Hi! I'm Daniel

How can we turn AI into applications that create value in the real world?
As a senior researcher in computer vision for medical imaging I focus on building machine learning models designed to hold up outside the lab. My work spans self-supervised learning, generative modeling, domain/test-time adaptation and anomaly detection , with a focus on making models robust enough for deployment on messy, real-world data.

Recent projects include:

• self-supervised models for extracting quantitative markers from imaging data
• generative models for image-to-image prediction and synthesis
• test-time adaptation to handle distribution shift across imaging devices and populations
• motion reconstruction and point tracking in video/temporal sequences

I’m always happy to talk about computer vision, machine learning, and robust AI.

Publications

Hierarchical Adaptive networks with Task vectors for Test-Time Adaptation

Sameer Ambekar, Marta Hasny, Laura Alexandra Daza, Daniel M. Lang* and Julia A. Schnabel*

WACV 2026

Journal

Temporal Neural Cellular Automata: Application to modeling of contrast enhancement in breast MRI

Daniel M. Lang, Richard Osuala, Veronika Spieker, Karim Lekadir, Rickmer Braren and Julia A. Schnabel

MICCAI 2025

arXiv Journal Project Code

Towards learning contrast kinetics with multi-condition latent diffusion models

Richard Osuala, Daniel M. Lang, Preeti Verma, Smriti Joshi, ..., Julia A. Schnabel and Karim Lekadir

MICCAI 2024

arXiv Journal Code

Multispectral 3D masked autoencoders for anomaly detection in non-contrast enhanced breast MRI

Daniel M. Lang, Eli Schwartz, Cosmin I. Bercea, Raja Giryes and Julia A. Schnabel

MICCAI Workshop on Cancer Prevention through Early Detection 2023

arXiv Journal

Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients

Daniel M. Lang, Jan C. Peeken, Stephanie E. Combs, Jan J. Wilkens and Stefan Bartzsch

Cancers 2021

arXiv Journal Code

Resume