Mark Schwabacher's Publications
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.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. 1542-9423, 2009.
Full paper (pdf, 1.2 MB)
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.
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
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.
Full paper (pdf,
200 KB)
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 November 23, 2009
- NASA Official: Dave Korsmeyer
- Curator: ASANI Solutions