Silas Ruhrberg Estevez — accepted AI-conference papers

2026

AgentScore: Autoformulation of Deployable Clinical Scoring Systems

Silas Ruhrberg Estevez, Chris Chiu, Mihaela van der Schaar

ICML 2026poster

Modern clinical practice relies on evidence-based guidelines implemented as compact scoring systems composed of a small number of interpretable decision rules. While machine-learning models achieve strong performance, many fail to translate into routine clinical use due to misalignment with workflow…

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2026

CellBRIDGE: Learning Cellular Trajectories via Interaction-Aware Alignment

Silas Ruhrberg Estevez, Nicolas Huynh, Tennison Liu, Roderik Kortlever, Gerard Evan, David Bentley +1

ICML 2026poster

Inferring dynamics from population snapshots is a fundamental challenge in machine learning and biology. In scRNA-sequencing (scRNA-seq), destructive measurements preclude direct tracking of individual cells across time, making trajectory inference underdetermined. Optimal Transport (OT) provides a …

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