Dr. Ole J. Mengshoel is a Senior Systems Scientist with CMU Silicon Valley at the NASA Ames Research Center. His current research focuses on reasoning, diagnosis, decision support, reasoning, and machine learning under uncertainty - often using Bayesian networks – with aerospace applications of interest to NASA. Additional research interests include resource allocation and scheduling in real-time systems, intelligent user interfaces, information assurance, evolutionary algorithms, knowledge acquisition, and knowledge engineering. Dr. Mengshoel has managed and provided hands-on leadership in a wide range of research and development projects. Working with companies such as Boeing, Rockwell Automation, and Rockwell Collins, he has successfully developed new technologies and software that have or are being matured and transitioned into the aerospace, defense, finance, education, electronic commerce, and manufacturing sectors. Dr. Mengshoel has published over 50 articles and papers in journals and conferences, and holds 4 U.S. patents. He has a Ph.D. in Computer Science from the University of Illinois, Urbana-Champaign. His undergraduate degree is in Computer Science from the Norwegian Institute of Technology, Norway (now NTNU). Prior to his work with NASA, he was a research scientist in the Knowledge-Based Systems at SINTEF (Scandinavia's largest independent research organization) and in Decision Sciences Group at Rockwell Scientific (now Teledyne Scientific and Imaging).
Bayesian Networks
Multi-focus and Multi-window Techniques for Interactive Network Exploration (with Priya K. Sundararajan and Ted Selker), Proc. of Visualization and Data Analysis (VDA-13) (2013)
Networks analysts often need to compare nodes in different parts of a network. When zoomed...
MapReduce for Bayesian Network Parameter Learning using the EM Algorithm (with Aniruddha Basak and Irina Brinster), Proc. of Big Learning: Algorithms, Systems and Tools (2012)
This work applies the distributed computing framework MapReduce to Bayesian network parameter learning from incomplete...
Scaling Bayesian Network Parameter Learning with Expectation Maximization using MapReduce (with Erik B. Reed), Proc. of Big Learning: Algorithms, Systems and Tools (2012)
Bayesian network (BN) parameter learning from incomplete data can be a computationally expensive task for...
Software and System Health Management for Autonomous Robotics Missions (with Timmy Mbaya), Proc. of i-SAIRAS 2012 (2012)
Advanced autonomous robotics space missions rely heavily on the flawless interaction of complex hardware, multiple...
Reactive Bayesian Network Computation using Feedback Control: An Empirical Study (with Abe Ishihara and Erik Reed), Proc. of the 9th Bayesian Modelling Applications Workshop (2012)
This paper investigates the challenge of integrating intelligent systems into varying computational platforms and application...
Stochastic Local Search
Initialization and Restart in Stochastic Local Search: Computing a Most Probable Explanation in Bayesian Networks (with David C. Wilkins and Dan Roth), IEEE Transactions on Knowledge and Data Engineering (2011)
For hard computational problems, stochastic local search has proven to be a competitive approach to...
Portfolios in Stochastic Local Search: Efficiently Computing Most Probable Explanations in Bayesian Networks (with D Roth and D Wilkins), J Autom Reasoning (2010)
Portfolio methods support the combination of different algorithms and heuristics, including stochastic local search (SLS)...
Understanding the Role of Noise in Stochastic Local Search: Analysis and Experiments, Artificial Intelligence (2008)
Stochastic local search (SLS) algorithms have recently been proven to be among the best approaches...
Electrical Power Systems
Multi-Focus and Multi-Level Techniques for Visualization and Analysis of Networks with Thematic Data (with Michele Cossalter and Ted Selker), Proc. of Visualization and Data Analysis (VDA 2013) (2013)
Information-rich data sets bring several challenges in the areas of visualization and analysis, even when...
Multi-focus and Multi-window Techniques for Interactive Network Exploration (with Priya K. Sundararajan and Ted Selker), Proc. of Visualization and Data Analysis (VDA-13) (2013)
Networks analysts often need to compare nodes in different parts of a network. When zoomed...
Reactive Bayesian Network Computation using Feedback Control: An Empirical Study (with Abe Ishihara and Erik Reed), Proc. of the 9th Bayesian Modelling Applications Workshop (2012)
This paper investigates the challenge of integrating intelligent systems into varying computational platforms and application...
Adaptive Control of Bayesian Network Computation (with Erik Reed and Abe Ishihara), Proc. 5th International Symposium on Resilient Control Systems (ISRCS) (2012)
This paper considers the problem of providing, for computational processes, soft real-time (or reactive) response...
A Tutorial on Bayesian Networks for System Health Management (with Arthur Choi, Lu Zheng, and Adnan Darwiche), Machine Learning and Knowledge Discovery for Engineering Systems Health Management (2011)
Bayesian networks have established themselves as an indispensable tool in artificial intelligence, and are being...
Human Computer Interaction
Multi-Focus and Multi-Level Techniques for Visualization and Analysis of Networks with Thematic Data (with Michele Cossalter and Ted Selker), Proc. of Visualization and Data Analysis (VDA 2013) (2013)
Information-rich data sets bring several challenges in the areas of visualization and analysis, even when...
Multi-focus and Multi-window Techniques for Interactive Network Exploration (with Priya K. Sundararajan and Ted Selker), Proc. of Visualization and Data Analysis (VDA-13) (2013)
Networks analysts often need to compare nodes in different parts of a network. When zoomed...
Visualizing and Understanding Large-Scale Bayesian Networks (with Michele Cossalter and Ted Selker), The AAAI-11 Workshop on Scalable Integration of Analytics and Visualization (2011)
Bayesian networks are a theoretically well-founded approach to represent large multi-variate probability distributions, and have...
Verification and Validation of System Health Management Models using Parametric Testing (with Erik Reed and Johann Schumann), Proc. of Infotech@Aerospace 2011 (2011)
System Health Management (SHM) systems have found their way into many safety-critical aerospace and industrial...
Evolutionary Computation
Age-Layered Expectation Maximization for Parameter Learning in Bayesian Networks (with Avneesh Saluja and Priya Sundararajan), Proc. of the Fifteenth International Conference on Artificial Intelligence and Statistics (2012)
The expectation maximization (EM) algorithm is a popular algorithm for parameter estimation in models with...
Generalized Crowding for Genetic Algorithms (with Severino F. Galan), Genetic and Evolutionary Computation Conference 2010 (GECCO-10) (2010)
Crowding is a technique used in genetic algorithms to preserve diversity in the population and...
Constraint Handling Using Tournament Selection: Abductive Inference in Partly Deterministic Bayesian Network (with Severino F. Galan), Evolutionary Computation (2009)
Constraints occur in many application areas of interest to evolutionary computation. The area considered here...
The Crowding Approach to Niching in Genetic Algorithms (with David E. Goldberg), Evolutionary Computation (2008)
A wide range of niching techniques have been investigated in evolutionary and genetic algorithms. In...
Probabilistic Crowding: Deterministic Crowding with Probabilistic Replacement (with David E. Goldberg), Proc. of the Genetic and Evolutionary Computation Conference (GECCO-99) (1999)
This paper presents a novel niching algorithm, probabilistic crowding. Like its predecessor deterministic crowding, probabilistic...
Parallel Computing
MapReduce for Bayesian Network Parameter Learning using the EM Algorithm (with Aniruddha Basak and Irina Brinster), Proc. of Big Learning: Algorithms, Systems and Tools (2012)
This work applies the distributed computing framework MapReduce to Bayesian network parameter learning from incomplete...
Scaling Bayesian Network Parameter Learning with Expectation Maximization using MapReduce (with Erik B. Reed), Proc. of Big Learning: Algorithms, Systems and Tools (2012)
Bayesian network (BN) parameter learning from incomplete data can be a computationally expensive task for...
Accelerating Bayesian Network Parameter Learning Using Hadoop and MapReduce (with Aniruddha Basak, Irina Brinster, and Xianheng Ma), Proc. of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining (BigMine’12) (2012)
Learning conditional probability tables of large Bayesian Networks (BNs) with hidden nodes using the Expectation...
Belief Propagation by Message Passing in Junction Trees: Computing Each Message Faster Using GPU Parallelization (with Lu Zheng and Jike Chong), Proc. of the 27th Conference on Uncertainty in Artificial Intelligence (UAI-11) (2011)
Compiling Bayesian networks (BNs) to junction trees and performing belief propagation over them is among...
Synthetic Problems
Controlled generation of hard and easy Bayesian networks: Impact on maximal clique size in tree clustering (with David C. Wilkins and Dan Roth), Artificial Intelligence (2006)
This article presents and analyzes algorithms that systematically generate random Bayesian networks of varying difficulty...
Diagnosis
Multi-Focus and Multi-Level Techniques for Visualization and Analysis of Networks with Thematic Data (with Michele Cossalter and Ted Selker), Proc. of Visualization and Data Analysis (VDA 2013) (2013)
Information-rich data sets bring several challenges in the areas of visualization and analysis, even when...
Multi-focus and Multi-window Techniques for Interactive Network Exploration (with Priya K. Sundararajan and Ted Selker), Proc. of Visualization and Data Analysis (VDA-13) (2013)
Networks analysts often need to compare nodes in different parts of a network. When zoomed...
Software and System Health Management for Autonomous Robotics Missions (with Timmy Mbaya), Proc. of i-SAIRAS 2012 (2012)
Advanced autonomous robotics space missions rely heavily on the flawless interaction of complex hardware, multiple...
Reactive Bayesian Network Computation using Feedback Control: An Empirical Study (with Abe Ishihara and Erik Reed), Proc. of the 9th Bayesian Modelling Applications Workshop (2012)
This paper investigates the challenge of integrating intelligent systems into varying computational platforms and application...
Adaptive Control of Bayesian Network Computation (with Erik Reed and Abe Ishihara), Proc. 5th International Symposium on Resilient Control Systems (ISRCS) (2012)
This paper considers the problem of providing, for computational processes, soft real-time (or reactive) response...
Junction Trees
Reactive Bayesian Network Computation using Feedback Control: An Empirical Study (with Abe Ishihara and Erik Reed), Proc. of the 9th Bayesian Modelling Applications Workshop (2012)
This paper investigates the challenge of integrating intelligent systems into varying computational platforms and application...
Accelerating Bayesian Network Parameter Learning Using Hadoop and MapReduce (with Aniruddha Basak, Irina Brinster, and Xianheng Ma), Proc. of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining (BigMine’12) (2012)
Learning conditional probability tables of large Bayesian Networks (BNs) with hidden nodes using the Expectation...
Adaptive Control of Bayesian Network Computation (with Erik Reed and Abe Ishihara), Proc. 5th International Symposium on Resilient Control Systems (ISRCS) (2012)
This paper considers the problem of providing, for computational processes, soft real-time (or reactive) response...
Initialization and Restart in Stochastic Local Search: Computing a Most Probable Explanation in Bayesian Networks (with David C. Wilkins and Dan Roth), IEEE Transactions on Knowledge and Data Engineering (2011)
For hard computational problems, stochastic local search has proven to be a competitive approach to...
Understanding the Scalability of Bayesian Network Inference using Clique Tree Growth Curves, Artificial Intelligence (2010)
One of the main approaches to performing computation in Bayesian networks (BNs) is clique tree...
Arithmetic Circuits
Software and System Health Management for Autonomous Robotics Missions (with Timmy Mbaya), Proc. of i-SAIRAS 2012 (2012)
Advanced autonomous robotics space missions rely heavily on the flawless interaction of complex hardware, multiple...
Bayesian Software Health Management for Aircraft Guidance, Navigation, and Control (with Johann M. Schumann and Timmy Mbaya), Annual conference of the prognostics and health management society 2011 (PHM-11) (2011)
Modern aircraft — both piloted fly-by-wire commercial aircraft as well as UAVs — more and...
Integrating Probabilistic Reasoning and Statistical Quality Control Techniques for Fault Diagnosis in Hybrid Domains (with Brian Ricks and Craig Harrison), Annual Conference of the Prognostics and Health Management Society 2011 (PHM-11) (2011)
Bayesian networks, which may be compiled to arithmetic circuits in the interest of speed and...
Software Health Management with Bayesian Networks (with Johann M. Schumann), 2nd International Workshop On Software Health Management (2011)
Integrated Software and Sensor Health Management for Small Spacecraft (with Johann Schumann and Timmy Mbaya), Proc. of 4th International Conference on Space Mission Challenges for Information Technology (2011)
Despite their size, small spacecraft have highly complex architectures with many sensors and computer-controlled actuators....
Software Engineering
Accelerating Bayesian Network Parameter Learning Using Hadoop and MapReduce (with Aniruddha Basak, Irina Brinster, and Xianheng Ma), Proc. of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining (BigMine’12) (2012)
Learning conditional probability tables of large Bayesian Networks (BNs) with hidden nodes using the Expectation...
Bayesian Software Health Management for Aircraft Guidance, Navigation, and Control (with Johann M. Schumann and Timmy Mbaya), Annual conference of the prognostics and health management society 2011 (PHM-11) (2011)
Modern aircraft — both piloted fly-by-wire commercial aircraft as well as UAVs — more and...
Software Health Management with Bayesian Networks (with Johann M. Schumann), 2nd International Workshop On Software Health Management (2011)
Integrated Software and Sensor Health Management for Small Spacecraft (with Johann Schumann and Timmy Mbaya), Proc. of 4th International Conference on Space Mission Challenges for Information Technology (2011)
Despite their size, small spacecraft have highly complex architectures with many sensors and computer-controlled actuators....
Towards Software Health Management with Bayesian Networks (with Johann Schumann, Ashok Srivastava, and Adnan Darwiche), Proc. of the FSE/SDP Workshop on Future of Software Engineering Research (FoSER-10) (2010)
More and more systems (e.g., aircraft, machinery, cars) rely heavily on software, which performs safety-critical...
Verification and Validation
Multi-focus and Multi-window Techniques for Interactive Network Exploration (with Priya K. Sundararajan and Ted Selker), Proc. of Visualization and Data Analysis (VDA-13) (2013)
Networks analysts often need to compare nodes in different parts of a network. When zoomed...
Verification and Validation of System Health Management Models using Parametric Testing (with Erik Reed and Johann Schumann), Proc. of Infotech@Aerospace 2011 (2011)
System Health Management (SHM) systems have found their way into many safety-critical aerospace and industrial...
A Framework for Systematic Benchmarking of Monitoring and Diagnostic Systems (with Tolga Kurtoglu and Scott Poll), Proc. of the 2008 Conference on Prognostics and Health Management (PHM-08) (2008)
In this paper, we present an architecture and a formal framework to be used for...
Stochastic Optimization
Age-Layered Expectation Maximization for Parameter Learning in Bayesian Networks (with Avneesh Saluja and Priya Sundararajan), Proc. of the Fifteenth International Conference on Artificial Intelligence and Statistics (2012)
The expectation maximization (EM) algorithm is a popular algorithm for parameter estimation in models with...
Constraint Handling Using Tournament Selection: Abductive Inference in Partly Deterministic Bayesian Network (with Severino F. Galan), Evolutionary Computation (2009)
Constraints occur in many application areas of interest to evolutionary computation. The area considered here...
Understanding the Role of Noise in Stochastic Local Search: Analysis and Experiments, Artificial Intelligence (2008)
Stochastic local search (SLS) algorithms have recently been proven to be among the best approaches...
Probabilistic Crowding: Deterministic Crowding with Probabilistic Replacement (with David E. Goldberg), Proc. of the Genetic and Evolutionary Computation Conference (GECCO-99) (1999)
This paper presents a novel niching algorithm, probabilistic crowding. Like its predecessor deterministic crowding, probabilistic...
Aerospace
Multi-Focus and Multi-Level Techniques for Visualization and Analysis of Networks with Thematic Data (with Michele Cossalter and Ted Selker), Proc. of Visualization and Data Analysis (VDA 2013) (2013)
Information-rich data sets bring several challenges in the areas of visualization and analysis, even when...
Software and System Health Management for Autonomous Robotics Missions (with Timmy Mbaya), Proc. of i-SAIRAS 2012 (2012)
Advanced autonomous robotics space missions rely heavily on the flawless interaction of complex hardware, multiple...
Bayesian Software Health Management for Aircraft Guidance, Navigation, and Control (with Johann M. Schumann and Timmy Mbaya), Annual conference of the prognostics and health management society 2011 (PHM-11) (2011)
Modern aircraft — both piloted fly-by-wire commercial aircraft as well as UAVs — more and...
Integrating Probabilistic Reasoning and Statistical Quality Control Techniques for Fault Diagnosis in Hybrid Domains (with Brian Ricks and Craig Harrison), Annual Conference of the Prognostics and Health Management Society 2011 (PHM-11) (2011)
Bayesian networks, which may be compiled to arithmetic circuits in the interest of speed and...
Software Health Management with Bayesian Networks (with Johann M. Schumann), 2nd International Workshop On Software Health Management (2011)
Markov Chains
Generalized Crowding for Genetic Algorithms (with Severino F. Galan), Genetic and Evolutionary Computation Conference 2010 (GECCO-10) (2010)
Crowding is a technique used in genetic algorithms to preserve diversity in the population and...
Understanding the Role of Noise in Stochastic Local Search: Analysis and Experiments, Artificial Intelligence (2008)
Stochastic local search (SLS) algorithms have recently been proven to be among the best approaches...
Reconfiguration
Diagnosis and Reconfiguration using Bayesian Networks: An Electrical Power System Case Study (with W. Bradley Knox), The IJCAI-09 Workshop on Self-* and Autonomous Systems: reasoning and integration challenges (SAS-09) (2009)
Automated diagnosis and reconfiguration are important computational techniques that aim to minimize human intervention in...
Feedback Control
Reactive Bayesian Network Computation using Feedback Control: An Empirical Study (with Abe Ishihara and Erik Reed), Proc. of the 9th Bayesian Modelling Applications Workshop (2012)
This paper investigates the challenge of integrating intelligent systems into varying computational platforms and application...
Adaptive Control of Bayesian Network Computation (with Erik Reed and Abe Ishihara), Proc. 5th International Symposium on Resilient Control Systems (ISRCS) (2012)
This paper considers the problem of providing, for computational processes, soft real-time (or reactive) response...
Expectation Maximization
MapReduce for Bayesian Network Parameter Learning using the EM Algorithm (with Aniruddha Basak and Irina Brinster), Proc. of Big Learning: Algorithms, Systems and Tools (2012)
This work applies the distributed computing framework MapReduce to Bayesian network parameter learning from incomplete...
Scaling Bayesian Network Parameter Learning with Expectation Maximization using MapReduce (with Erik B. Reed), Proc. of Big Learning: Algorithms, Systems and Tools (2012)
Bayesian network (BN) parameter learning from incomplete data can be a computationally expensive task for...
Age-Layered Expectation Maximization for Parameter Learning in Bayesian Networks (with Avneesh Saluja and Priya Sundararajan), Proc. of the Fifteenth International Conference on Artificial Intelligence and Statistics (2012)
The expectation maximization (EM) algorithm is a popular algorithm for parameter estimation in models with...
Social Networks
Multi-Focus and Multi-Level Techniques for Visualization and Analysis of Networks with Thematic Data (with Michele Cossalter and Ted Selker), Proc. of Visualization and Data Analysis (VDA 2013) (2013)
Information-rich data sets bring several challenges in the areas of visualization and analysis, even when...
The Impact of Social Affinity on Phone Calling Patterns: Categorizing Social Ties from Call Data Records (with Sara Motahari, Phyllis Reuther, Sandeep Appala, Luca Zoia, and Jay Shah), Proc. of the Sixth Workshop on Social Network Mining and Analysis (2012)
Social ties defined by phone calls made between people can be grouped to various affinity...
MapReduce
MapReduce for Bayesian Network Parameter Learning using the EM Algorithm (with Aniruddha Basak and Irina Brinster), Proc. of Big Learning: Algorithms, Systems and Tools (2012)
This work applies the distributed computing framework MapReduce to Bayesian network parameter learning from incomplete...
Scaling Bayesian Network Parameter Learning with Expectation Maximization using MapReduce (with Erik B. Reed), Proc. of Big Learning: Algorithms, Systems and Tools (2012)
Bayesian network (BN) parameter learning from incomplete data can be a computationally expensive task for...