Machine Learning in Drug Discovery with Dataiku

April 12, -

Kelci Miclaus - Global Head of HLS AI Solutions, Dataiku

The opportunities for machine learning applications in the drug discovery and development process continue to grow and are changing the paradigm of successful scientific discovery and research in the pharmaceutical and biotechnology industries.  A key focus for modern data science applications is to speed efficiency and innovation in the notoriously long, expensive process of bringing new therapeutics to market; with the promise of increasing experimental success and reducing years-long timelines to months. In this presentation, I will demonstrate two common innovation approaches:

Dr. Kelci Miclaus is Global Head of Health & Life Sciences AI Solutions at Dataiku. She joined Dataiku from Veeva Systems as Senior Director of Veeva Stats, leading product and strategy for statistical computing solutions. Previously, she spent 15 years developing statistical/ML algorithms and led R&D product teams for the JMP Life Sciences division at SAS Institute. She holds a Ph.D. in Statistics with biomedical and genomic research focus from North Carolina State University. Kelci is a subject matter expert around the role of software, data platforms, and analytics/visualization/ML/AI in advancing and operationalizing discoveries in health and life sciences.

Photo of Dr. Kelci Miclaus


Megan Deyncourt