Arvind Hosagrahara

Chief Solutions Architect, MathWorks, Inc.

Arvind Hosagrahara leads a team that helps organizations deploy MATLAB algorithms in critical engineering applications, with a focus on integrating MATLAB into the enterprise IT/OT systems. Arvind has extensive hands-on experience developing MATLAB and Simulink applications and integrating them with external technologies. He has helped design the software and workflow for a variety of production applications focusing on robustness, security, scalability, maintainability, usability, and forward compatibility across automotive, energy and production, finance and other industries.



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.