Tony Tohme is a PhD candidate whose research interests focus on reliable and interpretable machine learning, with a particular emphasis on white-box modeling and symbolic regression. While supported by his first MathWorks Fellowship, Tony conducted promising work in probabilistic machine learning to improve predictive uncertainty estimation in neural networks for supervised learning tasks. His second MathWorks Fellowship will enable him to pursue several innovative projects in machine learning with the goal of developing fast, accurate, and interpretable white-box modeling techniques. These include exploring the possibility of incorporating symbolic regression in density estimation techniques, invertible maps, and variance reduction methods. MATLAB is a vital tool in Tony’s research, which has the potential to contribute to more reliable and interpretable algorithms and significantly advance state-of-the-art machine learning technology.