Meaghan Kosmatka

Senior Engineer, Deere & Company

Meaghan is a Senior Engineer at John Deere Power Systems within the Applied Mechanics group and is currently working on development of high-fidelity mechanical damage/product life models and methodologies to further John Deere’s™ understanding of product usage. Meaghan has a vast knowledge of diesel engine development having held positions in engine controller software development, engine and aftertreatment calibration, base engine development and product verification/validation. With her multi-faceted skills, she has helped John Deere to continually increase their capabilities allowing the company to develop a deep understanding of their customer usage and develop robust solutions to meet their needs.

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Enabling Physics and Empirical-Based Algorithms with Spark Using the Integration of MATLAB in DatabricksSummit 2020

John Deere is a leading manufacturer of agricultural, construction and forestry machinery, diesel engines, drivetrains for a variety of applications ranging from lawn care to heavy equipment. The company collects large transient engineering datasets from John Deere test vehicles in the field, and via telematic data-loggers. The goal is to leverage physics / empirical-based strategies / algorithms for predictive life cycles / damage on engine components. This technology has allowed our organization to do very little re-work of our algorithms which were based in a MATLAB environment (including all the added functionality that MATLAB has robustly built in), so that the algorithms / models execute efficiently and accurately, on duty cycles that may have never been originally defined with engine dynamometer test cells.

An engine engineer can now spin up Spark-enabled parallel compute environments on-demand automatically, to analyze data coming in from around the world. This is an extraordinary capability that allows all domain specialized engineers, not formally trained as data scientists, to apply their understanding of engineering problems successfully and easily. The heavy-lifting needed to enable large data-processing on-demand at the cloud level is drastically simplified. Overall, this may help establish a more well-rounded data science community of engineers. This talk will discuss the challenges and solutions in working with engineering data and applying physics and statistics approaches to tackling our analysis needs.