Daniel Hsu named one of “AI’s 10 to watch”

Fitting models using unlabeled data (vs labeled data) is limited by computational barriers. Daniel Hsu works to overcome such barriers by developing new, computationally efficient and statistically consistent algorithms for fitting latent variable models. He also develops new active learning frameworks that substantially reduce required label ing effort while simultaneously managing sampling bias for statistical consistencies.