Head of Data Science at Uptake Technologies
Adam McElhinney is currently the Head of Data Science at Uptake Technologies, where he leads a team of 75 Data Scientists building cutting-edge industrial data analytics tools. Additionally, Adam is an Adjunct Professor in the Computer Science and Mathematics departments at Illinois Institute of Technology. Formerly, he was the Head of Business Analytics and the Head of Marketing Analytics at Enova Financial where he helped grow the company from a small startup to a publicly traded online lending leader that currently employs more than 1,000 associates in the Chicagoland area. He has previously worked as a management consultant as well as an analyst designing simulations for the Department of Defense. Adam is serving his seventh year on the Board of the Chicago Chapter of the American Statistical Association, currently in the role of President. Adam holds a Masters in Statistics from University of Illinois-Chicago and has undergraduate degrees in Mathematics, Economics and Political Science from Indiana University-Bloomington. Additionally, Adam has filed 18 patents for his research in machine learning, internet of things (IOT), software engineering and big data technology. Adam was recognized by the Illinois Technology Association as the 2018 Technologist of the Year.
Track: Data Mining
Tuesday, April 16, 4:40–5:30pm
Applications of Machine Learning to IOT
The proliferation of sensor technologies has resulted in more connected machines than ever before. This change is resulting in huge quantities of sensor data becoming available for analysis. Machine learning algorithms have resulted in a mixed track record of success with these data sources. This talk will give an overview of the state of machine learning as applied to IoT and industrial equipment. It will discuss some of the challenges with current approaches, exciting theoretical advancements and some “lessons learned” from the field.
- What do we mean by IoT?
- What is failure prediction and prognostics?
- What is the value of IoT?Differences between physics based approaches to IoT and data-driven approaches to IoT
- What are the challenges from applying data-driven approaches to IoT?
- How can recent advances in machine learning help with the unique challenges of IoT?
- Real-case study that illustrates the application of deep-learning, gradient boosting, transfer learning and other machine learning techniques for IoT applications
- What are the opportunities for future enhancements and exciting research in this area?