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machine learning
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M.Sc. degree in Process Control Engineering, with industrial project experience B.Eng. degree in Electric Engineering with solid background in automation Experience of control (PID, MPC, DCS, PLC) and data analytics Ability to read piping and instrumentation diagram (P&ID), process flow diagram (PFD), plant layouts Knowledge of various programming languages and software (Matlab, R, Jave, SPSS, Python, SAS, MS Excel) Software development (developed a data analytics toolbox)
University of Alberta
Master’s Degree, process control
January 1, 2013 – January 1, 2015
Jiangnan University
Bachelor of Engineering (B.Eng.), Electrical and Electronics Engineering
January 1, 2008 – January 1, 2012
University of Alberta
Research Assistant
November 1, 2015 – January 1, 2017
University of Alberta
Master Student
September 1, 2013 – November 1, 2015
Slurry pipeline transient data analysis
November 1, 2015 – March 1, 2016
Transport of large volumes of oil sand slurry via pipeline creates major integrity issues in the Alberta Oil Sands pipelines. Therefore, methods to help prolong pipeline life and lower maintenance costs are essential. It is necessary to investigate available data mining methods to explore the impact of operating process conditions on measured slurry pipeline wall volume loss and also be able to predict wear losses in new and existing materials and ultimately determine good operating conditions that can reduce material wear in pipelines. The overall purpose of this project was to identify key process variables and correlations to discover relationships (models) between measured wear rates and operating conditions of slurry pipelines. The steps undertaken to complete this study include data preparation, clustering analysis, identifying relationships between key process variables, identifying relationships between wear rates, identifying correlations between pipeline wear rate and process conditions, and time delay analysis. Various modeling methods (both linear and nonlinear) were applied and compared to find predictive capabilities of the different models. The methods include ...
Predictive Model for Plant Wide Optimization
September 1, 2014 – December 1, 2014
Production rates including froth and bitumen production rates are very important in the oil field industry. Online measurements of these parameters is available. However, it is always better to predict the production rate. A predictive model is to capture the trend of production and estimate future output value given input from a few steps ahead.
Engineering Codes Essentials for the Oil & Gas Industry
University of Alberta
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's background is heavily focused on academic research within the oil and gas industry, specifically related to process control and data analysis for industrial applications. While this aligns well with roles requiring deep analytical skills and problem-solving, the diversity of projects and exposure to different industry contexts or team structures is limited. The target role of 'Data Analyst' is a good fit for the technical skills demonstrated, but the breadth of experience beyond academic research is not evident, which might impact adaptability to diverse team environments or business challenges.
Soft Skills & Operational Fit
The candidate's experience as a Research Assistant and Master Student suggests an ability to work independently on complex problems and conduct in-depth analysis. The project descriptions indicate a methodical approach to problem-solving, including data preparation, clustering, correlation analysis, and model comparison. However, there is no direct information on team collaboration, stress handling, or communication clarity in a professional setting outside of academic research.