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.
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.
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.
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.
M. Schwabacher and A. Gelsey. Intelligent Gradient-Based Search of Incompletely Defined Design Spaces. AI EDAM, 11(3), 1997.
Full paper (PDF)(pdf, 264 KB)
T. Ellman, J. Keane, M. Schwabacher, and K. Yao. Multi-Level Modeling for Engineering Design Optimization. AI EDAM, 11(5), 1997.
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.
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.
A. Gelsey, D. Smith, M. Schwabacher, K. Rasheed, and K. Miyake. A Search Space Toolkit. Decision Support Systems, 18:341-356, 1996.
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.
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.
Conference papers
Paul Morris, Minh Do, Robert McCann, Lilly Spirkovska, Mark Schwabacher, and Jeremy Frank, "Determining Mission Effects of Equipment Failures", AIAA Space and Astronautics Forum and Exposition, August 2014.
Ryan May, James Soeder, Raymond Beach, Patrick George, Jeremy Frank, Mark Schwabacher, Silvano Colombano, Lui Wang, and Dennis Lawler, "An Architecture to Enable Autonomous Control of a Spacecraft", AIAA Propulsion and Energy Forum and Exposition, July 2014.
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.
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.
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..
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.
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.
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.
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.
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.
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.
M. Schwabacher and R. Waterman. Pre-Launch Diagnostics for Launch Vehicles. IEEE Aerospace Conference, 2008.
M. Schwabacher and K. Goebel. A Survey of Artificial Intelligence for Prognostics. AAAI Fall Symposium, 2007.
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.
M. Schwabacher, N. Oza, and B. Matthews. Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring. AIAA Infotech@Aerospace Conference, 2007.
M. Schwabacher. Machine Learning for Rocket Propulsion Health Monitoring. SAE World Aerospace Congress, 2005.
M. Schwabacher. A Survey of Data-Driven Prognostics. AIAA Infotech@Aerospace Conference, 2005.
S. D. Bay and M. Schwabacher. Mining Distance-Based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule. KDD-2003.
M. Schwabacher, J. Samuels, and L. Brownston. The NASA Integrated Vehicle Health Management Technology Experiment for X-37. SPIE AeroSense 2002.
M. Schwabacher and P. Langley. Discovering Communicable Scientific Knowledge from Spatio-Temporal Data. International Conference on Machine Learning, 2001.
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.
M. Schwabacher and A. Gelsey. Multi-Level Simulation and Numerical Optimization of Complex Engineering Designs. AIAA Symposium on Multidisciplinary Analysis and Optimization, 1996.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
R. Bixby and M. Schwabacher. Solving Linear Programs with Two Processors. Technical Report TR89-16, Department of Mathematical Sciences, Rice University, Houston, TX, 1989.
Last updated October 13, 2018