Mark Schwabacher

2.5k total citations · 1 hit paper
45 papers, 1.8k citations indexed

About

Mark Schwabacher is a scholar working on Artificial Intelligence, Control and Systems Engineering and Signal Processing. According to data from OpenAlex, Mark Schwabacher has authored 45 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 16 papers in Control and Systems Engineering and 8 papers in Signal Processing. Recurrent topics in Mark Schwabacher's work include Fault Detection and Control Systems (15 papers), AI-based Problem Solving and Planning (10 papers) and Anomaly Detection Techniques and Applications (10 papers). Mark Schwabacher is often cited by papers focused on Fault Detection and Control Systems (15 papers), AI-based Problem Solving and Planning (10 papers) and Anomaly Detection Techniques and Applications (10 papers). Mark Schwabacher collaborates with scholars based in United States and Netherlands. Mark Schwabacher's co-authors include Stephen D. Bay, Kai Goebel, Bhaskar Saha, Abhinav Saxena, Sankalita Saha, José Celaya, Edward Balaban, Andrew Gelsey, Bryan Matthews and Nikunj C. Oza and has published in prestigious journals such as Artificial Intelligence, Decision Support Systems and SAE technical papers on CD-ROM/SAE technical paper series.

In The Last Decade

Mark Schwabacher

42 papers receiving 1.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Metrics for evaluating performance of prognostic techniques

2008 401 citations Kai Goebel, Mark Schwabacher et al. profile →

Peers

Countries citing papers authored by Mark Schwabacher

Since Specialization

Citations

This map shows the geographic impact of Mark Schwabacher's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mark Schwabacher with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Schwabacher more than expected).

Fields of papers citing papers by Mark Schwabacher

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark Schwabacher. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mark Schwabacher. The network helps show where Mark Schwabacher may publish in the future.

Co-authorship network of co-authors of Mark Schwabacher

This figure shows the co-authorship network connecting the top 25 collaborators of Mark Schwabacher. A scholar is included among the top collaborators of Mark Schwabacher based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mark Schwabacher. Mark Schwabacher is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	A System for Fault Management for NASA's Deep Space Habitat 2013 ·NASA Technical Reports Server (NASA) ·Silvano P. Colombano, (unknown), (unknown), Robert S. McCann, V. Baskaran, (unknown), (unknown), David Iverson, Mark Schwabacher	1
2	General Purpose Data-Driven Monitoring for Space Operations 2012 ·Journal of Aerospace Computing Information and Communication ·David Iverson, Rodney Martin, Mark Schwabacher, Lilly Spirkovska, William M. Taylor, Ryan Mackey, J. Castle, V. Baskaran	37
3	Data-Driven Anomaly Detection Performance for the Ares I-X Ground Diagnostic Prototype 2010 ·Annual Conference of the PHM Society ·Rodney Martin, Mark Schwabacher, Bryan Matthews	3
4	General Purpose Data-Driven System Monitoring for Space Operations 2009 ·David Iverson, Rodney Martin, Mark Schwabacher, (unknown), William M. Taylor, Ryan Mackey, J. Castle	32
5	A Survey of Artificial Intelligence for Prognostics. 2007 ·National Conference on Artificial Intelligence ·Mark Schwabacher, Kai Goebel	161
6	Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring 2007 ·Mark Schwabacher, Nikunj C. Oza, Bryan Matthews	5
7	A Survey of Data-Driven Prognostics 2005 ·Mark Schwabacher	198
8	Mining distance-based outliers in near linear time with randomization and a simple pruning rule 2003 ·Stephen D. Bay, Mark Schwabacher	424
9	The NASA Integrated Vehicle Health Management Technology Experiment for X-37 2002 ·NASA Technical Reports Server (NASA) ·Mark Schwabacher, (unknown), Lee Brownston, Daniel Clancy	25
10	A transformation system for interactive reformulation of design optimization strategies 2002 ·View ·(unknown), (unknown), (unknown), Mark Schwabacher	0
11	<title>NASA integrated vehicle health management technology experiment for X-37</title> 2002 ·Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE ·Mark Schwabacher, (unknown), Lee Brownston	18
12	Discovering Communicable Scientific Knowledge from Spatio-Temporal Data 2001 ·International Conference on Machine Learning ·Mark Schwabacher, Pat Langley	9
13	Using modeling knowledge to guide design space search 1998 ·Artificial Intelligence ·Andrew Gelsey, Mark Schwabacher, (unknown)	32
14	Learning to set up numerical optimizations of engineering designs 1998 ·Artificial intelligence for engineering design analysis and manufacturing ·Mark Schwabacher, Thomas Ellman, Haym Hirsh	19
15	Intelligent Gradient-Based Search of Incompletely Defined Design Spaces \| NIST 1997 ·Artificial Intelligence in Engineering ·Mark Schwabacher, Andrew Gelsey	0
16	Multilevel modelling for engineering design optimization 1997 ·Artificial intelligence for engineering design analysis and manufacturing ·Thomas Ellman, (unknown), Mark Schwabacher, Ke-Thia Yao	3
17	Inductive learning for engineering design optimization 1996 ·Artificial intelligence for engineering design analysis and manufacturing ·Mark Schwabacher, Thomas Ellman, Haym Hirsh	2
18	The use of artificial intelligence to improve the numerical optimization of complex engineering designs 1996 ·View ·Mark Schwabacher	5
19	Automated redesign of the NASA P8 hypersonic inlet using numerical optimization 1995 ·View ·Vijay Shukla, Andrew Gelsey, Mark Schwabacher, (unknown), Doyle Knight	8
20	Intelligent Model Selection for Hillclimbing Search in Computer-Aided Design 1993 ·View ·(unknown), Mark Schwabacher, Thomas Ellman	14

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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