activity
20242026
collaborators

8 papers

eess.IV2026

When Repository Labels Are Not Image-Level Truth: A Supervision Auditing Framework for Chest Radiograph AI

Yesika Alexandra Agudelo-Londoño, Jhon Wilmer Pino-Román, Brahian Carrera Rodríguez +9

Public chest X-ray repositories are widely used to train medical AI systems, yet their labels are typically extracted from radiology reports rather than verified directly on images…

cs.AI2026

Automatic Prompt Engineering with No Task Cues and No Tuning

Faisal Chowdhury, Nandana Mihindukulasooriya, Niharika S D'Souza +4

This paper presents a system for automatic prompt engineering that is much simpler in both design and application and yet as effective as the existing approaches. It requires no tu…

cs.AI2025

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study

Nandana Mihindukulasooriya, Niharika S. D'Souza, Faisal Chowdhury +1

A KG represents a network of entities and illustrates relationships between them. KGs are used for various applications, including semantic search and discovery, reasoning, decisio…

q-bio.NC2024

A Joint Network Optimization Framework to Predict Clinical Severity from Resting State Functional MRI Data

Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas Wymbs +2

We propose a novel optimization framework to predict clinical severity from resting state fMRI (rs-fMRI) data. Our model consists of two coupled terms. The first term decomposes th…

cs.LG2024

Deep sr-DDL: Deep Structurally Regularized Dynamic Dictionary Learning to Integrate Multimodal and Dynamic Functional Connectomics data for Multidimensional Clinical Characterizations

Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti +4

We propose a novel integrated framework that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tr…

cs.LG2024

A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism

Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti +4

We propose an integrated deep-generative framework, that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imag…