A machine learning model to esimate crop areas. This involved cleaning and processing large volumes of satellite imagery.

A deep learning based cloud filter to improve the quality of data going into our models.

A model to estimate corn and soy yields based on real time satellite imagery. The model was trained on millions of acres of historical yield files and 8 growing seasons of satellite imagery.

App to automatically generate variable rate seeding and nitrogen prescriptions based on soil maps, climate data, satellite imagery and historical yield files. Users can customize rates and download in the format appropriate for their machinery.

A Data Scientist and Software Engineer. Michael loves all aspects of data rich software development. He enjoys being involved with the entire product, building and deploying models, services and web apps.

Experience

• Lead Data Scientist at Farmlogs (2015 -)
• Analyst at Kinesis (2015)
• Technology consultant at Threadharvest (2014)
• Programmer at BAE Systems Applied Intelligence, formerly Stratsec (2012)
• Intern analyst at Quantium (Summer 2010- 11)

Education

• PhD in Machine Learning and Groundwater Modelling at the Australian National University (2014 - present)
• BSc in Maths and Computer Science at the Australian National University, High Distinction Average, First Class Honours (2012)
• Higher School Certificate, ATAR of 99.75 (2008)