Join the Goergen Institute for Data Science for Moving Beyond Scale-Driven Learning with Hangfeng He, Assistant Professor of Computer Science and Data Science.

Abstract: Machine learning, especially deep learning, has been recognized as a monumentally successful approach to many data-intensive applications across a broad range of domains. Despite the great success achieved, recent progress mainly relies on scaling up existing learning methods with regard to the size of models or training data, consuming enormous time and energy. Therefore, my research focuses on moving beyond scale-driven learning to avoid large-scale training data and overly complicated models. 

My prior work has been driven by two problems: alleviating the supervision bottleneck and interpreting the behaviors of deep neural networks. The former reduces the demand for task-specific data, and the latter helps to design simple and efficient models. In the future, I aspire to expand my research goal from the learning realm to more areas, including comprehending the mechanism of reasoning and analyzing the structure of data.

Bio: Hangfeng is an Assistant Professor in the Department of Computer Science and the Goergen Institute for Data Science at the University of Rochester. Before this, he was a Ph.D. student at the University of Pennsylvania, where he worked with Dan Roth and Weijie Su. He received his bachelor’s degree from Peking University in 2017. His research interests include machine learning and natural language processing, with a focus on incidental supervision for natural language understanding, interpretability of deep neural networks, and reasoning in natural language.

We are providing a Zoom option for this event. Please use the link below to enter the Zoom webinar.

Webinar link: https://rochester.zoom.us/j/93458839067

Webinar ID: 934 5883 9067