I got my PhD from Professor Smyth's group in the Department of Information and Computer Science, UC Irvine. My interests are in data mining and knowledge discovery in the large data sets, probabilistic modeling, in understanding, application and development of the scalable machine learning techniques. My PhD thesis was on the application of probabilistic models to querying large data sets (see below). I used to work at NEC Laboratories America on model-based recommender systems for ResearchIndex.

Effective June 1, 2003 I have moved to Yahoo! BTW, We are hiring. If you are interested in a position with Yahoo! please drop me a message! Look at some of the cool things we work on at Yahoo! Search.

 

PATENTS

• "Using Cross-Entropy with Local Patterns to Predict Global Patterns in a Database", joint with Heikki Mannila and Padhraic Smyth, filed by Microsoft Research, 1999.
• US6662170: System and method for boosting support vector machines , joint with Byron Dom and Jianchang Mao, issued to IBM Almaden Research, 2003.

 

PUBLICATIONS

1. D. Pavlov, R. Balasubramanyan, B. Dom, S. Kapur, J. Parikh. Document preprocessing for Naive Bayes classification and clustering with mixture of multinomials. Proceedings of Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-04) 2004. Postscript.
2. D. Pavlov, E. Manavoglu, D. Pennock, C. Lee Giles. Collaborative Filtering with Maximum Entro py. IEEE Intelligent Systems, Special Issue on Mining the Web Actionable Knowledge. 2004. Postscript.
3. E. Manavoglu, D. Pavlov, C. Lee Giles. Probabilstic User Behavior Models. Proceedings of the Third IEEE International Conference on Data Mining (ICDM-03). Melbourne FL, 2003. PDF.
4. D. Pavlov. Sequence Modeling with Mixtures of Conditional Maximum Entropy Distributions. Proceedings of the Third IEEE Interna tional Conference on Data Mining (ICDM-03). Melbourne FL, 2003. Postscript.
5. D. Pavlov, A. Popescul, D. Pennock and L. Ungar. Mixtures of Conditional Maximum Entropy Models. Proceedings of the 2003 International Conference on Machine Learning (ICML-03). Washington DC, 2003. Postscript.
6. D. Pavlov, D. Pennock. A Maximum Entropy Approach To Collaborative Filtering in Dynamic, Sparse, High-Dimensional Domains. In Advances in Neural Information Processing 15 (NIPS-02). Postscript.
7. D. Pavlov, P. Smyth. Adaptive Approximate Querying of Large Sparse Binary Data Sets via Probabilistic Model Averaging. Proc. of the SIAM International Conference on Data Mining, San Francisco, 2003. Postscript.
8. D. Pavlov. Probabilistic Query Models for Transaction Data. PhD dissertation. University of California, Irvine, 2002. Postscript (180 pages, 2.3Mb). Gzipped Postscript (660K).
9. D. Pavlov, P. Smyth. Probabilistic Query Models for Transaction Data. Proc. of Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-01), San Francisco, 2001. Postscript.
10. D. Pavlov, H. Mannila, P. Smyth. Beyond Independence: Probabilistic Models for Query Approximation on Binary Transaction Data. Technical Report UCI-ICS TR-01-09, Information and Computer Science Department, UC Irvine, January 2001 (Accepted to IEEE Transactions on Data and Knowledge Engineering). Postscript.
11. D. Pavlov, D. Chudova, P. Smyth. Towards Scalable Support Vector Machines Using Squashing. Proc. of Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-00), Boston, 2000. Postscript.
12. D. Pavlov, H. Mannila, P. Smyth. Probabilistic Models for Query Approximation with Large Sparse Binary Data Sets. Proc. of Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI-00), Stanford, 2000. Postscript.
13. D. Pavlov, J. Mao, B. Dom. Scaling-up Support Vector Machines Using Boosting Algorithm. Proc. of International Conference on Pattern Recognition, Barcelona 2000. Postscript.
14. H. Mannila, D. Pavlov, P. Smyth. Prediction with Local Patterns Using Cross-Entropy. Proc. of Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-99), San Diego, 1999. Gzipped Postscript.
15. X. Ge, S. Gaffney, D. Pavlov, P. Smyth, Local context matching for page replacement Technical Report UCI-ICS 99-37, Sept 1999. Gzipped Postscript.
16. D. I. Chudova, S. A. Dolenko, Yu. V. Orlov, D. Yu. Pavlov, I. G. Persiantsev. Benchmarking of Different Modifications of the Cascade Correlation Algorithm. Proc. 3rd International Conference on Adaptive Computing in Design and Manufacture, 1998, pp.339-344.
17. Yu.V.Orlov, I.G.Persiantsev, D.I.Chudova, D.Yu.Pavlov, S.M.Babichenko. Development of a Statistics Based System for Fluorescent Diagnostics of Organic Pollution in Water. Proc.3rd EARSeL Workshop on Lidar Remote Sensing of Land and Sea, 1997, pp.157-162.
18. Dmitry Pavlov Application of Neural Networks to Plasma Boundary Determination (in Russian) //Achievements of Science and Technology. Modern Mathematics' Problems. Thematical Reviews, 1995, (translated into English in Alerton Press Inc.)
19. Dmitry Pavlov Neural Networks in Solution of an Inverse Coefficient Heat Conduction Problem (in Russian). //Vestnik Moskovskogo Universiteta, 4, 1994 (translated into English in Alerton Press Inc.)

 

LINKS

• NeuroProject AI Software Company - a Moscow State University based firm that I was working for a long time.
• Web page of the Department of Heat and Mass Transfer Processes and Devices of the Moscow Power Engineering Institute, which is led by my Dad - Professor Yuri Pavlov, Doctor of Technical Sciences, State Prize Laureate