PhD Speaking Qualifier

Abstract: Collision detection between objects is critical for simulation, control, and learning for robotic systems. However, existing collision detection routines are inherently non-differentiable, limiting their applications in gradient-based optimization tools. In this talk, I present DCOL: a fast and fully differentiable collision-detection framework that reasons about collisions between a set of composable and highly expressive [...]

Abstract: A standard critique of machine learning models (especially neural networks) is that they pick up on spurious correlations rather than causal relationships and are therefore brittle in the face of distribution shift. Solving this problem in full generality is impossible (i.e. there might be no good way to distinguish between the two). However, if [...]