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Mihai Nica is an Assistant Professor in the Department of Mathematics and Statistics at the University of Guelph, where he has been a faculty member since 2020. He obtained his PhD from New York University in 2017. Following his doctoral studies, he held a position as a postdoctoral fellow at the University of Toronto from 2017 to 2019. His research interests center on probability, stochastic processes, and their applications in machine learning. Nica’s work particularly explores the application of probabilistic tools to Deep Neural Networks (DNNs) and investigates the algorithms underlying machine learning technologies. His previous research involves addressing significant questions in the field, including the scaling limits of deep neural networks and the effectiveness of neural networks in overcoming challenges faced by traditional numerical methods. Nica is also affiliated with the CARE-AI Institute at the Vector Institute. He has received recognition for his work, including the 2018 F.V. Atkinson Teaching Award from the University of Toronto.
University of Toronto • Toronto, ON
Conducted research on applications of probabilistic methods in deep learning.
University of Guelph • Guelph, ON
Teaching and research in applied mathematics and statistics.
Department of Clinical Studies. Offers MSc by thesis (2 years) and MSc by coursework (1 year).