Sophie Ostmeier

I am a PhD student in the SPY Lab, supported by the ETH AI Center. I completed a master's degree in CS at Stanford University, where I researched at the AIMI Center. My research was generously supported by the German Research Foundation (DFG) (2023-2025). I completed an M.D. and Dr.med. (Prof. Daniela Pfeiffer) at the Technical University of Munich.

Outside the lab you will find me running, cycling, bikepacking, swimming, playing pickleball, or hiking. I follow a wide range of sports, especially tennis, triathlon, and track and field, but I can appreciate almost any activity with movement.

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Research

My research focuses on how machine-learning models learn, how they make predictions, and how we measure that, with a current emphasis on safety and security aspects. I am also curious about a path toward reliable intelligent decision-making systems and wonder how they would practice medicine.

Some recent papers (Google Scholar)

We investigated if a method can estimate answer correctness with access only to the attention-based model, the prompt, and its answer. Our method, HeadEntropy, uses a simple statistic that measures how sensitive each attention pattern would be to gradient updates. With no training, HeadEntropy reaches 0.74 AUROC over 5 models and 5 benchmarks, beats all 6 training-free baselines on average, and matches a trained hidden-state probe out-of-domain.

We investigated how to increase a transformer's capacity to learn positional information for n-dimensional inputs. We generalized rotary position encodings (RoPE) from fixed 2D rotation blocks to learned, high-dimensional rotation matrices by using their Lie group structure, and we tested this approach on 2D and 3D vision tasks.

We present GREEN, an open-source metric that employs language models to spot and explain clinically significant errors in radiology reports, providing interpretable feedback, and commercial-grade performance.

We investigate evaluation metrics for medical image segmentation that account for uncertainty, small structures, and empty reference annotations.

Miscellaneous

Website Template source code.