I am a currently a Member of Technical Staff at MIT Lincoln Laboratory. Prior to that, I worked in AI/ML modeling at JPMorgan Chase. I received my PhD from the Department of Electrical and Computer Engineering at the University of Michigan in 2022, where I was advised by Professor Laura Balzano.

My research focuses on unsupervised machine learning, statistical signal processing, and optimization. I design and analyze new algorithms for problems with big messy data, developing methods with principled modeling techniques and theoretical guarantees. I have applied my work across a diverse range of real applications that spans signal processing, computer vision, medical imaging, banking, environmental sensing, and more. My favorite problems include

• Nonconvex matrix and tensor factorization
• Optimization on Riemannian manifolds
• Online algorithms and low-dimensional models
• Heteroscedastic noise models
• Corrupted and noisy data with missing entries
• Semidefinite programming

Publications

Kyle Gilman, Sam Burer, & Laura Balzano. A Semidefinite Relaxation for Sums of Heterogeneous Quadratics on the Stiefel Manifold, 2022. arXiv preprint. Paper

Kyle Gilman, Davoud Ataee Tarzanagh, & Laura Balzano. Grassmannian Optimization for Online Tensor Completion and Tracking with the t-SVD, 2022. IEEE Transactions On Signal Processing. 70 pp. 2152-2167. Paper

David Hong, Kyle Gilman, Laura Balzano, & Jeffrey A. Fessler. HePPCAT: Probabilistic PCA for Data with Heteroscedastic Noise, 2021. IEEE Transactions on Signal Processing, Vol. 69, 2021. Paper

Kyle Gilman and Laura Balzano. Online Tensor Completion and Free Submodule Tracking with the t-SVD, 2020. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Paper

Kyle Gilman and Laura Balzano. Panoramic Video Separation with Online Grassmannian Robust Subspace Estimation, 2019. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). Paper