Data-Driven Scientific Discovery Through Interpretable Machine Learning | RENCI Connects

Unsupervised learning is increasingly being used to mine the heterogeneity present in biomedical datasets to make discoveries in critical domains across health and science. However, there is a lack of standardized methodologies to ensure these results are reliable and interpretable. Join Dr. Tarek Zikry as he presents work across several domains including neuroscience and cancer, where we leverage data-driven methodological development in statistics and computational biology for robust scientific discovery.