Michele Tufano is Co-Founder of Eatpol and a Ph.D. researcher in AI and Computer Vision at Wageningen University & Research. His research focuses on automating eating behavior analysis using advanced deep learning models that detect bites, chews, sips, and behavioral signals from video data. He is also a Research Affiliate at MIT’s Senseable City Lab, where his work has explored how food environments influence health outcomes.

At Eatpol, Michele translates cutting-edge AI into practical tools for food and CPG companies. Eatpol enables brands to test products in real homes using video and audio analysis, capturing how consumers truly prepare, cook, eat, and store products. By measuring real behavior rather than relying only on surveys, Eatpol helps reduce launch risk, uncover hidden friction points, and support the development of products consumers genuinely choose again.