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Statistics and Machine Learning Researcher and Software Developer

Bozeman, MT  ·  jordans1882@gmail.com  ·  jordanschupbach.us  ·  github.com/jordanschupbach

Research Profile

Statistics and machine learning researcher developing rigorous, computationally practical methods for complex and high-dimensional data. Interested in bridging statistical theory, optimization, and scientific applications, with particular emphasis on reliable inference and principled quantification of uncertainty. Programming enthusiast with a strong interest in software development, scientific computing, and translating new methods into dependable software.

Research Interests

Statistical methodology
Functional data analysis; spatial statistics; point processes; topological data analysis; nonparametric statistics; hypothesis testing; optimal experimental design; uncertainty quantification; Bayesian statistics.
Computation and learning
Digital pathology; numerical optimization; statistical machine learning; manifold learning; neural networks; computer vision; simulation-based methods; scientific software development.

Education

Research Experience

Selected Publications and Preprints

  1. E. Ginsberg, J. Schupbach, J. Sheppard, and N. Turk. “Applying Factored Evolutionary Algorithms to the B-Spline Knot Selection Problem.” 2025 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 21–25, 2025.
  2. B. Holmgren, E. Quist, J. Schupbach, B. T. Fasy, and B. Rieck. “The Manifold Density Function: An Intrinsic Method for the Validation of Manifold Learning.” arXiv:2402.09529, 2024.
  3. J. Schupbach, E. Pryor, K. Webster, and J. Sheppard. “A Risk-Based Approach to Prognostics and Health Management Combining Bayesian Networks and Continuous-Time Bayesian Networks.” IEEE Instrumentation & Measurement Magazine, vol. 26, no. 5, pp. 3–11, 2023.
  4. J. Schupbach, E. Pryor, K. Webster, and J. Sheppard. “Combining Dynamic Bayesian Networks and Continuous Time Bayesian Networks for Diagnostic and Prognostic Modeling.” 2022 IEEE AUTOTESTCON, pp. 1–8, 2022.
  5. J. Schupbach, J. W. Sheppard, and T. Forrester. “Quantifying Uncertainty in Neural Network Ensembles Using U-Statistics.” 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–8, 2020.
  6. R. L. Belton, B. T. Fasy, R. Mertz, S. Micka, D. L. Millman, D. Salinas, A. Schenfisch, J. Schupbach, and L. Williams. “Reconstructing Embedded Graphs from Persistence Diagrams.” Computational Geometry, vol. 90, article 101658, 2020.
  7. P. Lawson, J. Schupbach, B. T. Fasy, and J. W. Sheppard. “Persistent Homology for the Automatic Classification of Prostate Cancer Aggressiveness in Histopathology Images.” Proceedings of SPIE, vol. 10956, 2019.
  8. B. T. Fasy, E. Quist, A. Schenfisch, J. Schupbach. “A Generative Model for Simulating Prostate Cancer Nuclei.” In Manuscript.
  9. J. Borkowski, M. Greenwood, B. T. Fasy, J. Schupbach, J. Sheppard. “Mixed-curve Modeling for Repeated Measures Topological Data Analysis.” In Manuscript.
  10. J. Borkowski, M. Greenwood, J. Schupbach, J. Sheppard. “Mixed-Effect Hypothesis Testing for Repeated Measures Functional Data.” In Manuscript.
  11. J. Borkowski, M. Greenwood, J. Schupbach, J. Sheppard. “Modeling and Model Selection with Hierarchical P-splines.” In Manuscript.

Selected Projects

Teaching and Mentoring

Technical Skills

Core languages
C++, C#, Java, JavaScript, R, Python
Additional exposure
D, Fortran, Lisp, Lua, OCaml, Perl, PHP, Ruby, Tcl, and more
Methods
Statistical inference, Bayesian computation, optimization, machine learning, simulation studies, experimental design, reproducible research
Development
Scientific computing, software design, testing, Git, Linux/macOS/Windows, Nix, Kubernetes, PyTorch, TensorFlow, high-performance computing, literate programming, reproducible workflows

Honors and Service