Nutritional science and data science are two fields on a collision course. The field of nutrition has been plagued by confusing signals to the public, largely due to statistical limitations and poor data quality—both of which can be enhanced by data science and the collective power of new technologies.
Noah Gift and Michelle Davenport explore exciting ideas in nutrition using data science; specifically, they analyze the detrimental relationship between sugar and longevity, obesity, and chronic diseases and make predictions based on our current consumption rates.
Noah Gift is lecturer and consultant at both the UC Davis Graduate School of Management MSBA Program and the Graduate Data Science Program at Northwestern, where he designs and teaches graduate machine learning, AI, data science courses and consults on machine learning and cloud architecture for students and faculty. These responsibilities including leading a multicloud certification initiative for students. As the founder of Pragmatic AI Labs, he also consults with companies on machine learning, cloud architecture, and CTO-level concerns. In the last 10 years, he’s been responsible for shipping many new products at multiple companies that generated millions of dollars of revenue and had global scale. His previous roles have included CTO, general manager, consulting CTO, consulting chief data scientist, and cloud architect, at companies such as ABC, Caltech, Sony Imageworks, Disney Feature Animation, Weta Digital, AT&T, Turner Studios, and Linden Lab. As an SME on machine learning for AWS, he helped created the AWS machine learning certification.
Noah is a Python Software Foundation Fellow, AWS Subject Matter Expert (SME) on machine learning, AWS Certified Solutions Architect and AWS Academy Accredited Instructor, Google Certified Professional Cloud Architect, and Microsoft MTA on Python. He has published close to 100 technical publications, including two books on subjects ranging from cloud machine learning to DevOps, for companies like Forbes, IBM, Red Hat, Microsoft, O’Reilly, and Pearson. He’s also led workshops and talks around the world, for organizations including NASA, PayPal, PyCon, Strata, and Foo Camp. His most recent book is Pragmatic AI: An Introduction to Cloud-Based Machine Learning (Pearson), and his most recent video series is Essential Machine Learning and AI with Python and Jupyter Notebook LiveLessons. He holds an MBA from UC Davis, an MS in computer information systems from Cal State Los Angeles, and a BS in nutritional science from Cal Poly San Luis Obispo.
Michelle Davenport is one of the most sought-after nutrition experts in food and tech. Currently Michelle consults startups and larger companies on data science in nutrition data analysis and precision-based methods to food and health product research and development. She also serves as a clinical and scientific advisor to startups, including Ritual, a direct-to-consumer supplement company for women. Previously, she was the cofounder and president of Raised Real, where she created a venture-funded, tech-driven, subscription food program for children that targets infant nutritional milestones. As the fastest growing kids’ food brand in the US, Raised Real currently delivers to thousands of families nationwide. Before that, she was the director of nutrition for Zesty (acquired by Square), where she developed the food and nutrition API and a proprietary nutrient analysis program. Michelle and her work have been featured in Fast Company, Forbes, Time, and the Wall Street Journal, among others. She lives in Menlo Park, CA, with her husband, Josh, and daughter, Sophie. Michelle holds a PhD in nutrition from New York University; she did her clinical training as a registered dietitian at the University of California, San Francisco.
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