Statistical inference has long provided the foundation for scientific reconstruction, enabling principled estimation and uncertainty propagation across a wide range of applications. With the rapid adoption of artificial intelligence in scientific computing, an important question arises: how can modern AI be integrated within statistically consistent inference rather than replacing it?

This seminar introduces the motivation and design principles behind KalmanAI, an ongoing effort toward a modular Engine-Model-Filter architecture for sequential statistical inference. After revisiting the Bayesian foundations underlying Kalman filtering, the discussion will illustrate how these principles naturally extend across diverse estimation problems, including spacecraft navigation, launch and flyby trajectory estimation, interacting multiple-model (IMM) filtering, and scientific reconstruction. Finally, the seminar will explore how probabilistic state estimation and modern AI can be combined to develop physics-driven learning algorithms that preserve uncertainty propagation, statistical consistency, and physical constraints.