Inverse problems arise when one seeks to recover unknown parameters or functions from indirect, noisy observations via a forward model. The Bayesian framework casts this recovery as the updating of a ...

Paper: "Robust Nonparametric Bias-Corrected Inference in the Regression Discontinuity Design", (joint work with Sebastian Calonico and Rocio Titiunik).

Expertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. We are still only at the beginning of this AI rollout, where the training of models is still ...

“The rapid release cycle in the AI industry has accelerated to the point where barely a day goes past without a new LLM being announced. But the same cannot be said for the underlying data,” notes ...

How to improve the performance of CNN architectures for inference tasks. How to reduce computing, memory, and bandwidth requirements of next-generation inferencing applications. This article presents ...