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researcher

Jason H. Moore

4 papers hereh-index 19 citations5 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author2
  • last author1

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL1
  • cs.CR1
  • cs.CV1
  • cs.MA1
same name
  • Jason H. Moore — 2 papers, h 3
  • Jason H. Moore — 2 papers, h 2
  • Jason H. Moore — 1 paper, h 4
  • Jason H. Moore — 1 paper, h 3
  • Jason H. Moore — 1 paper, h 6

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

collaborators

4 papers

cs.MA2026

Ablation Study of a Fairness Auditing Agentic System for Bias Mitigation in Early-Onset Colorectal Cancer Detection

Amalia Ionescu, Jose Guadalupe Hernandez, Jui-Hsuan Chang +4

Artificial intelligence (AI) is increasingly used in clinical settings, yet limited oversight and domain expertise can allow algorithmic bias and safety risks to persist. This stud…

cs.CL2026

Tabular LLMs for Interpretable Few-Shot Alzheimer's Disease Prediction with Multimodal Biomedical Data

Sophie Kearney, Shu Yang, Zixuan Wen +8

Accurate diagnosis of Alzheimer's disease (AD) requires handling tabular biomarker data, yet such data are often small and incomplete, where deep learning models frequently fail to…

cs.CR2025

SafeGenes: Evaluating the Adversarial Robustness of Genomic Foundation Models

Huixin Zhan, Clovis Barbour, Jason H. Moore

Genomic Foundation Models (GFMs), such as Evolutionary Scale Modeling (ESM), have demonstrated significant success in variant effect prediction. However, their adversarial robustne…

cs.CV2025

Agentic Surgical AI: Surgeon Style Fingerprinting and Privacy Risk Quantification via Discrete Diffusion in a Vision-Language-Action Framework

Huixin Zhan, Jason H. Moore

Surgeons exhibit distinct operating styles shaped by training, experience, and motor behavior-yet most surgical AI systems overlook this personalization signal. We propose a novel…

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