Skip to main content

Knowledge Discovery and Data Mining focuses on the process of extracting meaningful patterns from biomedical data (knowledge discovery), using automated computational and statistical tools and techniques on large datasets (data mining). Its underlying goal is to help humans make high-level sense of large volumes of low-level data, and share that knowledge with colleagues in related fields. It can involve methods for data preparation, cleaning, and selection, use of appropriate prior knowledge, development and application of data mining algorithms, and proper results analysis. It engages methods from such diverse areas as machine learning, pattern recognition, database science, statistics and analytics, artificial intelligence, knowledge acquisition for expert systems, data modeling and visualization, and high performance computing.


Profile image for Lixia Yao, PhD

Lixia Yao, PhD

Director, Real-world Data Analytics & Innovation
Profile image for Ying Li

Ying Li

Vice Chair
Regeneron Pharmaceuticals
Profile image for Fei Wang, PhD

Fei Wang, PhD

Past Chair
Weill Cornell Medicine
Profile image for Shamsi R. Berry, PhD

Shamsi R. Berry, PhD

Western Michigan University Homer Stryker MD School of Medicine
Profile image for Zhe He, PhD

Zhe He, PhD

Associate Professor (tenured)
Florida State University
Profile image for Eileen Koski, MPhil, FAMIA

Eileen Koski, MPhil, FAMIA

Informatics Partnership Council Chair
Program Director, Health Data & Insights
IBM Research