Matthew J. Daigle
Artificial Intelligence, Machine Learning, & Data Science
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I am a research scientist working in the areas of artificial intelligence, machine learning, and data science. While I have worked in diverse application domains and topics such as electric automobiles and aircraft, robotics, cryogenic propellant systems, simulation, and airspace safety, my main research emphasis is in systems health management, that is, I develop algorithms through which machines can self-diagnose their condition and predict their future failures.

Currently, I am a Principal AI Scientist at NIO USA, Inc.. Prior to joining NIO, I was a Research Computer Scientist and Lead of the Prognostics & Diagnostics Group at NASA Ames Research Center. Prior to that, I was an Associate Scientist with the University of California, Santa Cruz at NASA Ames.

I received the B.S. degree in Computer Science and Computer and Systems Engineering from Rensselaer Polytechnic Institute, Troy, NY, in 2004, and the M.S. and Ph.D. degrees in Computer Science from Vanderbilt University, Nashville, TN, in 2006 and 2008, respectively. From September 2004 to May 2008, I was a Graduate Research Assistant with the Institute for Software Integrated Systems and Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN.

Fun Fact: I have an Erdös number of 6, and a Bacon number of 4, resulting in an Erdös-Bacon number of 10.

Email: me at

Recent Publications

  • M. Daigle, A. Bregon, and I. Roychoudhury, “Diagnosis of Hybrid Systems using Structural Model Decomposition,” Fault Diagnosis of Hybrid Dynamic and Complex Systems, pp. 179-207, March 2018. [show abstract]
  • Q. Gaudel, E. Chanthery, P. Ribot, and M. Daigle, “Diagnosis of Hybrid Particle Petri Nets: Theory and Application on a Planetary Rover,” Fault Diagnosis of Hybrid Dynamic and Complex Systems, pp. 209-241, March 2018. [show abstract]
  • C. Kulkarni, M. Daigle, G. Gorospe, and K. Goebel, “Experimental Validation of Model-based Prognostics for Pneumatic Valves,” International Journal of Prognostics and Health Management, vol. 8, no. 1, December 2017. [show abstract]
Abstract: Prognostics is a systems engineering discipline focused on predicting end-of-life of components and systems. As a relatively new and emerging technology, there are few fielded implementations of prognostics, due in part to practitioners perceiving a large hurdle in developing the models, algorithms, architecture, and integration pieces. Similarly, no open software frameworks for applying prognostics currently exist. This paper introduces the Generic Software Architecture for Prognostics (GSAP), an open-source, cross-platform, object-oriented software framework and support library for creating prognostics applications. GSAP was designed to make prognostics more accessible and enable faster adoption and implementation by industry, by reducing the effort and investment required to develop, test, and deploy prognostics. This paper describes the requirements, design, and testing of GSAP. Additionally, a detailed case study involving battery prognostics demonstrates its use.
  • G. Gorospe, M. Daigle, S. Sankararaman, C. Kulkarni, and E. Ng, “GPU Accelerated Prognostics,” Annual Conference of the Prognostics and Health Management Society 2016, St. Petersburg, FL, October 2017. [show abstract]
Abstract: Prognostic methods enable operators and maintainers to predict future performance for critical systems. However, these methods can be computationally expensive and should be performed each time new information about the system becomes available. In light of these computational requirements, we have investigated the application of graphics processing units (GPUs) as a computational platform for general and real-time prognostics. Recent advances in GPU technology have reduced cost and increased the computational capability of these highly parallel processing units, making them more attractive for the deployment of prognostic software. We present a survey of model-based prognostic algorithms with considerations for leveraging the parallel architecture of the GPU and a case study of GPU-accelerated battery prognostics with computational performance results.
  • M. Daigle, I. Roychoudhury, L. Spirkovska, K. Goebel, S. Sankararaman, J. Ossenfort, and C. Kulkarni, “Real-Time Prediction of Safety Margins in the National Airspace,” AIAA Aviation Conference, Denver, CO, June 2017. [show abstract]
Abstract: Underlying all operations in the National Airspace System (NAS) is the concept of safety. Safety, as defined by acceptable levels of risk, is to be maintained at all times. The real-time safety monitoring (RTSM) framework is under development to provide an automated system to quantify safety in the NAS, estimate the current level of safety, and predict the future evolution of safety and the occurrence of events that pose an increased risk to flights so that these occurrences can be managed strategically rather than mitigated reactively. This paper presents the mathematical framework, the models, and the monitoring and prediction algorithms used to achieve this. RTSM computes safety as expressed through a set of safety margins based on user-defined safety metrics, thresolds, and events. Sources of uncertainty are modeled and propagated through the predictions in order to compute the probabilistic evolution of safety and the probability of events that introduce increased risk to operations. A prototype implementation is discussed and results demonstrating feasibility are presented. The results highlight the kinds of predictions that can be computed and the fidelity that is currently achieved.
  • L. Spirkovska, I. Roychoudhury, M. Daigle, and K. Goebel, “Real Time Safety Monitoring: Concept for Supporting Safe Flight Operations,” AIAA Aviation Conference, Denver, CO, June 2017. [show abstract]
Abstract: Processes, procedures, regulations, and technologies are continuously evolving to maintain or improve the safety of the National Airspace System (NAS). In this paper, we describe a Real Time Safety Monitoring (RTSM) system that benefits from these efforts to define a set of safety metrics that are automatically monitored in real-time. In addition to providing information about current potentially adverse conditions to a variety of users, from those who need a broad overview of a day's flight operations to those who need to decide on a control tactic to employ in the next five minutes, the RTSM system predicts conditions within a specified prediction horizon. Its intelligent interface alerts the user, presenting the information as appropriate considering the current context and circumstances. We illustrate the system concept with five conceptual use cases, describing which safety metrics may be of the most interest to five user groups and suggesting a multi-modal display format. We posit that having access to information about adverse conditions in time to make efficient preemptive decisions without sacrificing safety will improve the already high level of safety and aid in the expansion planned for the NAS under the Next Generation Air Transportation System (NextGen).
Abstract: Prognostics is the science of making predictions of engineering systems. It is part of a suite of techniques that determine whether a system is behaving within nominal operational bounds and – if it does not – that determine what is wrong and how long it will take until the system no longer fulfills certain functional requirements. This book presents the latest developments and research findings on the topic of prognostics by the Prognostics Center of Excellence at NASA Ames Research Center. The book is intended to provide a practitioner with an understanding of the foundational concepts as well as practical tools to perform prognostics and health management on different types of engineering systems and in particular to predict remaining useful life.
Abstract: This paper presents a computational methodology for uncertainty quantification in predicting the trajectory of a generic, realistic aircraft based on information regarding flight plan, aircraft information, wind and weather information, etc. Predicting the trajectory of aircraft is important from the point of view of analyzing and predicting the safety of the overall airspace, and making risk-informed decisions regarding the operations of the airspace. The proposed methodology is based on using first-principles for analyzing the motion of the aircraft and estimating its future trajectory. Since the core of this problem lies in predicting the future behavior of a generic aircraft, it is essential to understand that it is almost impossible to precisely predict the future trajectory with certainty. Hence, an intuitive approach is to analyze the various sources of uncertainty that affect the aircraft prediction and quantify their combined effect on the whole trajectory. Further, this paper develops a global sensitivity analysis-based methodology to quantify the relative contributions of the various sources of uncertainty to the uncertainty of the overall trajectory. The proposed methodology is illustrated using a numerical example consisting of an aircraft that takes off from the San Francisco International Airport.
Abstract: The operations of a planetary rover depend critically upon the amount of power that can be delivered by its batteries. In order to plan the future operation, it is important to make reliable predictions regarding the end-of-discharge time, which can be used to estimate the remaining driving time and distance. These quantities are stochastic in nature, not only because there are several sources of uncertainty that affect the rover's operation, but also since the future operating conditions cannot be known precisely. This paper presents a computational methodology to predict these stochastic quantities, based on a model of the rover and its batteries. We utilize a model-based prognostics framework that characterizes and incorporates the various sources of uncertainty into these predictions, thereby assisting operational decision-making. We consider two different types of driving scenarios, and develop methods for each to characterize the associated uncertainty. Monte Carlo sampling and the inverse first-order reliability method are used to compute the stochastic predictions of end-of-discharge time, remaining driving time, and remaining driving distance.