Home

Faculty News

Dr. Xiaowei Jia (assistant professor) received an Early Career Investigator Grant from NASA's Earth Science Division for his project “Towards Generalizable, Fair, and Knowledge Guided Machine Learning for Monitoring Earth Systems.” 

Dr. Longfei Shangguan (assistant professor) received an NSF CAREER award for his project “Ubiquitous Earable Sensing Using Low-Cost Earphones.”

Dr. Stephen Lee (assistant professor, Department of Computer Science) received a National Science Foundation (NSF) grant for his project, “Data-Driven System-Design for Sustainable Long-Lasting Distributed Infrastructures,” on Oct. 1, 2023.

Student News

Last month, 33 students (18 in person and 15 virtually) from the School of Computing and Information (SCI) attended the Grace Hopper Conference (GHC) in Orlando, FL.

In September, students from the School of Computing and Information, along with their peers from other schools within Pitt, came together to plan the Pitt Challenge 2023.

Two students from the School of Computing and Information (SCI) Department of Computer Science have been recognized for their undergraduate research by the Computing Research Association (CRA)!

Colloquium Talks

In this talk, we will discuss the evolution of this theorem and the surprising developments of the 1980s that led to the concept of the geometric phase. We will use simple models to demonstrate how QAC is applied in applications. Additionally, we will explore how the discovery of geometric phases has led to the idea of quantum holonomic computing.

In this talk, I will present our recent research on creating AI systems that can learn to understand our multimodal world with minimal human supervision. I will focus on systems that can understand images and text, and also touch upon those that utilize video, audio, and lidar. 

To avoid repetitive analysis, environmental impact factors (EIF) of common materials and products are published for use by LCA experts. However, finding appropriate EIFs for even a single product under study can require hundreds of hours of manual work, especially for complex products. We present Flamingo, an algorithm that leverages natural language machine learning (ML) models to automatically identify an appropriate EIF given a text description.