activity
20202026
most citedExploring the SARS-CoV-2 virus-host-drug interactome for drug repurposing

173 citations · 176 across the 6 of their papers we have counts for

collaborators

8 papers

cs.CV2026

Paired Uterine Whole-Slide Images and Pathology Reports for Multimodal Computational Pathology

Han Li, Jingsong Liu, Ayako Ura +14

Uterine diseases represent an important category of gynecologic pathology and require accurate histopathological assessment for diagnosis and treatment planning. Whole-slide images…

cs.LG2026

The Mean is the Mirage: Entropy-Adaptive Model Merging under Heterogeneous Domain Shifts in Medical Imaging

Sameer Ambekar, Reza Nasirigerdeh, Peter J. Schuffler +3

Model merging under unseen test-time distribution shifts often renders naive strategies, such as mean averaging unreliable. This challenge is especially acute in medical imaging, w…

cs.LG2024

Improved Localized Machine Unlearning Through the Lens of Memorization

Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Georgios Kaissis +3

Machine unlearning refers to removing the influence of a specified subset of training data from a machine learning model, efficiently, after it has already been trained. This is im…

cs.LG2021

The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond

Julian Matschinske, Julian Späth, Reza Nasirigerdeh +29

Machine Learning (ML) and Artificial Intelligence (AI) have shown promising results in many areas and are driven by the increasing amount of available data. However, this data is o…

cs.LG2021

HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning

Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Julian Matschinske +3

Federated learning (FL) enables multiple clients to jointly train a global model under the coordination of a central server. Although FL is a privacy-aware paradigm, where raw data…

q-bio.QM20202 cited

Flimma: a federated and privacy-preserving tool for differential gene expression analysis

Olga Zolotareva, Reza Nasirigerdeh, Julian Matschinske +9

Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange i…