Math, Machines, and Metacognition

The Stanford Intelligent Systems Lab (SISL) conducts research on decisions under uncertainty and optimization problems with applications to autonomous vehicles. These two math projects will apply calculus principles of optimization and statistical modeling with Bayesian recursion and uncertainty to high school curriculum. Both projects will give students a better sense the complex, multifaceted problems engineers face and resolve in STEM careers using appropriate STEM hardware. Students will also gain exposure to programming languages as the robots used within projects are given commands through the C/C++ language. Finally, students will be given metacognitive opportunities throughout the projects where they will connect their own habits and mindsets to those needed in order to succeed in STEM fields. These projects are suitable for students in calculus who have learned applications of differentiation and for statistics students who are familiar with conditional probability, Bayes’ rule, and probability distributions. Programming knowledge is not required but will be taught and presented as part of the design process. Students will be able to practice mathematical modeling, precision, and justifying explanations within these projects.