Alex Alberts

Academic appointments

Northwestern University · September 2026–present

Visiting Assistant Professor, Department of Engineering Sciences and Applied Mathematics

Purdue University · May 2024–August 2026

Postdoctoral Researcher, School of Mechanical Engineering

Education

PhD in Mechanical Engineering · Purdue University · 2019–2024

Thesis: An information field theory approach to engineering inverse problems
Advisor: Ilias Bilionis

MS in Applied Mathematics · The University of Akron · 2017–2019

Thesis: Analysis of Fano resonance in a cubic nonlinear mechanical system
Advisors: J. Patrick Wilber and D. Dane Quinn

BS in Applied Mathematics · The University of Akron · 2014–2019

Graduated with Honors and summa cum laude

Research interests

Bayesian inverse problems, uncertainty quantification, scientific machine learning, and mathematical physics. My work connects field-theoretic methods with physical inference, including applications in thermal protection, combustion engines, biomedical imaging, and dynamical systems.

Journal publications

1. Alex Alberts, Akshay Jacob Thomas, Kamran Daryabeigi, and Ilias Bilionis. “Bayesian identification of fibrous insulation thermal conductivity towards design of spacecraft thermal protection systems.” Acta Materialia, 318, 122613, 2026. doi:10.1016/j.actamat.2026.122613
2. Maxwell Bolt, Alex Alberts, Akash S. Desai, Peter Meckl, and Ilias Bilionis. “Modeling diesel output particulate matter as the Ornstein-Uhlenbeck process.” International Journal of Engine Research, 2026. doi:10.1177/14680874261471315
3. Alex Alberts and Ilias Bilionis. “An interpretation of the Brownian bridge as a physics-informed prior for the Poisson equation.” SIAM/ASA Journal on Uncertainty Quantification, 14(3), 1008–1044, 2026. doi:10.1137/25M1756831
4. Alex Alberts and Ilias Bilionis. “Bayesian neural networks with interpretable priors from Mercer kernels.” Computer Methods in Applied Mechanics and Engineering, 455, 118896, 2026. doi:10.1016/j.cma.2026.118896
5. Alex Alberts, Akash S. Desai, Ilias Bilionis, and Peter Meckl. “Bayesian reconstruction of intake manifold dynamics with parameter calibration.” ASME Journal of Verification, Validation and Uncertainty Quantification, 2025. doi:10.1115/1.4071047
6. Alex Alberts and Ilias Bilionis. “Physics-informed information field theory for modeling physical systems with uncertainty quantification.” Journal of Computational Physics, 486, 112100, 2023. doi:10.1016/j.jcp.2023.112100

Preprints

• Alex Alberts and Ilias Bilionis. “Uniqueness of MAP estimates for inverse problems under information field theory.” arXiv:2401.14224, 2024. Preprint · Under review at IEEE Transactions on Information Theory. arXiv:2401.14224

Fellowships and awards

• 2023 · Purdue College of Engineering inaugural Travel for Collaborative Research Grant ($4,500).
• 2019 · Lynn Fellowship, Purdue University Computational Interdisciplinary Graduate Programs.

Invited talks

• March 11, 2026 · “Interpretable priors for Bayesian neural networks through information field theory.” SMaRT Seminar Series, Johns Hopkins University, Baltimore, MD.
• January 26, 2026 · “Path integral methods for Bayesian inference.” Data-Oriented Mathematical and Statistical Sciences Seminar, Arizona State University, Tempe, AZ.
• October 17, 2025 · “An information field theory approach to physics-based inverse problems.” Advanced Modeling & Simulations Seminar Series, Texas A&M University–Kingsville, Kingsville, TX.
• June 27, 2025 · “Information field theory for solving Bayesian inverse problems.” CRUNCH Seminar, Brown University, Providence, RI.
• March 28, 2024 · “Bayesian reconstruction of unobserved states in dynamical systems.” Bayes Forum, Max Planck Institute for Astrophysics, Munich, Germany.

Minisymposia organized

• 2026 · Inverse Problems and Uncertainty Quantification in Biological Systems—Parts I and II. SIAM Conference on Uncertainty Quantification. With Atharva Hans, Adrian Buganza Tepole, and Ilias Bilionis.
• 2025 · Scientific Machine Learning for Biological Mechanics Models from Medical Data. SIAM Conference on Computational Science and Engineering. With Atharva Hans, Ilias Bilionis, and Pavlos Vlachos.
• 2024 · Information Field Theory: Towards a Unified Paradigm for Uncertainty Quantification—Parts I and II. SIAM Conference on Uncertainty Quantification. With Ilias Bilionis.

Selected conference presentations

• July 2026 · “Meaningful priors for Bayesian neural networks with applications to physics-based inverse problems.” SIAM Annual Meeting, Cleveland, OH. Alex Alberts.
• March 2026 · “Bayesian neural networks with meaningful functional priors.” SIAM Conference on Uncertainty Quantification, Minneapolis, MN. Alex Alberts and Ilias Bilionis.
• July 2025 · “Bayesian calibration of intake manifold models with varying degrees of error in internal combustion engines.” 18th US National Congress on Computational Mechanics, Chicago, IL. Alex Alberts, Akash S. Desai, Ilias Bilionis, and Peter Meckl.
• May 2025 · “Bayesian reconstruction of intake manifold dynamics with parameter calibration.” SIAM Conference on Applications of Dynamical Systems, Denver, CO. Alex Alberts, Akash S. Desai, Ilias Bilionis, and Peter Meckl.
• March 2025 · “An information field theory framework for model-form error detection.” SIAM Conference on Computational Science and Engineering, Fort Worth, TX. Alex Alberts and Ilias Bilionis.
• February 2024 · “Detection of model-form error through information field theory with physics-informed priors.” SIAM Conference on Uncertainty Quantification, Trieste, Italy. Alex Alberts and Ilias Bilionis.
• June 2023 · “Physics-informed information field theory for modeling physical systems with uncertainty quantification.” 5th International Conference on Uncertainty Quantification in Computational Science and Engineering, Athens, Greece. Alex Alberts and Ilias Bilionis.
• April 2022 · “An information field theory interpretation of physics-informed neural networks.” SIAM Conference on Uncertainty Quantification, Atlanta, GA. Alex Alberts and Ilias Bilionis.
• March 2021 · “Bayesian estimation of a system’s Lagrangian from data.” SIAM Conference on Computational Science and Engineering, virtual. Alex Alberts and Ilias Bilionis.

Teaching experience

Instructor · Northwestern University

• Fall 2026 · GEN_ENG 205-4 — Engineering Analysis IV

Instructor · Purdue University

• Spring 2026 · ME 697 — Advanced Scientific Machine Learning
• Fall 2024, Fall 2025 · ME 539 — Introduction to Scientific Machine Learning

Teaching assistant · Purdue University

• ME 539 — Introduction to Scientific Machine Learning
• ME 297 — Introduction to Data Science for Mechanical Engineers

Teaching assistant · The University of Akron

• MECE:260 — Engineering Analysis I
• MECE:310 — Fluid Mechanics I
• MECE:340 — System Dynamics & Response
• MECE:431 — Fundamentals of Mechanical Vibrations

Service

I am jointly organizing a Guided Affinity Group on inverse problems with Malena Español for the Building Engagement program at SIAM CSE 2027.

Journal reviewing

• ASME Journal of Biomedical Engineering
• Engineering with Computers
• Mechanics Research Communications
• Journal of Computational Physics