George Felobes

• Engineered Python scripts for economic modeling, enhancing efficiency by 20% and reducing emissions by 15% in energy facilities.
• Led stakeholder collaborations, resulting in a 30% increase in the adoption of advanced process control technologies aligned with financial goals.
• Developed user-friendly HMI solutions using Python Dash, improving operational monitoring efficiency by 25% and decision-making speed by 40%.
• Optimized robotic movements using advanced algorithms, achieving a 35% improvement in system performance and a 20% reduction in process control errors.

Smart Infrastructure Technologies (SITE) Research Group, Dr. Qipei (Gavin) Mei. Publication in progress

• Using LLMs for extracting data from engineering codes and standards, Structural Engineering
• Generative Design: Utilized natural language to generate structural engineering generative designs and developed Grasshopper environments for parametric modeling of structural elements.
• Data Automation and Machine Learning: Scripted Python scripts to create training datasets, fine-tuned language models to configure parametric designs, and built reinforcement learning algorithms to optimize Grasshopper parametric environments

• Built data exploration techniques to identify inconsistencies in physics equations in data historians with millions of data points, with high frequencies with complex time series behaviour
• Led multiple projects in Operations Process Engineering and Production Engineering, focusing on process simulations, equipment designs, and performance metrics.
  Achieved substantial cost savings: $500K in EB spending and $700K annually from reduced Dilburn loss.
  Enhanced FWKO temperature optimization, resulting in a 1.42% decrease in fuel consumption and approximately $2 million in annual savings.

• Automation Skills: Proficient in Python scripting to automate workflows and analyses, resulting in substantial time savings for senior engineers
• Web Development Expertise: Experienced in developing front-end Dash web applications, connecting data sources via PIweb-API, and creating analytics dashboards using Python Dash, Power BI, and TIBCO Spotfire
• Code Optimization: Successfully refactored and tested 10,000 lines of VBA code into Python, achieving a six-fold increase in execution speed
• Machine Learning Proficiency: Proficient in implementing machine learning algorithms like PCA, t-SNE, regressions, and neural networks using open-source libraries, and running large models on AWS Sagemaker notebook instances

Numerical physics simulations and competitor statistical analysis using R programming, leading to NPV9 of $5MM

Optimized the maintenance strategies as it relates to the following: preventative, predictive, job plans and spare parts for expansion joints in Foster Creek leading to over $100K of savings