<p>I created <a href="https://github.com/cleanlab/cleanlab" rel="nofollow noreferrer">cleanlab open-source</a> in grad school at MIT used by thousands of data scientists across hundreds of Fortune-500 companies to automatically improve any ML model by automatically identifying most kinds of issues in any ML dataset (data errors, label errors, outliers, overlapping classes, regression, multi-label, multi-class, named-entity recognition, etc). cleanlab works with any ML model and every major ML and deep learning library (tensorflow, pytorch, xgboost, fasttext, huggingface, etc).</p>
<p>Specialize in learning with noisy labels, <a href="https://l7.curtisnorthcutt.com/confident-learning" rel="nofollow noreferrer">confident learning</a>, and weakly supervised learning.</p>
<p>Website: <a href="http://curtisnorthcutt.com/" rel="nofollow noreferrer">https://www.curtisnorthcutt.com/</a></p>
<p>Recent work: <a href="https://l7.curtisnorthcutt.com/confident-learning" rel="nofollow noreferrer">Confident Learning: Finding Label Errors in Datasets and Learning with Noisy Labels</a></p>

cgnorthcutt