[first] [last] [at] google.com
Hello! I’m an engineer at Google DeepMind, where I work on post-training for the Gemini models. During my master’s, I conducted my thesis research as a visiting student in Gordon Wetzstein’s lab at Stanford University, completed an internship at Google with Thabo Beeler’s group, and spent time at Peking University, working with Liwei Wang. I earned my undergraduate degree from RWTH Aachen University, where I completed my bachelor’s thesis under the supervision of Leif Kobbelt.
Outside of research (but still cs), I really enjoy competitive programming. During university, I had a great time competing alongside my friends Vincent de Bakker, Lennart Ferlemann, and Viktor Körner as team ''r/wth''. I am really grateful for Peter Rossmanith who brought us together and coached us through the years. Music is a big part of my life, I play both the violin and the piano. I'm also a big fan of Nintendo games — from the Zelda series to Mario Kart. Another nostalgic passion of mine is collecting LEGO sets.
In Submission
TL;DR: We propose AsymFlow, a rank-asymmetric velocity parameterization that predicts noise in a low-rank subspace while preserving full-dimensional data prediction. This analytically recovers full-dimensional velocity without changing architecture or training, setting new state-of-the-art performance for pixel-space image generation and enabling effective finetuning from pretrained latent flow models.
SIGGRAPH Conference, 2026
TL;DR: We introduce a real-time, task-aware foveated imaging system that learns a policy to allocate scarce sensor bandwidth to the most informative regions while preserving low-resolution global context. This closes the perception-acquisition loop at capture time and delivers strong perception performance under strict pixel budgets in both simulation and real-world 200MP sensor experiments.
In Submission
TL;DR: We introduce a geometry-consistency reward that enforces physically plausible motion in generated videos. By decomposing flow into rigid camera motion and independent object motion, we turn geometric coherence into an explicit optimization objective via reinforcement fine-tuning, substantially reducing temporal artifacts while preserving quality.
Computer Vision and Pattern Recognition (CVPR), 2026
TL;DR: We propose a novel approach to video generation that decouples the control of time and camera pose. We introduce a new dataset and model that allows for the generation of videos with both temporal and spatial condition.
International Conference on Computer Vision (ICCV), 2025
TL;DR: We explore how to adapt an existing 3DGS scene representation to new inceremental changes. We propose a novel and efficient way to identify changed regions and then to locally optimize them. This not only produces more accurate scene updates but also enables new applications.
Computer Vision and Pattern Recognition (CVPR), 2025 Highlight
TL;DR: We introduce AIpparel, the first large-scale multimodal generative model designed specifically for digital garments. By extending LLaVA to incorporate a new garment modality, AIpparel enables the creation of sewing patterns from text, image, and garment inputs.
International Conference on Machine Learning (ICML), 2024
TL;DR: We explore the class of Linear and Sparse Transformers in a Chain-of-Thought (CoT) setting, finding that to match the performance of regular Transformers, their hidden dimensions must scale with the problem size.
British Machine Vision Conference (BMVC), 2023 Oral
TL;DR: We show that pretrained Mask-based segmentation models can predict anomalies without further tuning. Additionally, we introduce a metric for anomaly segmentation that favors models with confident predictions.
I have had the pleasure of working with the following students: