Webinar: Real use cases for AI and modelling in mining
Discover how AI resulted in safer operations, increased efficiency, less downtime, and increased discovery potential for our customers.
Date: Wednesday 22 July 2020
Time: 2.00 pm – 3.00 pm AEST
Presenter: Emmanuel Blanchard, Senior Application Engineer, MathWorks
AusIMM Member – Free
Non Member – $20.00
Please note: Registrants who cannot join the webinar at the scheduled time will receive a link to the recording to watch at a later time convenient to them.
The digitisation of mining is evolving at a faster pace than ever before. From geology to processing and operations, the influx of data combined with advancement in software technologies allows companies to be on the cutting edge of innovation. Learn how to embrace this innovation from MathWorks – labelled a Leader in the 2020 Gartner Magic Quadrant for Data Science and Machine Learning Platforms. Discover how AI resulted in safer operations, increased efficiency, less downtime, and increased discovery potential for our customers, as well as the lessons they learned in order to succeed.
- How companies such as Caterpillar have integrated AI into their projects
- How to implement AI successfully using your domain experts
- Current and Future Trends of AI
Emmanuel Blanchard is an application engineer at MathWorks who works with mining customers on projects related to data analytics and machine learning. He joined in 2014 as a training engineer teaching several MATLAB, Simulink and Simscape courses, including specialised topics such as machine learning, statistics, optimisation, image processing and parallel computing. Prior to joining MathWorks, he was a Lecturer in Mechatronic Engineering at the University of Wollongong. He holds a PhD in Mechanical Engineering from Virginia Tech. He also worked as a Systems / Controls Engineer at Cummins Engine Company and as a research assistant in several research institutions in California and Virginia.
For more information on MathWorks’ MATLAB and Simulink solutions for mining click here.