Anton Fadic
Data Scientist
I have a passion for bridging theory and first principles into real physical applications
Summary
I have a passion for computational physics and statistics. My main interest is to bridge these more theoretical fields into applications that become actionable insights by decision makers. Currently interested in Fracture Mechanics, Fitness-for-Service, Risk, Probabilistic Assessments, and Machine Learning applications with a focus on pipeline integrity. Also interested in Computational Fluid Mechanics, Reacting Flow, Statistical Mechanics and various mathematical topics.
Experience
Data Scientist
Irth Solutions / OneBridge Solutions
Implemented and automated Fitness-for-Service methodologies for piggable and hydrotested lines. Developed methodology towards best practices usage of ML in pipeline integrity. Investigated and developed probabilistic methodologies for quantitative risk assessments for cracks, including fatigue effects. Investigated and developed ILI-to-ILI alignment algorithms.
- Developed and deployed production quality Fitness-for-Service software used daily by 17 large pipeline operators
- Found and published the effects of autocorrelation in Machine Learning pointing towards the issue of data leakage under the random splitting assumption
- Speed up probabilistic assessments by 75-94%
- Studied the effects of corrosion clustering around elevation valleys, quantifying the effect with statistically rigorous methods
Visiting Researcher
TU Darmstadt
Research focused on first principles approach modeling of catalytic reactors for industrial applications. Developed surrogate models for computationally expensive CFD simulations.
Education
Ph.D. in Chemical Engineering
University of Alberta
Thesis: A study of the Nitrous Oxide production in the Nitric Acid process
M.S. in Chemical Engineering
University of Alberta
B.S. in Industrial Engineering
Universidad Tecnica Federico Santa Maria
Skills
Pipeline Integrity
API 579, CSA Z662, DNV-RP-F101, API 1176, API 1163, API 1160, BS 7910, B31.4, B31.8 and B31.8S, B31G, PRCI MAT-8, 49 CFR 192 and 195
Computational Physics
CFD, FEA, Linear Elasticity, Stress Intensity Factor, Turbulent Flow Simulation, Boundary Layer Analysis, Numerical Methods
Machine Learning
XGBoost, Neural Networks, Overfitting, SHAP, Data Leakeage
Software and Deployment
Linux, Azure, git, Kubernetes, Docker, C, Python, Ansys, OpenFOAM, Comsol Multiphysics
Process Engineering
Reaction Kinetics, Thermodynamics, Computational Transport Phenomena, Fluid Mechanics
Selected Publications
- • SCC Susceptibility Modeling to Demonstrate New Guidelines for Machine Learning in Pipeline Integrity — International Pipeline Conference (Accepted), 2026
- • False Confidence in Machine Learning: Autocorrelation and its Consequences in Pipeline Integrity — International Pipeline Conference (Accepted), 2026
- • Probabilistic Crack Fatigue Analysis: Accelerating Rare Event Risk Evaluation With Sobol sampling — International Pipeline Conference (Accepted), 2026
- • Quantifying Corrosion Patterns: A Study of Corrosion Clustering Around Elevation Valleys With a Large Inline Inspection Dataset — International Pipeline Conference, 2024
- • N2O selectivity in industrial NH3 oxidation on Pt gauze is determined by interaction of local flow and surface chemistry: A simulation study using mechanistic kinetics — Chemical Engineering Science, 2022
- • Study of Nitrous Oxide Production in the Nitric Acid Process — Dissertation, University of Alberta, Edmonton, 2018
- • Volatility forecast using hybrid neural network models — Expert Systems with Applications, 2014
- • A case study in multi-scale model reduction: The effect of cell density on catalytic converter performance — The Canadian Journal of Chemical Engineering, 2014
- • CFD modelling of the automotive catalytic converter — Catalysis Today, 2012