Applied ML Scientist

Matteo Corbetta, Ph.D.

10+ years of experience in probabilistic modeling, sensor-driven systems, and AI for industrial and safety-critical environments.

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Profile

Focus Areas

  • Time-series anomaly detection, state estimation, and sensor fusion.
  • Forecasting and uncertainty-aware decision making for high-stakes systems.
  • End-to-end delivery from low-TRL research to production-grade C++ and cloud deployments.
  • Open-source research software, proposal leadership, and cross-functional ML execution.
Tech stack

Python, PyTorch, Scikit-Learn, JAX, TensorFlow, Ray, Pandas, SQL, C++, Docker, AWS, GCP, Kubernetes, LangChain.

Track Record

Selected Accomplishments

Business Outcomes

  • Increased reach of an automotive wheel alignment monitoring system to more than 250,000 additional potential customers.
  • Led a team of 3 building a multi-agent workflow that enabled onboarding of a new customer for a Series A GenAI startup.
  • Designed, implemented, and deployed an AI-based root cause analysis workflow for cloud FinOps cost spikes.

Awards and Recognition

  • Core algorithms contributor to NASA’s 2024 Software of the Year: ProgPy.
  • OneKBR Award for outstanding work in NASA’s Diagnostics and Prognostics Task, 2023.
  • Outstanding Reviewer for the Prognostics and Health Management Society, 2018.
  • Best paper award at the European Prognostics and Health Management Conference, Bilbao, 2016.
  • Third-place best paper at ESREL, Amsterdam, 2013.

Scientific and Technical Contributions

Awarded Project Proposals

  • “Physics-aware quantum neural network modeling of Earth science phenomena”, NASA Ames AIST 2024.
  • “Acoustic Data-Based 0-gravity Boiling Characterization”, NASA Ames CIF 2023.
  • “Physics-Informed Neural Networks for Next-Gen Electric Aircraft”, NASA Ames CIF 2022.

Conferences and Societies

  • Panelist at SuperComputing 2022: “Physics-Informed Machine Learning meets High Performance Computing”.
  • Member of the Editorial Board for the Prognostics and Health Management Society, 2017 to 2024.
  • Presented technical work at more than a dozen conferences and workshops.
  • Reviewer for scientific journals and conferences for more than a decade.

Invention Disclosures and Patents

  • Coolant Pump and Valve Prognostic Strategy, Ford Motor Company, 2024.
  • Bayesian network for fault isolation of UAV electrical powertrain, KBR and NASA, 2022.
  • Vibration-based monitoring of wind turbine direct-drive generators, Siemens Gamesa, 2017.

Selected Work

Selected Work

Open source ProgPy NASA prognostics and health management software. Publication Battery Hybrid PIML Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis. Presentation PIML for Prognostics and Health Management NASA presentation on hybrid physics-informed approaches for PHM applications. Technical report Spectral Mass Gauging Applied modeling work on fluid state estimation and sensing in aerospace contexts.

ProgPy: Python Packages for Prognostics and Health Management of Engineering Systems (2023)

C. Teubert, K. Jarvis, Matteo Corbetta, Chetan S. Kulkarni, M. Daigle

Journal of Open Source Software

Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis (2021)

R. Nascimento, Matteo Corbetta, Chetan S. Kulkarni, F. Viana

Journal of Power Sources

Application of sparse identification of nonlinear dynamics for physics-informed learning (2020)

Matteo Corbetta

IEEE Aerospace Conference

Comparison of Surrogate Modeling Techniques for Life Cycle Models of Advanced Air Mobility (2023)

A. Pohya, G. Wende, Matteo Corbetta, Chetan S. Kulkarni

AIAA AVIATION 2023 Forum

Particle filtering‐based adaptive training of neural networks for real‐time structural damage diagnosis and prognosis (2019)

F. Cadini, C. Sbarufatti, Matteo Corbetta, Francesco Cancelliere, M. Giglio

Structural Control & Health Monitoring

Hybrid Modeling of Unmanned Aerial Vehicle Electric Powertrain for Fault Detection and Diagnostics (2023)

Matteo Corbetta, K. Jarvis, S. Schuet

AIAA AVIATION 2023 Forum

Systems Health Monitoring: Integrating FMEA into Bayesian Networks (2021)

Chetan S. Kulkarni, Matteo Corbetta, E. Robinson

IEEE Aerospace Conference

Uncertainty Quantification of Expected Time-of-Arrival in UAV Flight Trajectory (2021)

P. Banerjee, Matteo Corbetta

AIAA AVIATION 2021 FORUM

On the performance of a cointegration-based approach for novelty detection in realistic fatigue crack growth scenarios (2019)

M. Salvetti, C. Sbarufatti, E. Cross, Matteo Corbetta, K. Worden, M. Giglio

Mechanical systems and signal processing

Enabling in-time prognostics with surrogate modeling through physics-enhanced Dynamic Mode Decomposition method (2022)

K. Jarvis, Matteo Corbetta, C. Teubert, S. Schuet

Annual Conference of the PHM Society

Accelerating uncertainty propagation in power laws for prognostics and health management (2020)

Matteo Corbetta

IEEE Aerospace Conference

Optimal tuning of particle filtering random noise for monotonic degradation processes (2016)

Matteo Corbetta, C. Sbarufatti, M. Giglio

PHM Society European Conference