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Researcher profile

Sangjoon Park

Sangjoon Park contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer VisionMachine LearningArtificial Intelligenceeess.IVphysics.soc-ph

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Published work

4 published item(s)

preprint2026arXiv

AI-Driven Stabilization in Power Grids through Controlling Line Admittances

The global transition from traditional power plants to renewable energy sources introduces new challenges in grid stability, primarily because inverter-based technologies provide insufficient inertia. To address this, we introduce an artificial intelligence algorithm that autonomously stabilizes power grids by adaptively tuning admittance regulators in response to disturbances. This Adaptive Admittance Controller (AAC) algorithm not only stabilizes the system in real time but also identifies the best regulator locations, thereby unifying grid planning and real time control within a single framework. When tested on a real UK power grid, the AAC markedly reduces frequency deviations and rapidly restores nominal operation. In addition, the algorithm isolates a small number of key regulators and intervenes only on these, lowering both system complexity and cost. The AAC algorithm further reduces the nonlinearity effect, quickly stabilizing the frequency and power flow. This intelligent control scheme enables power grids to reliably return to stable operating conditions under a broad spectrum of fault scenarios. The proposed framework can also be used to mitigate cascading failures by adaptively controlling critical links in a variety of networked infrastructures, such as cascades of traffic congestion on road networks or fuse failures in energy-saving systems.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

DuetFair: Coupling Inter- and Intra-Subgroup Robustness for Fair Medical Image Segmentation

Medical image segmentation models can perform unevenly across subgroups. Most existing fairness methods focus on improving average subgroup performance, implicitly treating each subgroup as internally homogeneous. However, this can hide difficult cases within a subgroup, where high-loss samples are obscured by the subgroup mean. We call this problem \textbf{intra-group hidden failure}. To solve this, we propose \textbf{DuetFair} mechanism, a dual-axis fairness framework that jointly considers inter-subgroup adaptation and intra-subgroup robustness. Based on DuetFair, we introduce \textbf{FairDRO}, which combines distribution-aware mixture-of-experts (dMoE) with subgroup-conditioned distributionally robust optimization (DRO) loss aggregation. This design allows the model to adapt across subgroups while also reducing hidden failures within each subgroup. We evaluate FairDRO on three medical image segmentation benchmarks with varying degrees of within-group heterogeneity. FairDRO achieves the best equity-scaled performance on Harvard-FairSeg and improves worst-case subgroup performance on HAM10000 under both age- and race-based grouping schemes. On the 3D radiotherapy target cohort, FairDRO further improves worst-group Dice by 3.5 points ($\uparrow 6.0\%$) under the tumor-stage grouping and by 4.1 points ($\uparrow 7.4\%$) under the institution grouping over the strongest baseline.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Multi-Task Distributed Learning using Vision Transformer with Random Patch Permutation

The widespread application of artificial intelligence in health research is currently hampered by limitations in data availability. Distributed learning methods such as federated learning (FL) and shared learning (SL) are introduced to solve this problem as well as data management and ownership issues with their different strengths and weaknesses. The recent proposal of federated split task-agnostic (FeSTA) learning tries to reconcile the distinct merits of FL and SL by enabling the multi-task collaboration between participants through Vision Transformer (ViT) architecture, but they suffer from higher communication overhead. To address this, here we present a multi-task distributed learning using ViT with random patch permutation. Instead of using a CNN based head as in FeSTA, p-FeSTA adopts a randomly permuting simple patch embedder, improving the multi-task learning performance without sacrificing privacy. Experimental results confirm that the proposed method significantly enhances the benefit of multi-task collaboration, communication efficiency, and privacy preservation, shedding light on practical multi-task distributed learning in the field of medical imaging.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Deep Learning COVID-19 Features on CXR using Limited Training Data Sets

Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of the CXR data set for deep neural network training is difficult. To address this problem, here we propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. The proposed method is inspired by our statistical analysis of the potential imaging biomarkers of the CXR radiographs. Experimental results show that our method achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage.

0 citations0 reviewsNo code linkedOpen access