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Books
 A. N. Srivastava and M. Sahami eds., “Text Mining: Classification, Clustering, and Applications”, Taylor and Francis, 2009.
 A. N. Srivastava and J. Han, eds., “Machine Learning and Knowledge Discovery for Systems Health Management,” Taylor and Francis, 2011.
 M. Way, J. Scargle, K. Ali, and A. N. Srivastava, eds., “Advances in Machine Learning and Data Mining for Astronomy,” Taylor and Francis, 2011.
 A. N. Srivastava, R. Nemani, and K. Steinhauser, eds, “LargeScale Machine Learning in the Earth Sciences,” Taylor and Francis, 2017.
Journal

 L. F. Goodrich and A. N. Srivastava, “Software Techniques to Improve Data Reliability in Superconductor and LowResistance Measurements”, Journal of Research of the National Institute of Standards and Technology, 95, 575589, (1990).
 L. F. Goodrich and A. N. Srivastava, “Comparison of Transport Critical Current Measurement Methods,” Advanced Cryogenics Engineering Materials, 38B., 559566, (1992)
 L. F. Goodrich and A. N. Srivastava, and T. C. Stauffer, “Simulators of Superconductor Critical Current: Design, Characteristic and Applications,” Journal of Research of the National Institute of Standards and Technology, 96, 703724, (1990).
 L. F. Goodrich and A. N. Srivastava, “CriticalCurrent Simulation and Data Acquisition,” Superconductor Industry Magazine, spring 1992, pp. 2836.
 L. F. Goodrich and A. N. Srivastava, “Critical Current Measurement Methods: Quantitative Evaluation,” Versailles Project on Advanced Materials and Standards (VAMAS), Cryogenics, Vol. 35, VAMAS Supplement, pp S19S23, 1995
 L. F. Goodrich and A. N. Srivastava, “Thermal Contraction of FiberglassEpoxy Sample Holders Used for Nb3Sn CriticalCurrent Measurements,” Versailles Project on Advanced Materials and Standards, Cryogenics, 1994.
 L. F. Goodrich and A. N. Srivastava, M. Yuyama, and H. Wada, “nValue and Second Derivative of the Superconductor Voltage Current Characteristic,” IEEE Transactions on Applied Superconductivity, Vol. 3, No. 1, pp. 12651268, 1993.
 L. F. Goodrich and A. N. Srivastava, “Standard Reference Devices for High Temperature Superconductor CriticalCurrent Measurements,” Cryogenics, 33, 11421148, 1993.
 L. F. Goodrich, A. N. Srivastava, T. C. Stauffer, A. Roshko, and L. R. Vale, “High Current Pressure Contacts to Ag Pads on Thin Film Superconductors”, IEEE Transactions on Applied Superconductivity, Vol. 3, No. 2, 6163, June 1994.
 L. F. Goodrich, J. A. Wiejackza, A. N. Srivastava, and T. C. Stauffer, “Superconductor Critical Current Measurement Standards for Fusion Applications,” NISTIR1994, Boulder, CO.
 L. F. Goodrich, J. A. Wiejackza, T. C. Stauffer, and L. T. Medina, USA Interlaboratory Comparison of Superconductor Simulator Critical Current Measurements, IEEE Transactions on Applied Superconductivity, 5, Part 1 (2): pp. 548551; June, 95.
 L. F. Goodrich, J. A. Wiejackza, T. C. Stauffer, and L. T. Medina, First VAMAS USA Interlaboratory Comparison of High Temperature Superconductor Critical Current Measurements, IEEE Transactions on Applied Superconductivity, 5, Part 1 (2): pp. 552555; June, 95.
 L. F. Goodrich, J. A. Wiejackza, and A. N. Srivastava, “Anomalous Switching Phenomenon in Critical Current Measurements When Using Conductive Mandrels” IEEE Trans. On Applied Superconductivity, Vol 5, No. 3, pp. 34423444, 1995.
 A. S. Weigend and A. N. Srivastava, “Predicting Probability Distributions, A Connectionist Approach,” International Journal of Neural Systems, 6, pp. 109118, 1995.
 A. S. Weigend, M. Mangeas, and A. N. Srivastava, “Nonlinear Gated Experts for Time Series: Discovering Regimes and Avoiding Overfitting,” International Journal Neural Systems, vol. 6, pp. 373399, 1996.
 A. N. Srivastava, “Segmenting Noisy Time Series using Scale Sensitive Gated Experts with Applications to Signal Processing,” Ph.D. Dissertation, University of Colorado, Department of Electrical and Computer Engineering, 1996.
 A. N. Srivastava and A. S. Weigend, “Special Issue: Data Mining in Finance,” International Journal of Neural Systems, vol. 6, 1998.
 A. N. Srivastava, “Data Mining in Semiconductor Yield Forecasting: Enhancement of Manufacturing Productivity,” Future Fab International Magazine, Volume 7, 1999.
 A. N. Srivastava, R. Su, and A. S. Weigend, “Data Mining for Features using Scale Sensitive Gated Experts,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 21 (12), December 1999.
 A. N. Srivastava, N. C. Oza, and J. Stroeve, “Virtual Sensors: Using Data Mining Techniques to Efficiently Estimate Remote Sensing Spectra,” Special Issue on Advanced Data Analysis, IEEE Transactions on Geoscience and Remote Sensing, Vol 43 (3), March 2005.
 M. Way and A. N. Srivastava, “Novel Methods for Predicting Photometric Redshifts from Sloan Digital Sky Survey Photometry”, Astrophysical Journal, 647:102115, 2006.
 S. Budalakoti, A. N. Srivastava, and M. Otey, “Detecting and Diagnosing Anomalies in HighDimensional Symbol Sequences with Applications to Airline Safety,” IEEE Transactions on Systems Man and CyberneticsC, 39 (1), 2009.
 S. Das, A. Chattopadhyay, and A. N. Srivastava, “Classifying Induced Damage in Composite Plates using One Class Support Vector Machines,” AIAA Journal, Volume 48, Number 4, April 2010.
 A. N. Srivastava and S. Das, “Detection and Prognostics for LowDimensional Systems,” IEEE Transactions on Systems Man and CyberneticsC, 39 (1), 2009.
 C. Milesi, A. Samanta, H. Hashimoto, K. K. Kumar, S. Ganguly, P. S. Thenkabail, A. N. Srivastava, R. R. Nemani, and R. B. Myneni, “Decadal Variations in NDVI and Food Production in India”, Remote Sensing, 2, 758776, 2010.
 L. Foster, A, A. Waagen, N. Aijaz, M. Hurley, A. Luis, J. Rinsky, C. Satyavolu, M. J. Way, P. Gazis, and A. N. Srivastava, “Stable and Efficient Gaussian Process Calculations” Journal of Machine Learning Research, 10(Apr):857–882, 2009.
 B. Matthews, A. N. Srivastava, D. Iverson, B. Beil, and B. Lane, “Multidimensional Anomaly Detection on the Space Shuttle Main Propulsion System: A Case Study”, in press, IEEE Systems, 2011.
 M. J. Way, L. Foster, P. R. Gazis, and A. N. Srivastava, “New Approaches to Gaussian Process Regression in the Sloan Digital Sky Survey,” Astrophysical Journal, Volume 706, Number 1, 2009.
 A. N. Srivastava, C. Meyer, and R. Mah, “A Survey of Integrated Vehicle Health Management: Tools and Technologies for Aerospace Applications” AIAA Encyclopedia of Aerospace Systems, 2010 (invited).
 D. Zhang, C. Zhai, J. Han, A. N. Srivastava, and N. C. Oza, “Topic Modeling for OLAP on Multidimensional Text Databases: Topic Cube and its Applications“, Statistical Analysis and Data Mining, Volume 2, pp. 378395, December 2009.
 X. Lin, B. Ding, J. Han, N. C. Oza, A. N. Srivastava, B. Zhao and F. Zhu, “Text Cube: Computing IR Measures for Multidimensional Text Database Analysis“, submitted to IEEE Trans. on Knowledge and Data Engineering, 2009.
 K. Bhaduri, M. Stefanski, and A. N. Srivastava, “Privacy Preservation through Random Nonlinear Distortion”, IEEE Transactions on Systems, Man and Cybernetics, Part B. Volume 41, Issue 1, pp. 260272. 2011
 M. Gariel, A. N. Srivastava, and E. Feron, “Trajectory Clustering and an Application to Airspace Monitoring”, IEEE Transactions on Intelligent Transportation Systems, 2011.
 A. N. Srivastava and J. Han, “Machine Learning and Knowledge Discovery for Systems Health Management, An Introduction,” Machine Learning and Knowledge Discovery for Systems Health Management, Taylor and Francis, 2011.
 B. Ding, B. Zhao, C. X. Lin, J. Han, C. Zhai, A. N. Srivastava, and N. C. Oza, “Efficient KeywordBased Search for TopK Cells in Text Cube”, IEEE Transactions on Knowledge and Data Engineering, 2011.
 A. N. Srivastava, D. Mylaraswamy, R. Mah, and E. Cooper, “Vehicle Level Reasoning Systems: Concept and Future Directions,” Society of Automotive Engineers Integrated Vehicle Health Management Book, Ian Jennions, Ed., 2011.
 M. M. Masud, Q. Chen, L. Khan, C. C. Aggarwal, J. Gao, J. Han, A. N. Srivastava, and N. C. Oza, “Adaptive Classification and Novel Class Detection of FeatureEvolving Data Streams,” submitted to IEEE Transactions on Knowledge Discovery and Data Engineering, 2011.
 A. N. Srivastava, “Greener Aviation with Virtual Sensors: A Case Study”, Data Mining and Knowledge Discovery, 24:443471, 2012.
 A. N. Srivastava, “Predicting Adverse Events in NextGen,” in preparation for AIAA Journal, 2012.
 K. Das and A. N. Srivastava, “Sparse Inverse Gaussian Process Regression with Applications to Climate Network Discovery”, in preparation for Statistical Analysis and Data Mining, a Journal of the American Statistical Association, 2012 (invited, working title).
 L. El Ghaoui, G. C. Li, V. Duong, V. Pham, A. N. Srivastava, and K. Bhaduri, “Sparse Machine Learning Methods for Understanding Large Text Corpora”, submitted to Statistical Analysis and Data Mining, a Journal of the American Statistical Association, 2012 (invited, working title).
 J. Schumann, T. Mbaya, O. Mengshoel, K. Pipatsrisawat, A. N. Srivastava, A. Choi, and A. Darwiche, “Software Health Management with Bayesian Networks,” submitted to Innovations in Systems and Software Engineering, 2012.
 J. Schumann and A. N. Srivastava, “Software Health Management: A Necessity for Safety Critical Systems”, submitted to Innovations in Systems and Software Engineering, 2012.
 S. Das, K. Bhaduri, N. C. Oza, and A. N. Srivastava, “nuAnomica: A Fast Support Vector based Novelty Detection Technique”, submitted to IEEE Transactions on Systems, Man, and Cybernetics, Part B.
 B. Matthews, S. Das, K. Bhaduri, K. Das, R. Martin, N. C. Oza, J. Stutz, and A. N. Srivastava, “Discovering Anomalous Aviation Safety Events using Scalable Data Mining Algorithms,” submitted to the ACM SIGKDD Knowledge Discovery and Data Mining Conference, 2012.
 A. N. Srivastava, R. Bharadwaj, D. Mylaraswamy, and R. Mah, “Vehicle Level Reasoning Systems: Realtime Diagnostics, Prognostics, and Data Mining,” SAE International Book on IVHM Applications, 2012.
 L. Li, S. Das, R. J. Hansman, R. Palacios, and A. N. Srivastava, “Analysis of Flight Recorder Data Using Clustering Techniques for Detecting Abnormal Operations,” AIAA Journal of Aerospace Information Systems, 2015.
Conference and Technical Publications

 L. F. Goodrich and A. N. Srivastava, “Trends in Superconductor CriticalCurrent Measurement Technology in the USA,” Proceedings of the International Symposium on Prestandards Research for Advanced Materials, 297300, Dec. 1991, Tokyo, Japan.
 L. F. Goodrich and A. N. Srivastava, “Trends in Superconductor CriticalCurrent Measurement Technology in the USA,” Proceedings of the 4th Annual US Japan Workshop on High Temperature Superconductivity, Nov. 1992, Washington, D.C.
 L. F. Goodrich and A. N. Srivastava, “Standardization of CriticalCurrent Measurements on Nb3Sn Wires,” Proceedings of the Conference on US Japan Cooperation, Madison, Wisconsin, March 1993.
 A. N. Srivastava and R. Su, “ Associative and Non associative Neural Network Design for Somatosensory Integration,” Proceedings of the IEEE Engineering in Medicine and Biology Conference, San Diego, CA, October 1993.
 D. Aized, J. W. Haddad, C. H. Joshi, L.F. Goodrich, and A. N. Srivastava, “Comparing the accuracy of criticalcurrent measurements using the voltagecurrent simulator,” to be published, Proceedings of the Thirteenth International Conference on Magnet Technology, British Columbia, Canada, Sept. 2024, 1993.
 L. F. Goodrich and A. N. Srivastava, “A Simple and Repeatable Technique for Measuring the Critical Current of Nb3Sn Wires,” Proceedings of the 7th International Workshop on Critical Currents in Superconductors, World Scientific Publishing CO. PTE. LTD., Alpbach, Austria, 1994.
 A N. Srivastava and A. S. Weigend, “Computing the Probability Density in Connectionist Regression,” Proceedings of the International Conference on Artificial Neural Networks (ICANN), Sorrento, Italy, May 2629, 1994, SpringerVerlag, pp. 685688.
 A. N. Srivastava and W. Buntine, “Data Analysis of Components in the Optical Plume of the Space Shuttle Main Engine,” Proceedings of the AIAA Electrochemical Conference, San Antonio, Texas, 1995.
 A. S. Weigend and A. N. Srivastava (1996) Modeling, Learning, and Meaning: Extracting Regimes from Time Series. In: Melecon ’96 (8th Mediterranean Electrotechnical Conference, May 1316, 1996, Bari, Italy), pp. 9599. Piscataway, NJ: IEEE Service Center.
 A. N. Srivastava, and J. Stroeve, “Snow, Ice, and Cloud Detection using Kernel Clustering,” Proceedings of the Workshop on Machine Learning for Autonomous Space Applications, International Conference on Machine Learning, 2003
 A. N. Srivastava, J. C. Stroeve, and N. Oza, “Using Kernel Methods to Detect Clouds, Snow, Ice and other Geophysical Processes,” American Geophysical Union, San Francisco, CA, 2003.
 A. Lotsch, M. Friedl, A. Anderson, C. Tucker, and A. N. Srivastava, “Linking OceanAtmosphere Dynamics to PrecipitationVegetation Covariability,” American Geophysical Union, San Francisco, CA, 2003.
 A. N. Srivastava, “Mixture Density Mercer Kernels: A Method to Learn Kernels Directly from Data”, Proceedings of the 2004 SIAM Data Mining Conference, Orlando FL.
 A. N. Srivastava and N. Oza, “Knowledge Driven Image Mining with Mixture Density Mercer Kernels,” European Space Agency Special Publication #553, Proceedings of the European Image Information Mining Coordination Group, Madrid, Spain 2004
 A. N. Srivastava and B. ZaneUlman, “Discovering Hidden Anomalies in Text Reports Regarding Complex Space Systems”, IEEE Aerospace Conference, Big Sky, MT, 2005.
 A. N. Srivastava, J. Schumann, and B. Fischer, “An Ensemble Approach to Building Mercer Kernels with Prior Information”, submitted to IEEE Systems Man and Cybernetics Conference Workshop, 2005.
 A. N. Srivastava, “Discovering Anomalies in Sequences with Applications to System Health,” Proceedings of the 2005 Joint Army Navy NASA Air Force Interagency Conference on Propulsion, Charleston SC, 2005.
 N. C. Oza, A. N. Srivastava, and J. Stroeve, “Improvements in Virtual Sensors: Using Spatial Information to Estimate Remote Sensing Spectra,” IEEE International Geoscience and Remote Sensing Symposium Proceedings, Seoul Korea, 2005.
 A. N. Srivastava, R. Akella, et. al., “Enabling the Discovery of Recurring Anomalies in Aerospace System Problem Reports using HighDimensional Clustering Techniques”, 2006 Proceedings of the IEEE Aerospace Conference.
 S. Budalakoti, A. N. Srivastava, R. Akella, “Discovering Atypical Flights in Sequences of Discrete Flight Parameters,” 2006 Proceedings of the IEEE Aerospace Conference, 2006.
 A. N. Srivastava and R. Nemani, “Quantifying Seasonal Variation in Cloud Cover with Predictive Models,” Invited Talk at American Geophysical Union, 2005.
 S. Das, A. N. Srivastava, and A. Chattopadhyay, “OneClass SVMs Based Classification of Damage Signatures in Composite Plates”, accepted for publication in the Proceedings of the 2007 IEEE Aerospace Conference.
 A. N. Srivastava, M. J. Way, and P. Gazis, “Calculating Photometric Redshifts Using Nonlinear Regression Methods: A Comparative Study”, 2006 Fall AGU Meeting, San Francisco.
 R. Martin, M. Schwabacher, N. Oza, and A. N. Srivastava, “Comparison of Unsupervised Anomaly Detection Methods for Systems Health Management using Space Shuttle Main Engine Data”, Joint Army Navy NASA Air Force Conference on Propulsion Systems, May 2007. (Note: This work received the Outstanding Technical Achievement in Liquid Propulsion Systems, Joint Army Navy NASA Air Force Conference on Propulsion)
 A. N. Srivastava, B. Matthews, and S. Das, “Novel Methods for Spectral Signal Decomposition with Applications to Optical Spectroscopy,” accepted to the proceedings of the Joint Army Navy NASA Air Force Conference on Propulsion Systems, Dec. 2008.
 B. Matthews and A. N. Srivastava, “A Comparison of Anomaly Detection Methods on a Ground Test Firing of a Subscale SRM,” accepted to the proceedings of the Army Navy NASA Air Force Conference on Propulsion Systems, Dec. 2008.
 K. Bhaduri and A. N. Srivastava, “A Local Scalable Distributed Expectation Maximization Algorithm for Large PeertoPeer Networks”, Proceedings of the IEEE International Conference on Data Mining, 2009.
 D. Christensen, S. Das, and A. N. Srivastava, “Highly Scalable Matching Pursuit Signal Decomposition Algorithm”, accepted at the International Workshop on Structural Health Management, Stanford, CA, 2009.
 D. Gorinevsky, R. Mah, and A. N. Srivastava, “Integration Architecture for the IVHM Project,” accepted at the 2009 Integrated Systems Health Management Conference, AFRL, 2009.
 S. Das, K. Bhaduri, N. Oza, A. N. Srivastava, “nuAnomica: A Fast Support Vector based Novelty Detection Technique”, Proceedings of the IEEE International Conference on Data Mining, 2009.
 S. Das, B. Matthews, K. Bhaduri, and A. N. Srivastava, “Detecting Anomalies in Multivariate Data Sets with Switching Sequences and Continuous Streams,” Neural Information Processing Systems Workshop on Multiple Kernel Learning Techniques, 2009 (poster).
 A. N. Srivastava, N. V. Chawla, P. S. Yu, and P. Melby (eds), “Proceedings of the 2010 Conference on Intelligent Data Understanding,” CIDU 2010, October 56, 2010, Mountain View, CA, USA.
 B. Matthews and A. N. Srivastava, “Adaptive Fault Detection on Liquid Propulsion Systems with Virtual Sensors: Algorithms and Architectures”, Joint Army Navy NASA Air Force Conference on Propulsion Systems, Integrated Health Management Panel, 2010.
 R. M. Mastrapa, J. W. Barnes, B. Matthews, B. OuYang, A. N. Srivastava, “Ammonia on Enceladus,” American Geophysical Union, 2009.
 S. Das, B. Matthews, A. N. Srivastava, and N. C. Oza, “Multiple Kernel Learning for Heterogeneous Anomaly Detection: Algorithm and Aviation Safety Case Study,” Proceedings of the ACM SIGKDD Knowledge Discovery and Data Mining Conference, 2010.
 K. Das and A. N. Srivastava, “BlockGP: Scalable Gaussian Process Regression for Multimodal Data”, Proceedings of the IEEE International Conference on Data Mining, 2010.
 K. Bhaduri, Q. Zhu, N. Oza, and A. N. Srivastava, “Fast and Flexible Multivariate Time Series Subsequence Search”, Proceedings of the IEEE International Conference on Data Mining, 2010.
 J. Schumann, A. N. Srivastava, and O. J. Mengshoel, “Who guards the Guardians? Toward V&V of Health Management Software,” Proceedings of the Runtime Verification Conference, 2010.
 J. Schumann, O. J. Mengshoel, A. N. Srivastava, and A. Darwiche, “Towards Software Health Management with Bayesian Networks,” Proceedings of the 2010 Workshop on the Future of Software Engineering.
 A. N. Srivastava and J. Schumann, “The Case for Software Health Management”, IEEE Systems, Man, and Cybernetics Conference on Information Technology, August 2011.
 A. N. Srivastava and P. Fabiani, “NASAONERA Collaboration on Human Factors in Aviation Accidents and Incidents: First Annual Report,” NASA Technical Publication, 2011.
 A. N. Srivastava and P. Barton, “NASAEasyJet Collaboration on the Human Factors Monitoring Program (HFMP) Study,” NASA Technical Publication, 2011.
 K. Das and A. N. Srivastava, “Sparse Inverse Gaussian Process Regression with Applications to Climate Network Discovery”, Proceedings of the Conference on Intelligent Data Understanding, October 2011.
 L. El Ghaoui, G. C. Li, V. Duong, V. Pham, A. N. Srivastava, and K. Bhaduri, “Sparse Machine Learning Methods for Understanding Large Text Corpora”, Proceedings of the Conference on Intelligent Data Understanding, October 2011.
 A. N. Srivastava, N. V. Chawla, A. S. Perera (eds), “Proceedings of the 2011 Conference on Intelligent Data Understanding,” CIDU 2011, October 1921, 2011, Mountain View, CA, USA.
 I. Statler, A. N. Srivastava and P. Fabiani, “NASAONERA Collaboration on Human Factors in Aviation Accidents and Incidents: Second Annual Report,” NASA Technical Publication, 2012, in preparation.
 I. Statler, A. N. Srivastava and P. Barton, “NASAEasyJet Collaboration on the Human Factors Monitoring Program (HFMP) Study: Second Annual Report,” NASA Technical Publication, 2012, in preparation.
 J. Jenkins, S. McCauliff, C. Burke, J. Twicken, T. Klaus, D. Sanderfer, A. N. Srivastava, M. Haas, “AutoVetting Transiting Planet Candidates Identified by the Kepler Science Pipeline,” submitted to the International Astronomical Union, Beijing, China, March 2012.
 T. Woodbury and A. N. Srivastava, “Analysis of Virtual Sensors for Predicting Aircraft Fuel Consumption”, AIAA Infotech Conference, Anaheim CA, 2012.
 S. Das, S. Sarkar, A. Ray, A. N. Srivastava, D. L. Simon, “Anomaly Detection in Flight Recorder Data: A Dynamic DataDriven Approach,” submitted to the American Control Conference, 2013.
 S. Das, L. Li, A. N. Srivastava, and R. J. Hansman, “Comparison of Algorithms for Anomaly Detection in Flight Recorder Data of Airline Operations,” Proceedings of the 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, Indianapolis, Indiana, 17 – 19 September 2012.
 B. Matthews, J. Schade, N. Oza, and A. N. Srivastava, “Discovery of Abnormal Flight Patterns in Radar Track Data: A Case Study,” in preparation for the Tenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2013).
 A. Rider and A. N. Srivastava, “Dynamic Topic Models for Trends in Aviation Safety,” Extended Abstract and Poster Presentation, Conference on Intelligent Data Understanding, 2012.
 S. Das., S. Sarkar, A. Ray, A. N. Srivastava, and D. L. Simon, “Anomaly Detection in Flight Recorder Data: A Dynamic DataDriven Approach,” submitted to the American Control Conference, 2013.
 A. N. Srivastava and P. Barton, NASA easyJet Collaboration on the Human Factors Monitoring Program Study, NASA Technical Report 2012.
 A. N. Srivastava and P. Fabiani, NASA Onera Collaboration on the Human Factors in Aviation Accidents and Incidents, NASA Technical Report 2012.
 A. N. Srivastava, J. Hamlett (Project Officers), “NASA Southwest Airlines Automated Analysis of Diverse Aviation Data,” by Ryan Nurnburger and Sricharan Kumar, NASA Technical report 2012.
 A. N. Srivastava, S. Harms (Project Officers), “NASA HP Corporate Jets Automated Analysis of Aircraft Performance,” by Kamalika Das and Bryan Matthews, NASA Technical report 2012.
 A. N. Srivastava, N. V. Chawla, K. Das (eds), “Proceedings of the 2012 Conference on Intelligent Data Understanding,” CIDU 2012, October 2012, Boulder CO, USA.