College of IST announces Data Sciences Research Seminar Series

Jordan Ford
September 28, 2018

UNIVERSITY PARK, Pa. — With the rapid growth of Big Data, organizations are evolving to understand how they can analyze and transform this information to uncover actionable intelligence. These challenges and opportunities will be the focus of the newly announced Data Sciences Research Seminar Series, which aims to foster a collaborative data sciences research community at Penn State.

The series offers a forum for data sciences faculty, postdoctoral researchers and graduate students to present their research and explore data sciences applications in a variety of areas, including life sciences, biomedical and health sciences, material sciences, environmental sciences, engineering, social and behavioral sciences, and cognitive and brain sciences. 

The seminars will be held on Tuesdays throughout the fall from 12-1 p.m. in room E202 in the Westgate Building on the University Park campus. Each seminar is free and open to the public.

The 2018 series includes:

  • Oct. 2: Sharon Huang, associate professor of information sciences and technology

  • Oct. 9: Naomi Altman, professor of statistics

  • Oct. 16: Luke Huan, director, Big Data Research Lab, Baidu Research

  • Oct. 23: Zihan Zhou, assistant professor of information sciences and technology 

  • Oct. 30: David Reitter, associate professor of information sciences and technology

  • Nov. 6: Sara Rajtmajer, assistant professor of information sciences and technology 

  • Nov. 13: Daniel Susser, assistant professor of information sciences and technology

  • Nov. 27: Shomir Wilson, assistant professor of information sciences and technology 

  • Dec. 4: Yasser El-Manzalawy, assistant research professor of information sciences and technology 

The opening talk in the series, titled “Generative Adversarial Networks for Image Synthesis and Segmentation,” will be delivered by Sharon Huang, associate professor of information sciences and technology, on Oct. 2. In her talk, Huang will discuss frameworks for two Generative Adversarial Networks that can generate high-solution realistic images conditioned on natural language descriptions of a scene, which can be useful in applying supervised learning techniques to biomedical image classification and segmentation.

Additional information can be found on the Data Sciences Research Seminar Series’ website.

Last Updated October 01, 2018