Data Science Talk Series
We actively invite excellent scholars in Data Science and related disciplines to share their research with our faculty and students. So far we have connected to the University of Pittsburgh, the University of Oregon, the University of Michigan, and so on. The topics include AI Sensing, LLMs, Mobile Acoustic, Data Quality-aware Knowledge Graphs, etc.
GitHub Repo
This Github Page provides useful resources on data quality (DQ) for machine learning (ML) and artificial intelligence (AI), including research papers, open-source DQ tools, and other explorations.
MCN is a natural language processing (NLP) task that seeks to map
informal medical terms or phrases. This page provides zero-shot and few-shot data augmentation methods for MCN tasks, evaluation of the original data and augmented data, etc.
Workshops
This workshop aims to engage related communities in open problems in the extraction and evaluation of knowledge entities from scientific documents and AI + Informetrics.
This workshop aims to gather researchers and practical users to initiate a collaborative platform for exchanging ideas, sharing pilot studies, and scoping future directions on innovation measurement for scientific communication.
This workshop addresses the challenges faced by archivists, historians, and researchers in managing and utilizing large-scale digital archives.
Data quality could significantly impact the performance of the intelligent system. This special track will explore methodologies, tools and frameworks that have been or need to be developed to evaluate and enhance data quality.
This workshop aims to unite leaders, practitioners, and researchers to explore and discuss novel solutions, the latest techniques, best practices, and future directions for developing high-performance and trustworthy AI systems for healthcare from a data quality perspective.
Tutorials
Academic Table and Figure Understanding for Digital Library @ JCDL 2024
Data Quality Dimensions and Tools for Machine Learning @ AITest 2024