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Mihai Nica joined the Department of Mathematics and Statistics at the University of Guelph in 2020. His work focuses on improving the theoretical understanding of artificial intelligence technologies through mathematical analyses. Nica's research primarily investigates Deep Neural Networks (DNNs) and the algorithms that underpin machine learning technologies. His algorithms are modeled after human brain functions and are used to recognize patterns. Additionally, he is exploring mathematical concepts related to probability and stochastic processes, as well as their applications in DNNs. He is a member of the CARE-AI initiative, which aims to bridge the gap between purely mathematical results and real-world applications. Nica has conducted research on scaling limits of DNNs, numerical methods utilizing neural networks, and phase transitions in high-dimensional learning problems. He holds a Bachelor of Mathematics from the University of Waterloo and a PhD from the Courant Institute of Mathematical Sciences at New York University.
Department of Clinical Studies. Offers MSc by thesis (2 years) and MSc by coursework (1 year).