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Other links: Courses I took and taught, Old Projects
Research interests
My main area of interest is statistical machine learning. Specifically, I am interested in
- Developing efficient algorithms for sparse Bayesian regression and graph structure learning.
- Variational algorithms, MCMC and greedy search algorithm.
- Applications involving computational biology, social networks, computer vision and control systems.
Previously, I have worked on statistical signal processing for control (at Honeywell, India) and biomedicine (at IISc, Bangalore).
Publications
- Baback Mogaddham, Benjamin Marlin, Mohammad Emtiyaz Khan and Kevin Murphy, "Accelerating Bayesian Structural Inference for Non-decomposable Gaussian Graphical Model", Accepted for oral presentation in NIPS 2009.
- M. E. Khan and D. N. Dutt, "An Expectation-Maximization Algorithm Based Kalman Smoother Approach for Event-Related Desynchronization (ERD) Estimation from EEG", Vol. 54, No. 7, July 2007, IEEE Transactions on Biomedical Engineering pre-print, Code
- M. E. Khan, H. Raghavan, J. Brahmajosyula, S. K. Ramalingam, S. Narasimhan, "State Estimation with Wireless Devices", Third International Conference on Intelligent Sensing and Information Processing (ICISIP), 14-17 December 2005, Bangalore , India. pdf
- M. E. Khan, H. Raghavan, J. Brahmajosyula, S. K. Ramalingam, S. Narasimhan, "Hybrid System Framework for State Estimation in Systems with Wireless Devices", 2005 Annual Meeting of American Institute of Chemical Engineers (AIChe), Cincinnati, October 30-November 4, 2005.
- M. E. Khan and D. N. Dutt, "Expectation-Maximization (EM) Algorithm for Instantaneous Frequency Estimation with Kalman Smoother", 12th European Signal Processing Conference EUSIPCO 2004,Vienna, Austria.pdf
- M. E. Khan and D. N. Dutt, "Estimation of ERS/ERD with Kalman Smoother: An EM Algorithm Approach", 17th international EURASIP conference BIOSIGNAL 2004, Brno, Czech Republic.
Technical Reports and Talks
- Tech report on "Comparison of Bayesian regression algorithms", (coming soon).
- Derivation for Gaussian likelihood with Gaussian prior on mean. (Feb. 25, 2009).
- A note on Empirical Bayes estimate of Covariance for Multivariate Normal Distribution, (Jan. 29, 2009).
- Tech report on Bayesian search algorithms for decomposable Guassian graphical model, (Dec. 24, 2008).
- Updating Inverse of a Matrix when a Column is added/removed, Notes, code (Feb. 27, 2008).
- Kalman Filter Presentation, Demo Code, and a note on Information filter for Dynamic Programming course (Feb. 25, 2008).
- Presentation of Variational Bayes and Message passing at Machine
learning Reading Group (Oct. 30, 2007)
- A note on Exchangeability, Polya’s Urn, and De-Finetti’s Theorem (Oct. 2, 2007).
- Linear Algebra Tutorial for refresher course (Sep. 28, 2007), Outline, slides.
- Probability Tutorial for refresher course (Sep. 18, 2007), Outline, Slides.
- Talk on "Multi-scale Structure Learning" (Aug. 8, 2007 at CIFAR, NCAP summer school, Toronto)
- Talk on "Brain-Computer Interface: Overview, methods and opportunities" (June 14, 2007 at CIFAR, Time-series Workshop, Toronto)
- Talk on "An Overview of Brain-Computer Interface" (June 08, 2007 at UDLS)
- Talk on "Signal Compression and JPEG" Abstract (May 18, 2007 at UDLS)
- "Machine Learning" course project "Compressed Sensing, Compressed Classification and Joint Signal Recovery", mail me if you need code.(April,07)
- "Statistical Computation" course project "Gibbs Sampling for the Probit Regression Model with Gaussian Markov Random Field Latent Variables", Slides, mail me if you need code. (April,07)
- Talk on "Introduction to probability theory" Abstract (January 26, 2007 at UDLS)
- "Multi-agent systems" course project "Game theory models for Pursuit-evasion games" (Dec.07)
- "Optimization" course project "An incremental deployment algorithm for mobile sensors" (Dec.07)
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