PhD Engineer | Estimation, Machine Learning, Sensor Data Systems

Quade Butler

I develop algorithms and analysis pipelines that turn noisy sensor and engineering data into reliable estimates, diagnostics, and decisions.

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Portrait of Quade Butler

About

Algorithms and data systems for sensor-driven physical systems.

My PhD research at McMaster University with the ICE Lab sits between applied machine learning, signal processing, controls, numerical methods, and experimental validation. It has produced nonlinear filtering algorithms, MATLAB and Python analysis pipelines, and validation workflows for sensor-driven dynamic systems.

My mechanical engineering background gives me a practical foundation in physical systems, controls, manufacturing, and experimental validation. I focus on building reliable methods for complex physical and industrial systems, from state estimation and sensor fusion to condition monitoring and predictive maintenance.

Education

Academic Background

PhD in Mechanical Engineering

McMaster University | ICE Lab | 2021-2025

Research focused on nonlinear Bayesian state estimation, double exponential cubature methods, robust filtering, sensor fusion, and condition monitoring applications for dynamic physical systems.

Awarded the Ontario Graduate Scholarship for the 2023-2024 academic year.

Bachelor of Engineering in Mechanical Engineering

University of Guelph | Minor in Mathematics

Undergraduate foundation in mechanical systems, engineering design, applied mathematics, controls, numerical methods, and experimental analysis.

Experience

Research, data pipelines, controls, and system validation.

PhD Researcher

McMaster University | ICE Lab | 2021-2025

  • Developed nonlinear Gaussian filtering methods based on double exponential cubature for Bayesian state estimation and sensor fusion.
  • Built MATLAB simulation and benchmarking frameworks for UKF, CKF, Gauss-Hermite, DECKF, and robust filtering comparisons.
  • Evaluated estimator performance using RMSE, NEES, NIS, Monte Carlo simulation, and statistical consistency testing.

Undergraduate Researcher

University of Guelph | 2019-2020

  • Studied strain partitioning in advanced high strength automotive steels using in-situ SEM tensile testing and digital image correlation.
  • Prepared specimens, captured SEM image sequences, optimized DIC parameters, and generated strain field visualizations.
  • Contributed to peer reviewed research on microstructural deformation in multi-phase steels.

Production Engineering Co-op Student

FIO Automotive | January-August 2018

  • Supported hot stamping production lines and robotic welding cells for automotive structural components.
  • Designed pneumatically actuated part stockers, CAD components, safety placards, and spare parts reliability documentation.
  • Evaluated thermal imaging systems and supported hardness, microstructure, and quality testing of hot stamped steel parts.

Continuous Improvement Co-op Student

GEEP Canada | May-August 2017

  • Developed SOPs, process documentation, and engineering reports for electronics recycling operations.
  • Designed workplace improvements including tool board systems, workstation cooling supports, process layouts, and material staging analyses.
  • Supported maintenance, commissioning, quality testing, and process improvement work across recycling process lines.

Teaching Assistant

McMaster University

  • Supported multiple semesters of differential equations, along with courses in mechanical vibrations, engineering design, engineering measurements, and mechatronics.
  • Assisted with tutorials, labs, grading, student support, and technical communication across modeling, measurement, design, and dynamic systems topics.

Research

Selected research interests and outputs.

Research Summary

My research develops estimation, identification, and monitoring methods for nonlinear dynamic systems. Recent work includes a Double Exponential Cubature Kalman Filter for nonlinear Gaussian filtering, robust filtering under outliers and model uncertainty, operating mode and fault detection for magnetorheological dampers, and sensorless condition monitoring for machine tool feed drives.

Journal Articles

Selected journal publications

  • Q. Butler, Y. Ziada, and S. A. Gadsden, "Gaussian Filtering Using a Spherical-Radial Double Exponential Cubature," IEEE Open Journal of Signal Processing, 2025. DOI
  • A. Bardelcik and Q. Butler, "Strain Partitioning Characterization of Advanced High Strength Steels Using In-Situ Tensile Tests with Micro Digital Image Correlation - Methodology and Analysis," Materials Characterization, 2024. DOI
  • B. S. Sicard, Q. Butler, Y. Ziada, E. Hughey, and S. A. Gadsden, "Preload Loss Detection in a Ball Screw System Using Interacting Models," IEEE Open Journal of Instrumentation and Measurement, 2023. DOI
  • Q. Butler, Y. Ziada, D. Stephenson, and S. A. Gadsden, "Condition Monitoring of Machine Tool Feed Drives: A Review," ASME Journal of Manufacturing Science and Engineering, 2022. DOI

Conference Proceedings

Selected conference publications

  • P. Kosierb, Y. Wu, Q. Butler, et al., "System Identification and State Estimation for Magnetorheological Dampers," SPIE Sensor Fusion and Target Recognition XXXIV, 2025.
  • B. Sicard, Y. Wu, Q. Butler, and S. A. Gadsden, "Friction Modeling and Monitoring for Machine Tool Health Management," IEEE Prognostics and Health Management, 2025.
  • B. Sicard, Q. Butler, et al., "Generating Synthetic Data for Artificial Intelligence Solutions via a Digital Twin for Condition Monitoring in Machine Tools," SPIE Synthetic Data for AI & ML II, 2024.
  • Q. Butler, et al., "Nonlinear Filtering Using the Double Exponential Transformation," SPIE Sensor Fusion & Target Recognition XXXIII, 2024.
  • Q. Butler, et al., "Generalizing the Unscented Kalman Filter for State Estimation," SPIE Sensor Fusion & Target Recognition XXXII, 2023.
  • Q. Butler, B. Sicard, et al., "Rapid Parameter Estimation and Monitoring of CNC Feed Drive Systems," SPIE Sensor Fusion & Target Recognition XXXI, 2022.

Featured Paper

Gaussian Filtering Using a Spherical-Radial Double Exponential Cubature

Q. Butler, Y. Ziada, and S. A. Gadsden, IEEE Open Journal of Signal Processing, 2025.

This paper introduces the Double Exponential Cubature Kalman Filter (DECKF), a nonlinear Gaussian state estimator that uses a spherical-radial double exponential cubature rule to approximate Gaussian-weighted integrals in Bayesian filtering.

  • Derived the DE cubature construction, cubature points and weights, and full DECKF prediction/update recursion.
  • Included a stochastic stability analysis under stated assumptions on the nonlinear system and noise model.
  • Validated the method through numerical integration studies and nonlinear target tracking examples.
  • Benchmarked estimator accuracy and consistency against UKF, CKF, and Gauss-Hermite filtering baselines.
Read Open Access Paper

Resume/CV

Downloadable materials.

CV PDF Download CV

Contact

Connect

Email quadedbutler@gmail.com LinkedIn linkedin.com/in/quadebutler GitHub github.com/quade-butler Google Scholar Publication profile