Research Assistant
Soft Matter & Interfaces Research Group — University of Alberta
Edmonton, AB
May 2025 – Sept 2025
Applied machine learning to chemical-engineering and industrial pipeline problems, from exploratory analysis through validated surrogate models and technical reporting.
Key Achievements
- ▸Collaborated with post-doctoral researchers to design ML surrogate models that reduced computational time for complex CFD simulations.
- ▸Led work with proprietary Imperial Oil operational data to develop predictive ANN models for slurry waste pipeline pressure drop under varying flow and material conditions.
- ▸Built end-to-end data pipelines: cleaning, feature engineering from sensor and experimental data, model training/validation, and hyperparameter tuning for stable predictions.
- ▸Presented results in technical reports and visualizations, informing research decisions and discussions on industrial deployment of ML surrogates.
- ▸Performed exploratory data analysis on high-dimensional sensor data to support feature selection for downstream models.
- ▸Evaluated generalization on unseen conditions using cross-validation and held-out test sets to support industrial decision-making.