Improved safety and service in the Engineering and Mechanical Depts. by leading projects of diverse focus: -Component failure analysis of metal, polymer, and composite materials including large-section railroad steel castings and forgings, track tools, and misc. components-Product evaluations inspecting and testing component manufacturing quality against industry specifications (ASTM, ANSI, SAE, IEEE, AAR, AREMA) and internal specifications including reviewing engineering drawings and material test results-Lead manufacturing facility quality assurance and technical audits resulting in determination of root cause and corrective actions for findings, and follow-up to determine corrective action effectiveness-Updating Norfolk Southern (NS) internal standards for operation critical materials and American Association of Railroads Manual of Standards and Recommended Practices-Interfaced on technical issues with all levels of NS management, NS field personnel, representatives of other railroads and the rail supply industry-Communicated project results via written reports to audiences of diverse technical and non-technical backgrounds-Served as NS alternate on two AAR Technical Committees: Coupling Systems and Truck Castings Technical Committee and Wheel, Axle, Bearing, and Lubrication Committee-Re-invented and automated rail material certificate data review against company specifications by creating a R Shiny dashboard app; data is automatically inserted in a SQL Server database after review-Coordinated and managed manufacturer-supplied material certificate data requirements, ETL, analysis, and storage in SQL Server database-Automated research data file aggregation, filtering, and export of over 10,000 log files for analysis using Python

Lead and conducted failure analysis projects of steel railroad components leading to improved component quality, reliability and safetyPerformed material and product testing (mechanical property testing, microstructural analysis, spectrographic analysis, metrology) to ASTM, AAR, AREMA and manufacturer standardsReviewed and propose changes to industry material and manufacturing standardsReported analysis results to management via written reports

Relevant Coursework and Final Projects:Big Data Systems, PySparkExpected May 2022 May 2016 • Learned Spark for large-scale analytics and machine learning and AWS and Hadoop for data storage and retrieval• Led a team of four students to develop a random forest model in Apache PySpark to predict AQI from gaseous and particulatematter data gathered from USGS resulting in a 99.4% adjusted R2Foundations of Computer Science, Python• Studied algorithm design, data structures, databases and analysis techniques. Including, algorithms for hash tables, trees, queues,sorting, graphs, key-value stores at scale, relational algebra (RA), entity relationship (ER) diagrams, MySQLData Mining, R• Learned regression, classification, clustering, regularization, tree-based methods, ensembles, boosting, and SVM methods• Developed logistic regression, LDA, QDA, KNN, SVM, random forest models to identify blue tarps; the random forest modelperformed best with 99.2% precisionLinear Models for Data Science, R• Studied linear statistical models including simple and multiple linear regression, generalized linear models, time series, analysis ofco-variance, tree-based classification, and principal components• Collaborated with three students to predict tipping occurrence on City of Chicago ride shares with logistic regressionProgramming and Systems for Data Science, Python• Learned programming concepts in Python including structures, techniques, testing and debugging, exception handling, and datavisualization• Partnered with four students to create a Python module called the Drug Awareness Response Tool (DART) to query and visualizedrug epidemic data with Plotly

-Assisted in the development of novel synthesis methods of silicon nanoparticles and ceramic fibers and their characterization resulting in a publication-Co-authored the paper “Size Control of Porous Silicon-Based Nanoparticles via Pore-Wall Thinning” in Langmuir an ACS journal

-Maintained laboratory by preparing solutions, utensils, and autoclaving biohazard trash-Maintained organization of stock items, lab benches, and storage-Performed experiments for the group’s research projects

-Provided excellent customer service in assisting homebuyers while finding and purchasing a home-License maintained (SL3274469)

-Conducted metallurgical analysis on parts for failure analysis -Conducted metallurgical analysis on parts for new product evaluation -Wrote analysis reports-Conducted microhardness research for application in new internal company standards