Alexander Lapanowski received his Ph.D. in statistics from Texas A&M University, where his research focused on machine learning methods for non-linear sparse feature selection and data compression within the discriminant analysis framework. He has presented his work at leading conferences, such as the Joint Statistical Meeting (JSM) and Artificial Intelligence and Statistics (AISTAT). He joined SABIC full-time in August 2020, and has brought his machine learning expertise to a number of projects. He is the author of four research papers focusing on applied mathematics and machine learning. Previously, he worked as a research intern at SABIC, where he has used machine learning methods to model oxidative coupling and assist scientists in finding optimal catalysts and experimentation conditions.