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Faculty News
Dr. Xulong Tang, an associate professor in the Department of Computer Science, is participating in a five-year $5 million NSF ExpandQISE Track 2 Grant. The project, titled “ExpandQISE: URI-PQI Collaboration - Application of Quantum Fundamentals to Advance Research and Workforce Development,” is led by the University of Rhode Island in collaboration with the Pittsburgh Quantum Institute, Carnegie Mellon University, and the University of Pittsburgh.
Dr. Ryan Shi, assistant professor in the Department of Computer Science and Intelligent Systems Program, recently received a Google Academic Research Award to address these issues and better reach farmers, particularly smallholder farmers in the global south.
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.”
Student News
Senior computer science Student Griffin J. Hurt has been named a 2024 Graduate Research Fellowship Program (GRFP) scholar.
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)!
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.
Colloquium Talks
In this talk, I argue that NLP is experiencing an identity crisis: it is increasingly subsumed under machine learning (ML), optimizing for predictive performance while neglecting its core scientific objective—the study of language as a system of meaning embedded in dynamic social, cultural, and epistemic contexts. We need to move beyond benchmark Illusions and establish sciences of annotation, evaluation, and veracity that embrace pluralism coupled with responsible thinking.
In this talk, I’ll characterize the complex relationship between AI and the environment, as exemplified by LLMs. I’ll describe what we currently know about the positive and negative environmental impacts of LLMs, and identify some of the key challenges to understanding and shaping those impacts moving forward.
This talk will discuss both the method and data foundations to scale AI models for large-scale geospatial applications and science questions, covering new frameworks for spatially-explicit learning, knowledge-guided learning, task-aligned pretraining, as well as new benchmark datasets and data generation methods.