KU Leuven (Belgium)
Master of Science, Statistics • 2017 — 2019
Completed an interdisciplinary program, accredited by the Royal Statistics Society (UK) with Cum Laude distinction.
- Developed an integrated predictive model (accounting for event history, condition monitoring, and production throughput) used it to identify the reliability of the mechanical equipment, and made maintenance-specific recommendations to improve reliability.
- Used R and Python, to perform extensive preprocessing, cleaning, transformation, and integration of 3 years of stoppage records, vibration measurements, and monthly production totals coming from flat files of various formats (HTML, Excel).
- Performed extensive feature extraction including text mining, natural language processing, and part-of- speech tagging to identify the failure mechanism, maintenance action, and repair status for each failure event.
- Performed a criticality analysis (identifying cement mills and fans as the most critical equipment), disproved presence of trend in inter-failure durations, and identified the potential for event clustering.
- Estimated semi-parametric (stratified extended Cox) and fully parametric (accelerated failure time) models and determined that decreasing production load and replacing (compared to repairing) broken components significantly decreased the risk of failures in cement mills and fans.
University of Michigan
Bachelor of Science, Informatics • 2009 — 2014
Through the Informatics program, I explored a cross-disciplinary approach to the intersection of technology and human interaction. I learned concepts including complex networks (technical and relational), statistics, mathematics, data analysis, usability, and object-oriented programming.
Between my work experience interfacing with international counterparts, pursuing graduate education and abroad, and maintaing a personal and working life all across the globe, I am able to work and thrive in any locale.
Through the work of my masters program I have gained extensive experience in the following areas.
- Machine Learning
- Multivariate Analysis
- Mixed Models
- Experimental Design
- Generalized Linear Models
- Survival Analysis
- Time Series Analysis
- Artificial Neural Networks
- Bayesian Analysis
- Natural Language Processing
I perform a significant amount of development work in order to build and deploy data science solutions, using some of the following frameworks.