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PhD Dissertation

Privacy preserving distributed data mining based on multi-objective optimization and algorithmic game theory.

Journals

  1. B. Matthews, S. Das, K. Bhaduri, K. Das, R. Martin, N. Oza. Discovering Anomalous Aviation Safety Events using Scalable Data Mining Algorithms. Journal of Aerospace Information Systems. Volume 10 Number 10, pp. 467-475. October 2013. [pdf]

  2. K. Das, A. N. Srivastava. Sparse Inverse-Kernel Gaussian Process Regression. Statistical Analysis and Data Mining Journal. Volume 6 Issue 3, pp. 205-220. June 2013. [pdf]

  3. K. Das, K. Bhaduri, P. Votava. Distributed Anomaly Detection using 1-class SVM for Vertically Partitioned Data. Statistical Analysis and Data Mining Journal. Volume 4 Issue 4, pp. 393-406 August 2011. [pdf]

  4. K. Bhaduri, K. Das, K. Borne, C. Giannella, T. Mahule, H. Kargupta. Scalable, Asynchronous, Distributed Eigen-Monitoring of Astronomy Data Streams. Statistical Analysis and Data Mining Journal. Volume 4, Issue 3, pp. 336-352, June 2011. [pdf]

  5. K. Das, K. Bhaduri, H. Kargupta. Multi-objective Optimization Based Privacy Preserving Distributed Data Mining in Peer-to-peer Networks. Peer-to-Peer Networking and Applications. Volume 4, Issue 2, pp. 192-209. 2011. [pdf]

  6. K. Das, K. Bhaduri, H. Kargupta. A Local Asynchronous Distributed Privacy Preserving Feature Selection Algorithm for Large Peer-to-Peer Networks. Knowledge and Information Systems Journal. Volume 24, Issue 3, pp. 341-367. September 2010. [pdf]

  7. K. Das, K. Bhaduri, K. Liu, H. Kargupta. Distributed Identification of Top-l Inner Product Elements and its Application in a Peer-to-Peer Network. IEEE Transactions on Knowledge and Data Engineering. Volume 20, Issue 4, pp. 475-488, April 2008. [pdf]

  8. K. Liu, K. Bhaduri, K. Das, P. Nguyen, H. Kargupta. Client-side Web Mining fo Community Formation in Peer-to-Peer Environments. SIGKDD Explorations. Volume 8, Issue 2, pp. 11-20. December 2006. [pdf]

Conferences and Workshops

  1. A. Asanjan, K. Das, A. Li, V. Chirayath, J. Torres-Perez, S. Sorooshian. Learning Instrument Invariant Characteristics for Generating High-resolution Global Coral Reef Maps. ACM SIGKDD (KDD) 2020, Accepted. 2020. [pdf].

  2. A. Kodali, M. Szubert, K. Das, S. Ganguly, J. Bongard. Understanding climate-vegetation interactions in global rainforests through a GP-tree analysis. Parallel Problem Solving in Nature (PPSN) 2018, pp 525-536. 2018. [pdf].

  3. A. Basak, K. Das, O. Mengshoel. CADDeLaG: Framework for distributed anomaly detection in large dense graph sequences. arXiv preprint arXiv:1802.05421. 2018.

  4. K. Das, I. Avrekh, B. Matthews, M. Sharma, N. Oza. ASK-the-Expert: Active learning based knowledge discovery using the expert. ECML-PKDD 2017, pp. 395-399, Skopje, Macedonia. [pdf].

  5. M. Sharma, K. Das, M. Bilgic, B. Matthews, D. Nielsen, N. Oza. Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation. ECML-PKDD 2016, pp. 209-225, Riva del Garda, Italy. [pdf].

  6. M. Szubert, A. Kodali, S. Ganguly, K. Das, J. Bongard. Semantic Forward Propagation for Symbolic Regression, PPSN 2016, pp. 364-374, Dublin, Ireland. [pdf].

  7. M. Szubert, A. Kodali, S. Ganguly, K.Das, J. Bongard. Reducing Antagonism between Behavioral Diversity and Fitness in Semantic Genetic Programming, GECCO 2016, pp. 797-804, Denver, CO. [pdf].

  8. A. Kodali, M. Szubert, S. Ganguly, J. Bongard, K. Das. Regression based modeling of vegetation and climate variables for the Amazon rainforests, AGU Fall Meeting, 2015. San Francisco, CA. Poster presentation. [pdf].

  9. K. Das. Evaluation of Big Data Containers for Popular Storage, Retrieval, and Computation Primitives in Earth Science Analysis, AGU Fall Meeting, 2015. San Francisco, CA. Poster presentation. [pdf].

  10. K. Das, K. Bhaduri, B. Matthews, and N. Oza. Large scale support vector regression for aviation safety. IEEE BigData 2015, pp. 999-1006, Santa Clara, CA. [pdf].

  11. C. Basu, C. Koehler, K. Das, and A. Dey. PerCCS: Person-Count from Carbon dioxide using Sparse Non-negative Matrix Factorization. UbiComp 2015, pp. 987-998, Osaka, Japan. [pdf].

  12. K. Das, S. Agrawal, G. Atluri, S. Liess, M. Steinbach, and V. Kumar. Analyzing Global Climate System Using Graph Based Anomaly Detection, Stochastic Modeling and Complex System Approaches to Nonlinear Geophysical Systems Session I, AGU Fall Meeting, 2014. San Francisco, CA. (Oral presentation) [pdf]

  13. N. C. Oza, V. Kumar, R. R. Nemani, S. Boriah, K. Das, A. Khandelwal, B. Matthews, A. Michaelis, V. Mithal, G. Nayak, P. Votava. Integrating Parallel and Distributed Data Mining Algorithms into the NASA Earth Exchange (NEX). AGU Fall Meeting, 2014. San Francisco, CA. Poster. 2014. [pdf]

  14. S. Kumar, K. Das. Localizing anomalous changes in time-evolving graphs, Proceedings of ACM SIGMOD 2014, pp. 1347--1358. Snowbird, Utah. [pdf]

  15. K. Das, K. Bhaduri, N. Oza. ParitoSVR: Parallel Iterated Optimizer for Support Vector Regression in the Primal. Workshop on Optimization Methods for Anomaly Detection, pp. 1-3. 2014. Philadelphia, PA. [pdf]

  16. M. Stolpe, K. Bhaduri, K. Das, K. Morik. Anomaly Detection in Large Datasets by Vertically Distributed Core Vector Machines. Proceedings of ECML-PKDD, Part III, LNAI 8190, pp. 321--336. 2013. Prague, Czech Republic. [pdf]

  17. K. Bhaduri, K. Das, B. Matthews. Detecting Abnormal Machine Characteristics in Cloud Infrastructures. Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops, Vancouver, Canada. pp. 137-144, 2011. [pdf]

  18. K. Das, A. N. Srivastava. Sparse Inverse Gaussian Process Regression with Application to Climate Network Discovery. NASA Conference on Intelligent Data Understanding, Mountain View, CA. pp 233- 247. 2011. [pdf]

  19. K. Bhaduri, K. Das, C. Giannella. Distributed Monitoring of the R2 Statistic for Linear Regression. 11th SIAM International Conference on Data Mining, Phoenix, AZ. pp. 438-449. 2011. [pdf]

  20. K. Das, A. Srivastava. Block-GP: Scalable Gaussian Process Regression for Multimodal Data. 10th IEEE International Conference on Data Mining, Sydney, Australia. pp. 791-796. 2010. [pdf]

  21. K. Bhaduri, K. Das, P. Votava. Distributed Anomaly Detection using Satellite Data From Multiple Modalities. NASA Conference on Intelligent Data Understanding, Mountain View, CA. pp. 109-123. 2010. [pdf]

  22. K. Das, K. Bhaduri, H. Kargupta. A Distributed Asynchronous Local Algorithm using Multi-party Optimization based Privacy Preservation. IEEE International Conference on Peer-to-Peer Computing, Seattle. pp. 212-221. 2009. [pdf] (Invited for fast track submission to Springer Journal on Peer-to-Peer Networking and Applications (PPNA), as one of the outstanding papers of P2P'09 )

  23. K. Das, K. Bhaduri, S. Arora, W. Griffin, K. Borne, C. Giannella, H. Kargupta. Scalable Distributed Change Detection from Astronomy Data Streams using Local, Asynchronous Eigen Monitoring Algorithms. In The Ninth SIAM International Conference on Data Mining (SDM). pp 247 - 258. 2009. [pdf]

  24. K. Borne, H. Kargupta, K. Das, W. Griffin, C. Giannella. Scalable Scientific Data Mining in Distributed, Peer-to-Peer Environments. American Geophysical Union (AGU) Fall Meeting, 2008, San Francisco. [pdf]

  25. K. Das, W. Griffin, H. Kargupta, C. Giannella, Kirk Borne. Scalable Multi-Source Astronomy Data Mining in Distributed, Peer-to-Peer Environments. Astronomical Data Analysis Software & Systems (ADASS), 2008, Montreal, Canada. [pdf]

  26. H. Kargupta, K. Das, K. Liu. Multi-party, Privacy-Preserving Distributed Data Mining Using a Game Theoretic Framework . In 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD). pp 523-531. 2007. [pdf] (Nominated for the PET award)

  27. K. Bhaduri, K. Das, H. Kargupta. Peer-to-Peer Data Mining. Autonomous Intelligent Systems: Agents and Data Mining. V. Gorodetsky, C. Zhang, V. Skormin, L. Cao (Editors), LNAI 4476, Springer. pp. 1-10. 2007. [pdf]

  28. R. Dutton, P. Hu, K. Das, T. Gilbert, Y. Xiao. Can Temperature Probe Removal Be a Reliable Indicator for Case Finishing? American Society of Anesthesiologist (ASA) Annual Meeting. San Francisco. 2007. [pdf]

  29. K. Das, K. Liu and H. Kargupta. A Game Theoretic Perspective Toward Practical Privacy Preserving Data Mining. In National Science Foundation Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation. Baltimore, Maryland. 2007. [jpg]

  30. K. Liu, K Bhaduri, K. Das, P. Nguyen, H. Kargupta. Client-side Web Mining for Community Formation in Peer-to-Peer Environments. SIGKDD workshop on web usage and analysis (WebKDD). Philadelphia, Pennsylvania, USA. 2006. [pdf] (Selected as the most interesting paper from the WebKDD workshop)

  31. K. Das, P. Hu, Y. Xiao, M. Wasei. Reducing Uncertainty in Operating Room Management. In OR of the Future retreat. Columbia, Maryland. 2006. [pdf]

Book Chapters

  1. M. Stolpe, K. Bhaduri, K. Das. Distributed Support Vector Machines: An Overview. A chapter in Solving Large Scale Learning Tasks. Challenges and Algorithms. Springer Publishing House. 2016

  2. K. Das, K. Bhaduri, Parallel and Distributed Data Mining for Astronomy Applications. In Data Mining and Machine Learning for Astronomical Applications. Edited by K. Ali, A. Srivastava, J. Scargle and M. Way, CRC Press, 2010.

  3. K. Liu, K. Das, T. Grandison, H. Kargupta, Privacy-Preserving Data Analysis on Graphs and Social Networks. In Next Generation Data Mining. Edited by H. Kargupta, J. Han, P. Yu, R. Motwani, and Vipin Kumar, CRC Press, 2008. [pdf]

  4. K. Bhaduri, K. Das, K. SivaKumar, H. Kargupta. Algorithms for Distributed Data Stream Mining. A chapter in Data Streams: Models and Algorithms, Edited by C. Aggarwal, Springer. pp. 309-332. 2006.

 

I did my M.S. thesis in Bioinformatic Visualization. Here is a copy of my thesis.