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20232026
most citedAdvancing Problem-Based Learning in Biomedical Engineering in the Era of Generative AI

3 citations · 3 across the 14 of their papers we have counts for

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6 papers · 1 filter

cs.LG2026

Beyond Random Splits: Evaluating Drug-Target Affinity Models Under Chemically and Biologically Motivated Distribution Shifts Copy

Minjae Chung, Clara Li, Malar Paavai Muthukumaran +8

Drug-target affinity (DTA) prediction is widely used to prioritize candidate compounds before costly experimental screening. DTA models are often compared under a single data split…

cs.LG2026

Tree-of-Evidence: Efficient "System 2" Search for Faithful Multimodal Grounding

Micky C. Nnamdi, Benoit L. Marteau, Yishan Zhong +2

Large Multimodal Models (LMMs) achieve state-of-the-art performance in high-stakes domains like healthcare, yet their reasoning remains opaque. Current interpretability methods, su…

cs.LG2026

KindSleep: Knowledge-Informed Diagnosis of Obstructive Sleep Apnea from Oximetry

Micky C Nnamdi, Wenqi Shi, Cheng Wan +4

Obstructive sleep apnea (OSA) is a sleep disorder that affects nearly one billion people globally and significantly elevates cardiovascular risk. Traditional diagnosis through poly…

cs.LG2025

Developing Fairness-Aware Task Decomposition to Improve Equity in Post-Spinal Fusion Complication Prediction

Yining Yuan, J. Ben Tamo, Wenqi Shi +5

Fairness in clinical prediction models remains a persistent challenge, particularly in high-stakes applications such as spinal fusion surgery for scoliosis, where patient outcomes…

cs.LG2025

Causal Machine Learning for Surgical Interventions

J. Ben Tamo, Nishant S. Chouhan, Micky C. Nnamdi +6

Surgical decision-making is complex and requires understanding causal relationships between patient characteristics, interventions, and outcomes. In high-stakes settings like spina…

cs.LG2025

MENDR: Manifold Explainable Neural Data Representations

Matthew Chen, Micky Nnamdi, Justin Shao +7

Foundation models for electroencephalography (EEG) signals have recently demonstrated success in learning generalized representations of EEGs, outperforming specialized models in v…