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Mark Schwabacher's Publications

Magazine Article

S. Johnson, M. Schwabacher, and B. Brown. Diagnostic Models for Failure Analysis and Operations. NASA Tech Briefs, February 1, 2011.
Web-based article

Ph.D. Dissertation

M. Schwabacher. The Use of Artificial Intelligence to Improve the Numerical Optimization of Complex Engineering Designs. Ph.D. Dissertation, Rutgers University, Department of Computer Science, 1996.
Abstract and link to full dissertation

Journal articles

Note: In some cases, only draft versions of journal articles are available online.

David L. Iverson , Rodney Martin , Mark Schwabacher , Lilly Spirkovska , William Taylor, Ryan Mackey, J. Patrick Castle, and Vijayakumar Baskaran. General Purpose Data-Driven System Monitoring for Space Operations. Journal of Aerospace Computing, Information, and Communication 9:2, pp. 26-44, 2012.
Full paper (PDF)

M. Schwabacher, N. Oza, and B. Matthews. Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring. Journal of Aerospace Computing, Information, and Communication 6:7, pp. 464-482, 2009.
Abstract and link to full paper

M. Schwabacher, T. Ellman, and H. Hirsh. Learning to set up numerical optimizations of engineering designs. AI EDAM, 12(2), pp 173-192, 1998.
Full paper (pdf, 542 KB)

M. Schwabacher and A. Gelsey. Multi-Level Simulation and Numerical Optimization of Complex Engineering Designs. Journal of Aircraft, 35(3), 1998.
Full paper (pdf, 294 KB)

A. Gelsey, M. Schwabacher, and D. Smith. Using Modeling Knowledge to Guide Design Space Search. AI Journal, 101(1-2), 1998.
Full paper (pdf, 1.3 MB)

M. Schwabacher and A. Gelsey. Intelligent Gradient-Based Search of Incompletely Defined Design Spaces. AI EDAM, 11(3), 1997.
Abstract and link to full paper (pdf, 264 KB)

T. Ellman, J. Keane, M. Schwabacher, and K. Yao. Multi-Level Modeling for Engineering Design Optimization. AI EDAM, 11(5), 1997.
Full Paper (pdf, 389 KB)

V. Shukla, A. Gelsey, M. Schwabacher, D. Smith, and D. Knight. Automated Design Optimization for the P2 and P8 Hypersonic Inlets. Journal of Aircraft, 34(2), 1997.
full paper (pdf, 233 KB)

G.-C. Zha, D. Smith, M. Schwabacher, K. Rasheed, A. Gelsey, D. Knight, and M. Haas. High Performance Supersonic Missile Inlet Design Using Automated Optimization. Journal of Aircraft, 34(6), 1997.
full paper (pdf, 325 KB)

A. Gelsey, D. Smith, M. Schwabacher, K. Rasheed, and K. Miyake. A Search Space Toolkit. Decision Support Systems, 18:341-356, 1996.
abstract (text)
full paper (Compressed Postscript, 1.95MB)

Research abstract in journal

M. Schwabacher, T. Ellman, and H. Hirsh. Inductive learning for engineering design optimization. Research abstract. AI EDAM, 10:179-180. 1996.

Book Chapters

M. Schwabacher, P. Langley, C. Potter, S. Klooster, and A. Torregrosa. Discovering Communicable Models from Earth Science Data. In Computational Discovery of Scientific Knowledge, edited by Saso Dzeroski and Ljupco Todorovski, Lecture Notes in Artificial Intelligence, LNAI 4660, Springer, 2007.
Full chapter (pdf, 422 KB)

M. Schwabacher, T. Ellman, and H. Hirsh. Learning to Set Up Numerical Optimizations of Engineering Designs. In Data Mining for Design and Manufacturing: Methods and Applications, edited by Dan Braha, Kluwer Academic Publishers, 2001.
Full chapter (pdf, 214 KB)

Conference papers

S. Colombano, L. Sprikovska, V. Baskaran, G. Aaseng, R. McCann, J. Ossenfort, I. Smith, D. Iverson, and M. Schwabacher, "A system for fault management and fault consequences analysis for NASA’s Deep Space Habitat," AIAA SPACE 2013 Conference and Exposition, September 2013.
abstract

P. Morris, M. Schwabacher, M. Dilal, and C. Fry, "Embedding Temporal Constraints for Coordinated Execution in Habitat Automation," Proceedings of the International Workshop on Planning and Scheduling for Space, March 2013.
full paper (PDF)

R. Martin, M. Schwabacher, and B. Matthews, “Data-Driven Anomaly Detection Performance for the Ares I-X Ground Diagnostic Prototype,” PHM Conference, October 2010.
abstract and link to full paper

M.A. Schwabacher, R.A. Martin, R.D. Waterman, R.L. Oostdyk, J.P. Ossenfort, and B.L. Matthews. Ares I-X Ground Diagnostic Prototype. 57th Joint Army-Navy-NASA-Air Force (JANNAF) Propulsion Meeting / 7th Modeling and Simulation Subcommittee (MSS) / 5th Liquid Propulsion Subcommittee (LPS) / 4th Spacecraft Propulsion Subcommittee (SPS) Joint Meeting, Colorado Springs, May 2010.
Note: This paper is ITAR restricted. If you are a NASA employee and would like a copy, please contact me.

R.A. Martin, M.A. Schwabacher, and B.L. Matthews. Investigation of Data-Driven Anomaly Detection Performance for Simulated Thrust Vector Control System Failures. 57th Joint Army-Navy-NASA-Air Force (JANNAF) Propulsion Meeting / 7th Modeling and Simulation Subcommittee (MSS) / 5th Liquid Propulsion Subcommittee (LPS) / 4th Spacecraft Propulsion Subcommittee (SPS) Joint Meeting, Colorado Springs, May 2010.
Note: This paper is ITAR restricted. If you are a NASA employee and would like a copy, please contact me.

Mark Schwabacher, Rodney Martin, Robert Waterman, Rebecca Oostdyk, John Ossenfort, and Bryan Matthews. Ares I-X Ground Diagnostic Prototype. AIAA Infotech@Aerospace Conference, Atlanta, April 2010.
Abstract and link to full paper

D. L. Iverson, R. Martin, M. Schwabacher, L. Spirkovska, W. Taylor, R. Mackey, and J. P. Castle. General Purpose Data-Driven System Monitoring for Space Operations. AIAA Infotech@Aerospace Conference, 2009.
Full paper (pdf, 1.8 MB)

M. Schwabacher, R. Aguilar, and F. Figueroa. Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine. IEEE Aerospace Conference, 2009.
Full paper (pdf, 2.8 MB)

M. Schwabacher, R. Aguilar, and F. Figueroa. Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine. JANNAF Propulsion Meeting, 2008.
Note: This paper is ITAR restricted. If you are a NASA employee and would like a copy, please contact me. Otherwise, you may be interested in the above IEEE Aerospace Conference paper, which is a "sanitized" version of this paper.

A. Saxena, J. Celaya, E. Balaban, K. Goebel, B. Saha, S. Saha, and M. Schwabacher. Metrics for Evaluating Performance of Prognostic Techniques. International Conference on Prognostics and Health Management, 2008. Graduate of the Last Decade Best Paper Award.
Full paper (pdf, 793 KB)

F. Figueroa, R. Aguilar, M. Schwabacher, J. Schmalzel, and J. Morris. Integrated System Health Management (ISHM) for Test Stand and J-2X Engine: Core Implementation. AIAA Joint Propulsion Conference, 2008.
Full paper (pdf, 444 KB)

M. Schwabacher and R. Waterman. Pre-Launch Diagnostics for Launch Vehicles. IEEE Aerospace Conference, 2008.
Full paper (pdf, 471 KB)

M. Schwabacher and K. Goebel. A Survey of Artificial Intelligence for Prognostics. AAAI Fall Symposium, 2007.
Full paper (pdf, 186 KB)

R. A. Martin, M. Schwabacher, N. Oza, and A. Srivastava. Comparison of Unsupervised Anomaly Detection Methods for Systems Health Management Using Space Shuttle Main Engine Data. JANNAF Propulsion Meeting, 2007.
Full paper (pdf, 550 KB)

M. Schwabacher, N. Oza, and B. Matthews. Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring. AIAA Infotech@Aerospace Conference, 2007.
Full paper (pdf, 770 KB)

M. Schwabacher. Machine Learning for Rocket Propulsion Health Monitoring. SAE World Aerospace Congress, 2005.
Full paper (pdf, 220 KB)

M. Schwabacher. A Survey of Data-Driven Prognostics. AIAA Infotech@Aerospace Conference, 2005.
Full paper (pdf, 91KB)

S. D. Bay and M. Schwabacher. Mining Distance-Based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule. KDD-2003.
Abstract and link to full paper

M. Schwabacher, J. Samuels, and L. Brownston. The NASA Integrated Vehicle Health Management Technology Experiment for X-37. SPIE AeroSense 2002.
Full paper (pdf, 150 KB)

M. Schwabacher and P. Langley. Discovering Communicable Scientific Knowledge from Spatio-Temporal Data. International Conference on Machine Learning, 2001.
Full paper (pdf, 224 KB)

R. Sriram, S. Chase, S. Szykman, G. Kim, K. Lyons, P. Hart, M. Schwabacher, and R. Giachetti. Engineering Design Technologies Group: Research on Intelligent Systems. Intelligent Systems: A Semiotic Perspective, Proceedings of the 1996 International Multidisciplinary Conference, Vol. 2, NIST, Gaithersburg, MD, pp 148-153, 1996.

M. Schwabacher, T. Ellman, H. Hirsh, and G. Richter. Learning to choose a reformulation for numerical optimization of engineering designs. In J.S. Gero and F. Sudweeks (eds.), Artificial Intelligence in Design '96. Kluwer Academic Publishers, The Netherlands. 1996.
abstract (text)
full paper (Postscript, 450K)

M. Schwabacher and A. Gelsey. Multi-Level Simulation and Numerical Optimization of Complex Engineering Designs. AIAA Symposium on Multidisciplinary Analysis and Optimization, 1996.
full paper (pdf, 185K)

A. Gelsey, M. Schwabacher, and D. Smith. Using Modeling Knowledge to Guide Design Space Search. In J.S. Gero and F. Sudweeks (eds.), Artificial Intelligence in Design '96. Kluwer Academic Publishers, The Netherlands. 1996.
abstract (text)
full paper (Postscript, 69K)

V. Shukla, A. Gelsey, M. Schwabacher, D. Smith, and D. Knight. Automated Redesign of the NASA P8 Hypersonic Inlet Using Numerical Optimization. 32nd Joint Propulsion Conference, 1996.
abstract (text)
full paper (Postscript, 355K)

G.-C. Zha, D. Smith, M. Schwabacher, K. Rasheed, A. Gelsey, D. Knight, and M. Haas. High Performance Supersonic Missile Inlet Design Using Automated Optimization. AIAA Symposium on Multidisciplinary Analysis and Optimization, 1996.
full paper (Postscript, 3.1 Mb)

A. Gelsey, D. Knight, S. Gao, and M. Schwabacher. NPARC Simulation and Redesign of the NASA P2 Hypersonic Inlet. American Institute of Aeronautics and Astronautics Joint Propulsion Conference. 1995.
abstract (text)
full paper (Postscript, 472K)

T. Ellman, J. Keane, T. Murata, and M. Schwabacher. A Transformation System for Interactive Reformulation of Design Optimization Strategies. Proceedings of the Tenth Knowledge-Based Software Engineering Conference, Boston, Massachusetts, 1995.
Full Paper (pdf, 175K)

M. Schwabacher, H. Hirsh, and T. Ellman. Learning Prototype-Selection Rules for Case-Based Iterative Design. Proceedings of the Tenth IEEE Conference on Artificial Intelligence for Applications. San Antonio, Texas, 1994.
abstract (text)
full paper (Postscript, 430K)

T. Ellman, J. Keane, and M. Schwabacher. Intelligent Model Selection for Hillclimbing Search in Computer-Aided Design. Proceedings of the Eleventh National Conference on Artificial Intelligence, Washington, D.C., 1993.
abstract (text)
full paper (Postscript, 97K)

Workshop papers

S. Bay and M. Schwabacher. Near Linear Time Detection of Distance-Based Outliers and Applications to Security. SIAM Data Mining Conference, Workshop on Data Mining for Counter Terrorism and Security, San Francisco, CA, 2003.

M. Schwabacher, H. Hirsh, and T. Ellman. Learning To Select Prototypes and Reformulations for Design. AID-96 Workshop on Machine Learning in Design, Stanford, CA, 1996.

M. Schwabacher, T. Ellman, and H. Hirsh. Inductive Learning for Engineering Design Optimization. ICML-95 Workshop on Applying Machine Learning in Practice, Tahoe City, CA, 1995.
full paper (pdf, 140K)

M. Schwabacher, T. Ellman, H. Hirsh, and G. Richter. Learning when reformulation is appropriate for iterative design. IJCAI-95 Workshop on Machine Learning in Engineering, Montreal, Quebec, Canada, 1995.

M. Schwabacher, T. Ellman, H. Hirsh, and G. Richter. Learning when reformulation is appropriate for iterative design. Symposium on Abstraction, Reformulation, and Approximation, Ville d'Esterel, Quebec, Canada, 1995.

M. Schwabacher, H. Hirsh, and T. Ellman. Inductive Learning of Prototype-Selection Rules for Case-Based Iterative Design. IJCAI-93 Workshop on Artificial Intelligence in Design, Chambery, France, 1993.

Technical reports

W. Maul, H. Park, M. Schwabacher, M. Watson, R. Mackey, A. Fijany, L. Trevino, and J. Weir. Intelligent Elements for the ISHM Testbed and Prototypes (ITP) Project. NASA TM-2005-213849, September 2005.
Abstract and link to full PDF

T. Ellman and M. Schwabacher. Abstraction and Decomposition in Hillclimbing Design Optimization. Technical Report CAP-TR-14, Department of Computer Science, Rutgers University, New Brunswick, NJ, 1993.
abstract (text)
full paper (Postscript, 125K)

T. Ellman, J. Keane, and M. Schwabacher. The Rutgers CAP Project Design Associate. Technical Report CAP-TR-7, Department of Computer Science, Rutgers University, New Brunswick, NJ, 1992.
abstract (text)
full paper (Postscript, 123K)

R. Bixby and M. Schwabacher. Solving Linear Programs with Two Processors. Technical Report TR89-16, Department of Mathematical Sciences, Rice University, Houston, TX, 1989.
abstract
Full paper (pdf, 268 KB)

Last updated March 5, 2014

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