Due to the high speed of atmospheric entry flight, the main purpose is to control the dissipation of energy and satisfy various requirements and constraints. Trajectory optimization and control for atmospheric entry are difficult to solve due to the highly nonlinear dynamics, aerodynamic forces, and various path constraints. In our lab, we are developing convex optimization-based methods for potential real-time entry trajectory generation and autonomous entry guidance. The proposed methods are fast, reliable, readily implementable, and able to enforce common constraints while ensuring accuracy for various types of hypersonic missions. Additionally, we are also exploring machine learning methods to address complex entry dynamics and trajectory control problems.