1 citations · 1 across the 5 of their papers we have counts for
6 papers
Maximum Likelihood Particle Tracking in Turbulent Flows via Sparse Optimization
Griffin M Kearney, Kasey M Laurent, Makan Fardad
Lagrangian particle tracking is essential for characterizing turbulent flows, but inferring particle acceleration from inherently noisy position data remains a significant challeng…
Kinematic Tokenization: Optimization-Based Continuous-Time Tokens for Learnable Decision Policies in Noisy Time Series
Griffin Kearney
Transformers are designed for discrete tokens, yet many real-world signals are continuous processes observed through noisy sampling. Discrete tokenizations (raw values, patches, fi…
Adaptive Clutter Suppression via Convex Optimization
Yifan He, Griffin Kearney, Makan Fardad
Passive and bistatic radar systems are often limited by strong clutter and direct-path interference that mask weak moving targets. Conventional cancellation methods such as the ext…
Optimization Based Data Enrichment Using Stochastic Dynamical System Models
Griffin M. Kearney, Makan Fardad
We develop a general framework for state estimation in systems modeled with noise-polluted continuous time dynamics and discrete time noisy measurements. Our approach is based on m…
Maximum Likelihood Filtering for Particle Tracking in Turbulent Flows
Griffin M. Kearney, Kasey M. Laurent, Reece V. Kearney
Lagrangian Particle Tracking (LPT) enables practitioners to study various concepts in turbulence by measuring particle positions in flows of interest. This data is subject to measu…
Sheaf-theoretic framework for optimal network control
Griffin M. Kearney, Kevin F. Palmowski, Michael Robinson
In this paper, we use tools from sheaf theory to model and analyze optimal network control problems and their associated discrete relaxations. We consider a general problem setting…