Daniel Severo

Daniel Severo is a PhD candidate in Electrical and Electronics Engineering at the University of Toronto, with extensive experience in research and development across various sectors, including AI for healthcare, E-commerce, and embedded systems. Previous roles include serving as a Machine Learning Researcher at 3778, a Full-Stack Developer and Head of Data Science at Chaordic, and internships at companies like Google and Facebook AI, focusing on deep learning, data compression, and probabilistic modeling. Notable research contributions encompass the Ziggurat Method for random number generation and work on advanced product recommendation algorithms. Daniel has also collaborated in various educational projects, demonstrating a solid foundation in technical and managerial skills from an early career at organizations such as the Embedded Systems Group and the CERTI Foundation.