Gregory Ongie

Dr. Gregory Ongie is an Assistant Professor in the Department of Mathematical and Statistical Sciences at Marquette University. He holds a Ph.D. in Applied Mathematical and Computational Sciences from the University of Iowa (2016), an M.S. in Mathematics (2011), and a B.S. in Mathematics and Physics from Coe College (2008). His research focuses on machine learning, optimization, and compressed sensing applied to medical imaging, particularly in MRI and CT reconstruction. He has developed algorithms for inverse problems in imaging, including deep equilibrium models and Neumann networks.

Research Interests: Mathematics of deep learning, applied algebraic geometry in data science, machine learning for medical image reconstruction, and optimization in computational imaging. His work bridges theoretical foundations of neural networks with practical applications in medical imaging, including model observer analysis and compressed sensing techniques.

Grants and Awards: NSF CISE Research Initiation Initiative Grant (2022), Way Klinger Early Career Award (2023), D. C. Spriestersbach Dissertation Prize (2018), and Best Student Paper Award at ISBI 2015 for MRI super-resolution work.

Teaching: Courses include Applied Linear Algebra, Differential Equations, and Mathematics of Medical Imaging. He has mentored multiple graduate and undergraduate students in imaging-related projects. Active in service roles, including Marquette's Faculty Hiring Committee and seminar coordination.

Labs & Collaborations: Engaged in collaborative research with institutions like the University of Chicago and University of Michigan, focusing on computational imaging and machine learning. GitHub repositories include tools for matrix completion and MRI reconstruction.