Machine Learning

Machine learning aims to develop systems that are capable of learning from past experience or adapting to changes in the environment. We investigate a wide spectrum of machine learning topics that include time-series modeling, representation learning, metric learning, domain adaptation, active and  transfer learning, self-supervised learning, neuro-symbolic reasoning, and their applications in biology, medicine, education, physics.

People

Assistant Professor
(412) 624-8490
Professor and Director, MOMACS
(412) 382-3498
Professor
(412) 624-8845
Professor
(412) 624-7953
Assistant Professor
(412) 624-8490
Assistant Professor
(412) 624-8490
Assistant Professor
(412) 624-8852