Mikhail Golovnya is a Senior Advisory Data Scientist at Minitab. Mikhail has been prototyping new machine learning algorithms and modeling automation for the past twenty years. He has been a major contributor to Salford Systems/Minitab’s on-going search for technological improvements among the most important algorithms in Machine Learning: CART® Decision Trees, MARS® Non-linear Regression, TreeNet® gradient boosting, and Random Forests®. Mikhail has presented at multiple conferences and seminars. He has also taught about the mathematical foundations and applications of major predictive learning algorithms, both classical and modern. He has two master’s degrees, one in rocket science from Kharkov State Polytechnic University (Ukraine) and another in statistical computing from the University of Central Florida (Orlando). Mikhail is leading the next generation of Minitab’s machine learning product development.