Sok-song Jeong
Korea University · Computer Science
About the Lab
Professor Sok-song Jeong's research lab specializes in hybrid estimation and machine learning for dynamic systems, with a focus on developing adaptive filtering frameworks that integrate neural networks with classical estimation theory. The lab explores applications in real-time state estimation for complex systems, including object tracking and wireless communication channel modeling, particularly in reconfigurable intelligent surface (RIS)-assisted networks. By leveraging differentiable dynamics and automatic differentiation, the lab advances intelligent, online learning-based estimation methods that outperform traditional fixed-model approaches. The work bridges signal processing, machine learning, and physical system modeling to enable robust and accurate tracking under uncertainty.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
2Abstract Background: Invasive lobular carcinoma (ILC) is typically characterized by lobular morphology, CDH1 inactivation and reduced E-cadherin expression. However, discordance between morphologic and immunohistochemical (IHC) findings often complicates diagnosis. To address this issue, we performed whole transcriptome sequencing (WTS) on selected breast cancer cases exhibiting variable E-cadherin IHC patterns and morphology, including cases with and without CDH1 mutations identified by prior t
We propose the Augmented State Neural-Transition EKF (ANT-EKF), a hybrid filtering framework that embeds a learnable neural transition model inside the extended Kalman filter. By augmenting the state with the neural network’s parameters, ANT-EKF jointly tracks the physical state and adapts the dynamics model online using Jacobians obtained via autograd. We instantiate the method for (i) object trajectory tracking and (ii) RIS-assisted mobile channel tracking, where the measurement remains linear
Research Areas
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