• Selecting machine learning models for drug target identification
• Deep dive into tensor factorization and comparison with other models
• How AI and analytics tools work hand-in-hand with researchers to make informed decisions about novel targets to test in the lab
Alix has significant experience using data science and machine learning to advance biomedical discoveries. She holds a PhD in Molecular and Cellular Biology from Harvard University. Previously at IBM Watson Health, Alix led computational research projects in target identification and drug repurposing, most notably for Parkinson’s disease and amyotrophic lateral sclerosis, in collaboration with academic and pharma partners. At BenevolentAI, Alix connects AI and Drug Discovery groups to continuously improve the hypothesis generation pipeline.