Michele Tufano

Michele Tufano

Machine Vision Robot

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Michele is a Ph.D. Candidate in Human Nutrition and Health at Wageningen University and Research and a Research Affiliate at the Senseable City Lab, Massachusetts Institute of Technology. With expertise in computer vision and deep learning, Michele focuses on video classification to predict human eating behavior and count eating and drinking events. In free time, Michele enjoys playing basketball, hiking, and cooking Italian food.

Projects Michele Tufano is working on

Machine Vision and Eating Behaviour

Machine Vision and Eating Behaviour

This is an AI-powered project that includes Bitecounter and the Eatpol Toolkit for automated analysis of eating behavior. Bitecounter detects bites using facial landmarks from video, while the Eatpol Toolkit tracks eating speed, counts bites, chews, and sips. It can even identify the fastest researcher to eat a cookie!

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Foodpedia (Collaboration with MIT Senseable City Lab)

Foodpedia (Collaboration with MIT Senseable City Lab)

How does the urban food environment shape health outcomes? This study, under review in Nature Foods, analyzes the nutritional content of foods available in neighborhoods across Boston, London, and Dubai. Using data mining, SQL, NLP, BERT, LLM, and RAG, Michele Tufano quantifies food availability and its potential health impacts.

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