Nicolo Fusi
VP & Distinguished Scientist
About
I’m a VP & Distinguished Scientist at Microsoft Research, where I oversee AI research groups across the East Coast and lead a research program using generative AI to accelerate scientific discovery.
My research lives at the intersection of AI and the life sciences, and oscillates between the two: on the AI side, Bayesian optimization that powered Azure’s AutoML and optimal transport methods for comparing datasets; on the biology side, predictive models for CRISPR gene editing and statistical genetics.
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All about automated machine learning with Dr. Nicolo Fusi
Episode 43, September 26, 2018 -You may have heard the phrase, necessity is the mother of invention, but for Dr. Nicolo Fusi, a researcher at the Microsoft Research lab in Cambridge, Massachusetts, the mother of his invention wasn’t so much necessity as it was boredom: the special machine learning boredom of manually fine-tuning models and hyper-parameters that can eat up tons of human and computational resources, but bring no guarantee of a good result. His solution? Automate machine learning with a meta-model that figures out what other models are doing, and then predicts how they’ll work on a given dataset. On today’s podcast, Dr. Fusi gives us an inside look at Automated Machine Learning – Microsoft’s version of the industry’s AutoML technology – and shares the story of how an idea he had while working on a gene editing problem with CRISPR/Cas9 turned into a bit of a machine learning side quest and, ultimately, a surprisingly useful instantiation of Automated Machine Learning – now a feature of Azure Machine Learning – that reduces dependence on intuition and takes some of the tedium out of data science at the same time.