Christopher Quinn

Christopher Quinn is an Assistant Professor at Iowa State University, specializing in Artificial Intelligence, Machine Learning, and Data Science. He holds a B.S. in Engineering Physics from Cornell University (2008), an M.S. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (2010), and a Ph.D. in Electrical and Computer Engineering from UIUC (2014). His research focuses on optimization algorithms, neural networks, and information theory, with applications in neuroscience and distributed systems. He has contributed to advancements in submodular optimization, bandit algorithms, and federated learning. Quinn’s recent work explores topics such as combinatorial bandits, stochastic processes, and causal inference in neural data analysis.

His publications highlight expertise in machine learning theory, including studies on gradient methods for online optimization, redundancy analysis in code-trained models, and unsupervised change-point detection in time series. He has also investigated neural mechanisms underlying visual experience, such as theta oscillation synchronization in the visual cortex of Fmr1 KO mice. Quinn’s work often bridges theoretical foundations with practical applications, such as federated learning frameworks and robust directed tree approximations of stochastic networks.

Christopher Quinn’s research has been supported by grants including the NSF CIF: Small grant for sequential decision-making under uncertainty (2022). He actively publishes in top-tier venues and collaborates on interdisciplinary projects at the intersection of AI and neuroscience. His address is 113 Atanasoff Hall, Ames, Iowa 50010, and he can be reached at cjquinn@iastate.edu.