Nicholas Larus-Stone

Nicholas is a software engineer at Octant. He works on scaling Octant’s platform technologies and improving software best practices.

Nicholas’ undergraduate thesis under Margo Seltzer and Cynthia Rudin focused on scaling interpretable machine learning methods. His Master’s work under Pietro Lio and Jim Haseloff involved building metabolic models of cell-free systems. More recently, Nicholas spent a few years working at BenevolentAI as a software engineer focused on target identification and machine learning infrastructure.

Nicholas enjoys running, hiking, playing squash, reading, cooking, and, most importantly, eating.

Papers and Links

Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo Seltzer, and Cynthia Rudin. Learning Certifiably Optimal Rule Lists for Categorical Data. JMLR, 2018.

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