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title: Matteo Corbetta
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  <section class="hero">
    <p class="eyebrow">Applied ML Scientist</p>
    <h1>Matteo Corbetta, Ph.D.</h1>
    <p class="hero-summary">
      10+ years of experience in probabilistic modeling, sensor-driven systems, and AI for industrial and safety-critical environments.
    </p>
    <div class="contact-row">
      <a href="https://github.com/matteocorbetta">GitHub</a>
      <a href="https://www.linkedin.com/in/mcorbetta">LinkedIn</a>
      <a href="https://www.semanticscholar.org/search?q=Matteo%20corbetta&sort=relevance">Semantic Scholar</a>
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      <p class="section-kicker">Profile</p>
      <h2>Focus Areas</h2>
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    <div class="surface-card">
      <ul class="focus-list">
        <li>Time-series anomaly detection, state estimation, and sensor fusion.</li>
        <li>Forecasting and uncertainty-aware decision making for high-stakes systems.</li>
        <li>End-to-end delivery from low-TRL research to production-grade C++ and cloud deployments.</li>
        <li>Open-source research software, proposal leadership, and cross-functional ML execution.</li>
      </ul>
      <div class="stack-row">
        <span class="stack-label">Tech stack</span>
        <p>Python, PyTorch, Scikit-Learn, JAX, TensorFlow, Ray, Pandas, SQL, C++, Docker, AWS, GCP, Kubernetes, LangChain.</p>
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  <section class="section">
    <div class="section-heading">
      <p class="section-kicker">Track Record</p>
      <h2>Selected Accomplishments</h2>
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        <h3>Business Outcomes</h3>
        <ul>
          <li>Increased reach of an automotive wheel alignment monitoring system to more than 250,000 additional potential customers.</li>
          <li>Led a team of 3 building a multi-agent workflow that enabled onboarding of a new customer for a Series A GenAI startup.</li>
          <li>Designed, implemented, and deployed an AI-based root cause analysis workflow for cloud FinOps cost spikes.</li>
        </ul>
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      <article class="surface-card">
        <h3>Awards and Recognition</h3>
        <ul>
          <li>Core algorithms contributor to NASA’s 2024 Software of the Year: ProgPy.</li>
          <li>OneKBR Award for outstanding work in NASA’s Diagnostics and Prognostics Task, 2023.</li>
          <li>Outstanding Reviewer for the Prognostics and Health Management Society, 2018.</li>
          <li>Best paper award at the European Prognostics and Health Management Conference, Bilbao, 2016.</li>
          <li>Third-place best paper at ESREL, Amsterdam, 2013.</li>
        </ul>
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    <article class="surface-card section-block">
      <h3>Scientific and Technical Contributions</h3>
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          <h4>Awarded Project Proposals</h4>
          <ul>
            <li>“Physics-aware quantum neural network modeling of Earth science phenomena”, NASA Ames AIST 2024.</li>
            <li>“Acoustic Data-Based 0-gravity Boiling Characterization”, NASA Ames CIF 2023.</li>
            <li>“Physics-Informed Neural Networks for Next-Gen Electric Aircraft”, NASA Ames CIF 2022.</li>
          </ul>
        </div>
        <div>
          <h4>Conferences and Societies</h4>
          <ul>
            <li>Panelist at SuperComputing 2022: “Physics-Informed Machine Learning meets High Performance Computing”.</li>
            <li>Member of the Editorial Board for the Prognostics and Health Management Society, 2017 to 2024.</li>
            <li>Presented technical work at more than a dozen conferences and workshops.</li>
            <li>Reviewer for scientific journals and conferences for more than a decade.</li>
          </ul>
        </div>
        <div>
          <h4>Invention Disclosures and Patents</h4>
          <ul>
            <li>Coolant Pump and Valve Prognostic Strategy, Ford Motor Company, 2024.</li>
            <li>Bayesian network for fault isolation of UAV electrical powertrain, KBR and NASA, 2022.</li>
            <li>Vibration-based monitoring of wind turbine direct-drive generators, Siemens Gamesa, 2017.</li>
          </ul>
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  <section class="section">
    <div class="section-heading">
      <p class="section-kicker">Selected Work</p>
      <h2>Selected Work</h2>
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    <h3 class="subsection-title">  </h3>
    <div class="work-grid">
      <a class="work-card" href="https://github.com/nasa/progpy">
        <span class="work-type">Open source</span>
        <strong>ProgPy</strong>
        <span>NASA prognostics and health management software.</span>
      </a>
      <a class="work-card" href="https://www.nature.com/articles/s41598-023-33018-0">
        <span class="work-type">Publication</span>
        <strong>Battery Hybrid PIML</strong>
        <span>Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis.</span>
      </a>
      <a class="work-card" href="https://ntrs.nasa.gov/api/citations/20210013485/downloads/Towards_The_True_Hybrid_ENHANCE_2021.pdf">
        <span class="work-type">Presentation</span>
        <strong>PIML for Prognostics and Health Management</strong>
        <span>NASA presentation on hybrid physics-informed approaches for PHM applications.</span>
      </a>
      <a class="work-card" href="https://ntrs.nasa.gov/api/citations/20220013273/downloads/SMG_final.pdf">
        <span class="work-type">Technical report</span>
        <strong>Spectral Mass Gauging</strong>
        <span>Applied modeling work on fluid state estimation and sensing in aerospace contexts.</span>
      </a>
    </div>
    <h3 class="subsection-title subsection-title-publications">   </h3>
    <div class="publication-list">
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<article class="publication-entry">
  <h4>ProgPy: Python Packages for Prognostics and Health Management of Engineering Systems <span>(2023)</span></h4>
  <p>C. Teubert, K. Jarvis, Matteo Corbetta, Chetan S. Kulkarni, M. Daigle</p>
  <a href="https://www.semanticscholar.org/paper/2c9729ed8889e29aaf30678920146489902818f1">Journal of Open Source Software</a>
</article>

<article class="publication-entry">
  <h4>Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis <span>(2021)</span></h4>
  <p>R. Nascimento, Matteo Corbetta, Chetan S. Kulkarni, F. Viana</p>
  <a href="https://www.semanticscholar.org/paper/df0bfac3688f289e8552962db934d9395b51d87e">Journal of Power Sources</a>
</article>

<article class="publication-entry">
  <h4>Application of sparse identification of nonlinear dynamics for physics-informed learning <span>(2020)</span></h4>
  <p>Matteo Corbetta</p>
  <a href="https://www.semanticscholar.org/paper/71216fa1681378852870cd0fb1b8a6819c6212d5">IEEE Aerospace Conference</a>
</article>

<article class="publication-entry">
  <h4>Comparison of Surrogate Modeling Techniques for Life Cycle Models of Advanced Air Mobility <span>(2023)</span></h4>
  <p>A. Pohya, G. Wende, Matteo Corbetta, Chetan S. Kulkarni</p>
  <a href="https://www.semanticscholar.org/paper/ffda97d0bce652ce12c4116049ebe260654131c4">AIAA AVIATION 2023 Forum</a>
</article>

<article class="publication-entry">
  <h4>Particle filtering‐based adaptive training of neural networks for real‐time structural damage diagnosis and prognosis <span>(2019)</span></h4>
  <p>F. Cadini, C. Sbarufatti, Matteo Corbetta, Francesco Cancelliere, M. Giglio</p>
  <a href="https://www.semanticscholar.org/paper/a23b8e7705d47fbc5877b5fbdd6b9c5d13e56989">Structural Control &amp; Health Monitoring</a>
</article>

<article class="publication-entry">
  <h4>Hybrid Modeling of Unmanned Aerial Vehicle Electric Powertrain for Fault Detection and Diagnostics <span>(2023)</span></h4>
  <p>Matteo Corbetta, K. Jarvis, S. Schuet</p>
  <a href="https://www.semanticscholar.org/paper/6489e5efc946d89974e92de08ebf080ea22e73b8">AIAA AVIATION 2023 Forum</a>
</article>

<article class="publication-entry">
  <h4>Systems Health Monitoring: Integrating FMEA into Bayesian Networks <span>(2021)</span></h4>
  <p>Chetan S. Kulkarni, Matteo Corbetta, E. Robinson</p>
  <a href="https://www.semanticscholar.org/paper/bf956074b76b55a50bbdf01999604df2f3388332">IEEE Aerospace Conference</a>
</article>

<article class="publication-entry">
  <h4>Uncertainty Quantification of Expected Time-of-Arrival in UAV Flight Trajectory <span>(2021)</span></h4>
  <p>P. Banerjee, Matteo Corbetta</p>
  <a href="https://www.semanticscholar.org/paper/c298d8a1a9426876011a0eaef4c8cdd5cc6fa0db">AIAA AVIATION 2021 FORUM</a>
</article>

<article class="publication-entry">
  <h4>On the performance of a cointegration-based approach for novelty detection in realistic fatigue crack growth scenarios <span>(2019)</span></h4>
  <p>M. Salvetti, C. Sbarufatti, E. Cross, Matteo Corbetta, K. Worden, M. Giglio</p>
  <a href="https://www.semanticscholar.org/paper/332cc36dc003553433ae0a2e3481991f7c7a4b98">Mechanical systems and signal processing</a>
</article>

<article class="publication-entry">
  <h4>Approach for Uncertainty Quantification And Management Of Unmanned Aerial Vehicle Health <span>(2019)</span></h4>
  <p>Matteo Corbetta, Chetan S. Kulkarni</p>
  <a href="https://www.semanticscholar.org/paper/a38c5b171656509cc02175467e965bc731295ad7">Annual Conference of the PHM Society</a>
</article>

<article class="publication-entry">
  <h4>Enabling in-time prognostics with surrogate modeling through physics-enhanced Dynamic Mode Decomposition method <span>(2022)</span></h4>
  <p>K. Jarvis, Matteo Corbetta, C. Teubert, S. Schuet</p>
  <a href="https://www.semanticscholar.org/paper/c9e622088bda94d24dcd31687e51d301efc563b4">Annual Conference of the PHM Society</a>
</article>

<article class="publication-entry">
  <h4>Accelerating uncertainty propagation in power laws for prognostics and health management <span>(2020)</span></h4>
  <p>Matteo Corbetta</p>
  <a href="https://www.semanticscholar.org/paper/d9504110479eae052734fdcfc282a75cd08e70ca">IEEE Aerospace Conference</a>
</article>

<article class="publication-entry">
  <h4>Optimal tuning of particle filtering random noise for monotonic degradation processes <span>(2016)</span></h4>
  <p>Matteo Corbetta, C. Sbarufatti, M. Giglio</p>
  <a href="https://www.semanticscholar.org/paper/1fa6be5d79753df9b600985855bbbd14a300f7aa">PHM Society European Conference</a>
</article>

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