision and Pattern Recognition Workshop, 2006.
• National Science Foundation Panelist, April 2005
• Program Committee Member, International Conference on Machine Learning, 2005.
• Program Committee Member, Uncertainty in Artificial Intelligence, 2005.
• Program Committee Member, Conference on Learning Theory, 2005.
• Associate Editor, Neurocomputing Journal, ICDL Special Issue 2006.
• Program Chair, International Conference on Development and Learning, 2004.
• IEEE Computational Intelligence Society Autonomous Mental Development Technical Committee, 2004-Present.
• National Science Foundation Panelist, June 2004
• National Science Foundation Panelist, March 2004
• Editorial Board Member, Machine Learning Journal, (now Action Editor) 2004-2007.
• Program Committee Member, International Conference on Machine Learning, 2004.
• Program Committee Member, Uncertainty in Artificial Intelligence, 2004.
• Program Committee Member, International Conference on Computer Vision, 2003.
• Program Committee Member, Uncertainty in Artificial Intelligence, 2003.
• Program Committee Member, International Conference on Machine Learning, 2003.
• Program Committee Member, International Conference on Machine Learning Workshop "The Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining", 2003.
• Program Committee Member, European Conference on Machine Learning Workshop: "Probabilistic Graphical Models for Classification", 2003.
• Local Arrangements Chair, International Conference on Development and Learning, 2002.
• Co-Chair, International Joint Conference on Artificial Intelligence Workshop "Text Learning: Beyond Supervision", 2001.
• Organizer, MIT Media Lab Behavior and Learning Workgroup 1999-2001.
• Member of IMLS since 2006.
• Member of the New York Academy of Sciences since 2007.
• Member of ACM since 2002.
• Member of AAAI since 2002.
• Member of IEEE since 1995.

• Best Average Accuracy Algorithm in KDD Challenge 2005 for Entity Resolution Task (ER1B), October 2005.
• National Science Foundation Career Award, 2004.
• Best Paper Award at the International Conference on Machine Learning, ICML 2003 for "A Kernel Between Sets of Vectors" by R. Kondor and T. Jebara, 2003.
• Honorable Mention Winner of the 27th Annual Pattern Recognition Society Award, 2001.
• Semi-Finalist for Discover Magazine Award for Technological Innovation for "Stochasticks: Augmented Reality Billiards", 1999.
• NSERC Canada Graduate Scholarship CGS (Declined), 1996-1998.
• Who's Who (Sciences; Engineering Education).

• Press: American Psychological Association, www.apa.org/monitor/mar07/moveover.html, March 2007.
• Press: AAAI AI Alert, www.aaai.org/AITopics/assets/AIalerts/alert.11.14.02.html, November 2002.
• Press: Wired, www.wired.com/gadgets/miscellaneous/news/2002/06/52990, June 2002.
• Press: Webwereld www.webwereld.nl/articles/179/drager, March 2002.
• Press: Inside Pool, 2002.
• Press: Newsweek.
• Press: Scientific American.
• Press: McGill News.
• Press: Science Photo Library.
• Television: ABC News, World News Now, March 3, 2003.
• Television: New York One News, 2002.
• Television: Tech TV TechLive News, 2002.
• Television: ABC News, World News Tonight, 1998.
• Television: ABC News, Nightline, 1997.
• Television: BBC Tomorrow's World.
• Television: Millenial Mark News.
• Television: RTL Television (German).
• Television: NHK Documentary (Japanese).
• Radio: ZIP FM Radio, 2002 (Japanese).

• Heinz-Nixdorf Paderborn Podium - Wearables Exhibit of DyPERS, 1999.
• Nicograph - Wearables Tokyo Exhibit of DyPERS, 1998.
• SigGraph - Electric Garden - Toco the Toucan, 1997.

• BIRS Workshop: Mathematical Programming in Data Mining and Machine Learning (January 15, 2007)
• NSF Knowledge Discovery & Dissemination (KDD) Conference (October 3, 2006)
• AMS-IMS-SIAM Summer Conference on Machine and Statistical Learning (June 23, 2006)
• Rensselaer Polytechnic Institute CS Colloquium, Host. B. Yener (March 30, 2006)
• Columbia University Statistics Department, Host. L. Paninski (February 13, 2006)
• NSF Knowledge Discovery & Dissemination (KDD) Conference (November 1, 2005)
• NSF Knowledge Discovery & Dissemination (KDD) Challenge (September 28, 2005)
• University College London, Gatsby Unit, Host Z. Ghahramani (July 14, 2005)
• Key Note: Machine Learning & Multimodal Interfaces MLMI'05, Edinburgh (July 11, 2005)
• University of Chicago, Toyota Technology Institute, Host J. Langford (June 6, 2005)
• CIAR Neural Computation & Adaptive Perception Workshop (April 26, 2005)
• Johns Hopkins University CLSP Fall Seminar Series, Host I. Shafran (November 9, 2004)
• NSF Knowledge Discovery & Dissemination (KDD) Conference (September 21, 2004)
• University of Washington, CSEE Talk, Host D. Fox (May 19, 2004)
• Microsoft Research, Redmond, Host N. Jojic (May 10, 2004)
• Rutgers Center for Discrete Mathematics & Theoretical Computer Science (May 7, 2004)
• Rutgers Center for Computational Biomedicine Imaging & Modeling (May 7, 2004)
• Brooklyn Polytechnic, Computer Science Spring Seminar Series (April 2004)
• ETH Zurich, Computer Science, Graphics Seminar Talk (March 2004)
• NSF Knowledge Discovery & Dissemination (KDD) Conference (November 2003)
• Columbia University CAT Technology Forum (September 2003)
• AT&T Research, Florham Park (July 2003)
• AMS-IMS-SIAM Conference on Machine Learning, Statistics & Discovery (June 2003)
• Microsoft Research, Redmond (May 2003)
• IBM Watson Research, Hawthorne (December 2002)
• Columbia University, Applied Physics and Mathematics (November 2002)
• NASA and ONR Workshop on Combating Uncertainty with Fusion (April 2002)
• Snowbird Machine Learning Workshop (April 2002)
• GE Corporate Research & Development (February 2002)
• Microsoft Research, Redmond (May 2001)
• AT&T Research, Middletown (May 2001)
• IBM Almaden Research (May 2001)
• University of Washington, Computer Science (April 2001)
• Stanford University, Computer Science (April 2001)
• Columbia University, Computer Science (April 2001)
• Carnegie Mellon University, CALD (April 2001)
• Snowbird Machine Learning Workshop (April 2001)
• McGill University, Electrical Engineering (April 2001)
• WhizBang Research Labs (March 2001)
• University College London, Gatsby Unit (January 2001)
• BBN Technologies, Verizon (December 2000)

• DIMACS Workshop Bar Code of Life, Host: R. Jornsten (September 26, 2005)
• Google Faculty Summit (August 5, 2005)

• Recommender, MacArthur Foundation Fellowship Awards
• Reviewer, Journal of Machine Learning Research
• Reviewer, Journal of Artificial Intelligence Research
• Reviewer, Journal of Intelligent Information Systems
• Reviewer, Journal of Optical Society of America A
• Reviewer, IEEE Pattern Analysis and Machine Intelligence
• Reviewer, IEEE Signal Processing Letters
• Reviewer, IEEE Transactions on Neural Networks
• Reviewer, IEEE Transactions on Robotics and Automation
• Reviewer, IEEE Transactions on Systems, Man and Cybernetics
• Reviewer, IEEE Transactions on Image Processing
• Reviewer, Machine Learning Journal
• Reviewer, Image and Vision Computing Journal
• Reviewer, SIAM Review
• Reviewer, Computer Vision and Image Understanding
• Reviewer, International Journal of Computer Vision
• Reviewer, International Conference on Machine Learning
• Reviewer, International Conference on Computer Vision
• Reviewer, International Conference on Development and Learning
• Reviewer, International Joint Conference on Artificial Intelligence
• Reviewer, Neural Information Processing Systems (00, 01, 02, 03, 04, 05, 07)
• Reviewer, Conference on Uncertainty in Artificial Intelligence
• Reviewer, Computer Vision and Pattern Recognition Conference
• Reviewer, International Symposium on Mixed and Augmented Reality
• Reviewer, European Conference on Machine Learning
• Reviewer, American Mathematical Society NSA Grant Proposals
• Reviewer, SIGGRAPH Conference
• Reviewer for various workshops

• Course: Advanced Machine Learning 4772 (Fall 2007)
  Enrollment: approximately 30.
  Evaluation:
• Course: Machine Learning 4771 (Spring 2007)
  Enrollment: approximately 70.
  Evaluation: 4.18, 3.96, 3.84, 4.04, 4.02, 3.98, 4.02, 3.96
• Course: Learning and Empirical Inference 6998-4 (Spring 2007)
  (taught jointly with V. Vapnik, I. Rish and G. Tesauro)
  Enrollment: approximately 15.
  Evaluation: 3.63, 4.25, 4.25, 4.25, 4.38, 4.13, 4.25, 4.25
• Course: Advanced Machine Learning 6772 (Fall 2006)
  Enrollment: approximately 25.
  Evaluation: 4.62, 4.25, 4.17, 4.15, 4.54, 4.15, 4.60, 4.42
• Course: Machine Learning 4771 (Spring 2006)
  Enrollment: approximately 60.
  Evaluation: 3.83, 3.28, 3.64, 3.56, 3.64, 3.56, 3.56, 3.56
• Course: Advanced Machine Learning 6772 (Fall 2005)
  Enrollment: approximately 20.
  Evaluation: 4.62, 4.31, 4.13, 4.50, 4.19, 3.93, 4.00, 4.50
• Course: Machine Learning 4771 (Spring 2005)
  Enrollment: approximately 40.
  Evaluation: 4.29, 3.86, 3.90, 3.90, 4.05, 3.90, 4.24, 3.86
  Dean's Excellent Teachers List
• Course: Advanced Machine Learning 4995 (Fall 2004)
  Enrollment: approximately 30.
  Evaluation: 4.61, 4.33, 4.50, 4.47, 4.29, 4.29, 4.35, 4.50
  Dean's Excellent Teachers List
• Course: Machine Learning 4771 (Spring 2004)
  Enrollment: approximately 60.
  Evaluation: Above 4 on Average.
  Dean's Excellent Teachers List
• Course: Advanced Machine Learning 6772 (Fall 2003)
  Enrollment: approximately 20.
  Evaluation: Above 4 on Average.
  Dean's Excellent Teachers List
• Course: Machine Learning 4771 (Spring 2003)
  Enrollment: approximately 40.
  Evaluation: Above 4 on Average.
• Course: Computer Organization 3824 (Fall 2002)
  Enrollment: approximately 80.
  Evaluation: Above 4 on Average.
• Course: Advanced Machine Learning 6998-01 (Spring 2002)
  Enrollment: approximately 30.
  Evaluation: Above 4 on Average.

• Stuart Andrews (Post-Doctoral Research Scientist)
• Andrew Howard (Columbia CS PhD)
• Bert Huang (Columbia CS PhD, joint with A. Salleb-Aouissi)
• Raphael Pelossof (Columbia CS PhD, joint with D. Waltz)
• Pannagadatta Shivaswamy (Columbia CS PhD)
• Blake Shaw (Columbia CS PhD)
• Vlad Shchogolev (Columbia CS MS)

• Risi Kondor, Columbia CS PhD, Now PostDoc at University College London (Gatsby)
• Darrin Lewis, Columbia CS PhD (joint with W. Noble), Now at Morgan Stanley
• Katherine Heller, Columbia CS MS, Now PhD at University College of London (Gatsby)
• Benzhu Zhang, Columbia CS MS, Now at Goldman Sachs
• Deep Pai, Columbia CS MS, Now at Lucent
• Martin Wagner, MS, Now Faculty at Technische Universitat Munchen (Germany), wiki.medien.ifi.lmu.de/MartinWagner
• Shikher Bisaria, Undergrad, Now at Och-Ziff Capital Management Group
• Kenneth Russell, Undergrad, Now at Sun
• Naiqing Gu, Undergrad, Now PhD at University of Maryland
• Cyrus Eyster, Undergrad

• Risi Kondor, Group Theoretical Methods in Machine Learning, (Columbia CS, August 2007)
• Darrin Lewis, Combining Kernels for Classification, (Columbia CS, May 2006)

• Risi Kondor, Group Theoretical Methods in Machine Learning, (Columbia CS, August 2007)
• Henry Bigelow, Statistical Analysis and Prediction of Membrane Proteins using Bayesian Networks, (Columbia Biochemistry and Molecular Biophysics, April 2007)
• Rui Kuang, Inferring Protein Structure with Discriminative Learning and Network Diffusion, (Columbia CS, August 2006)
• German Creamer, Using Boosting for Automated Trading and Planning, (Columbia CS, June 2006)
• Darrin Lewis, Combining Kernels for Classification, (Columbia CS, May 2006)
• Sinem Guven, Authoring and Presenting Situated Media in Augmented and Virtual Reality, (Columbia CS, April 2006)
• Jouni Kerman, An Integrated Framework for Bayesian Graphical Modeling, Inference and Prediction, (Columbia Statistics, April 2006)
• Dong-Qing Zhang, Statistical Part-based Model for Object/Scene Detection, (Columbia EE, September 2005)
• Lexing Xie, Unsupervised Pattern Discovery for Multimedia Sequences, (Columbia EE, August 2005)
• Manuel Reyes, Statistical Graphical Models for Scene Analysis, Source Separtion and Other Audio Applications (Columbia EE, June 2005)
• Simon Lok, Automated Layout of Information Presentations (Columbia CS, April 2005)
• Yan Liu, Feature Selection in Large Dataset Processing, Especially in the Video Domain (Columbia CS, April 2005)
• Pablo Duboue, Indirect Supervised Learning of Strategic Generation Logic (Columbia CS, January 2005)
• Chris Pal, Probability Models for Information Processing and Machine Perception (University of Toronto, December 2004)
• Tiecheng Liu, Semantic Summarization and Indexing of Extended Videos, with Application to Instructional Videos (Columbia CS, July 2003)
• Efstathios Hadjidemetriou, Use of Histograms for Recognition (Columbia CS, September 2002)
• Eleazar Eskin, Sparse Sequence Modeling with Applications to Computational Biology and Intrusion Detection (Columbia CS, April 2002)

• Drexel Hallaway, FlyingFrames: Transforming a Static Optical Metrology System to Accomplish Dynamic Motion Tracking for Augmented Reality (Proposal, Columbia CS, May 2007)
• Mitchell Morris, Feature Selection for Video Recognition using Support Vector Machines (Candidacy Exam, Columbia CS, May 2007)
• Sean White, Visualization in Augmented Reality (Candidacy Exam, Columbia CS, December 2006)
• Risi Kondor, Learning on Groups (Proposal, Columbia CS, May 2006)
• Ashul Kundaje, Biology and Learning in High Throughput Data (Candidacy Exam, Columbia CS, May 2006)
• Andrew Howard, Time Series Models in Machine Learning (Candidacy Exam, Columbia CS, May 2006)
• Rui Kuang, Inferring Protein Structure with Discriminative Learning and Network Diffusion (Proposal, Columbia CS, November 2005)
• Darrin Lewis, Large Margin Latent Generative Models (Proposal, Columbia CS, April 2005)
• Sinem Guven, Situated Multimedia and Hypermedia Authoring in Augmented and Virtual Environments (Proposal, Columbia CS, April 2005)
• Risi Kondor, Learning in Structured Domains (Candidacy Exam, Columbia CS, December 2004)
• Edward Ishak, Interaction and Visualization Techniques to Virtually Expand Limited Screen Space (Candidacy Exam, Columbia CS, December 2004)
• Manuel Reyes, Statistical Graphical Models for Scene Analysis, Source Separtion and Other Audio Applications (Proposal, Columbia EE, September 2004)
• Rui Kuang, Machine Learning in the Study of Protein Structure (Candidacy Exam, Columbia CS, May 2004)
• German Creamer, Machine Learning Applications to Automated Trading and Corporate Finance Problems (Candidacy Exam, Columbia CS, April 2004)
• Ke Wang, Anomaly Detection in Network Security (Candidacy Exam, Columbia CS, April 2004)
• Drexel Hallaway, User Tracking for Augmented Reality (Candidacy Exam, Columbia CS, April 2004)
• Lexing Xie, Unsupervised Structure Discovery for Multimedia Sequences, (Proposal, Columbia EE, February 2004)
• Dong Qing Zhang, Discover Compositional Visual Patterns using Graphical Models with Relational Feature and Loopy Belief Inference, (Proposal, Columbia EE, February 2004)
• Yan Liu, Situated Multimedia and Hypermedia Authoring in Augmented and Virtual Environments (Proposal, Columbia CS, December 2003)
• Sinem Guven, Situated Multimedia and Hypermedia Authoring in Augmented and Virtual Environments (Candidacy Exam, Columbia CS, December 2003)
• Lijun Tang, Method and User Interface of Instructional Video Indexing (Candidacy Exam, Columbia CS, November 2003)
• Gabor Blasko, Manual Input Methods and Techniques for Mobile and Wearable Computer Systems (Candidacy Exam, Columbia CS, June 2003)
• Pablo Duboue, Inducing Content Planning Schemata from a Text and Knowledge Resource (Proposal, Columbia CS, May 2003)
• Darrin Lewis, Transduction (Candidacy Exam, Columbia CS, April 2003)
• Simon Lok, Automated Layout of Information Presentations (Proposal, Columbia CS, January 2003)

• Columbia Center for Computational Learning Systems, Advisory Committee, 2003-present.
• MS Admissions Committee, 2006-2007
• PhD Committee, 2006-2007
• Visibility Committee, 2006-2007
• Columbia CS Master's in Machine Learning Advisor, 2006-2007
• Faculty Recruiting Committee, 2005-2006
• Columbia CS Distinguished Lecture Series Chair, 2005-2006
• SEAS Undergraduate Advisor (SEAS), 2005-2006
• Columbia CS Distinguished Lecture Series Chair, 2004-2005
• PhD Recruiting Committee, 2004-2005
• SEAS Undergraduate Advisor (SEAS), 2004-2005
• Columbia CS Master's in Machine Learning Advisor, 2004-2005
• Columbia CS Distinguished Lecture Series Chair, 2003-2004
• Faculty Recruiting Committee, 2003-2004
• PhD Recruiting Committee, 2003-2004
• Junior and Combined Plan Advisor (SEAS), 2003-2004
• Columbia CS Distinguished Lecture Series Chair, 2002-2003
• Faculty Recruiting Committee, 2002-2003
• PhD Recruiting Committee, 2002-2003
• Freshman and Sophomore Advisor (SEAS), 2002-2003
• PhD Recruiting Committee, 2001-2002

• ONR Grant Award N000140710507 (PI: Jebara), $120,000, 2007
  Learning to Match Data from Heterogeneous Databases (Mod No: 07PR04918-00)
• CIA KDD Program Award (PI: Jebara), $219,000, 2006
  Learning to Match People, Multimedia and Graphs via Permutation
• CIA KDD Challenge Award (PI: Jebara), $40,180, 2005
  Text and Author Identity as a Permutation Learning Problem
• CIA KDD Program Award (PI: Jebara), $171,124, 2005
  Correspondence in Learning via Permutation Algorithms
• NSF Career Award IIS-0347499 (PI: Jebara), $498,964, 2004
  CAREER: Discriminative and Generative Machine Learning with Applications in Tracking and Gesture Recognition
• Microsoft Corporation Unrestricted Gift (PI: Jebara), $10,000, 2004
• U. of Washington, NSF Sub-Contract on IIS-0093302 (PI: Jebara), $121,909, 2004
  CAREER: Support Vector Methods for Functional Genomics
• AlphaStar Corporation Unrestricted Gift (PI: Jebara), $26,000, 2003
• NSF ITR CCR-0312690 (PI: Jebara), $240,215, 2003
  ITR: Representation Learning: Transformations and Kernels for Collections of Tuples