most citedAn MRI Atlas of the Human Fetal Brain: Reference and Segmentation Tools for Fetal Brain MRI Analysis

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2026

GoT-CD: Graph-of-Thoughts Causal Discovery and the Fragility of Post-hoc Path-Specific Fairness Audits

Nitish Nagesh, Elahe Khatibi, Thomas Dean Hughes +3

Causal discovery recovers directed structure from observational data and is increasingly used in clinical settings to support mechanism reasoning and fairness audits of predictive…

cs.LG2026

FairDiffuseVQVAE: Sampling-Time Fairness in Tabular Diffusion via Conditional Refinement of Vector-Quantized Latents

Nitish Nagesh, Mahdi Bagheri, Amir M. Rahmani

Synthetic tabular data is increasingly used in privacy-preserving data sharing, data augmentation, and to mitigate downstream classifier bias. State-of-the-art tabular diffusion mo…

cs.LG2026

Memisis: Orchestrating and Evaluating Synthetic Data for Tabular Health Datasets

Nitish Nagesh, Pengbao Zhou, Atchuth Naveen Chilaparasetti +8

Synthetic data is widely used in healthcare to create datasets that preserve statistical properties of real data without exposing sensitive patient information. Generating and eval…

q-bio.QM20261 cited

An MRI Atlas of the Human Fetal Brain: Reference and Segmentation Tools for Fetal Brain MRI Analysis

Mahdi Bagheri, Clemente Velasco-Annis, Jian Wang +10

Characterizing in-utero brain development is essential for understanding typical and atypical neurodevelopment. Building on prior spatiotemporal fetal brain MRI atlases, we present…

cs.LG2026

Evaluating Causal Discovery Algorithms for Path-Specific Fairness and Utility in Healthcare

Nitish Nagesh, Elahe Khatibi, Thomas Hughes +3

Causal discovery in health data faces evaluation challenges when ground truth is unknown. We address this by collaborating with experts to construct proxy ground-truth graphs, esta…

cs.LG2026

FairTabGen: High-Fidelity and Fair Synthetic Health Data Generation from Limited Samples

Nitish Nagesh, Salar Shakibhamedan, Mahdi Bagheri +4

Synthetic healthcare data generation offers a promising solution to research limitations in clinical settings caused by privacy and regulatory constraints. However, current synthet…