first.last [at] hotmail.com
Hello there! I am a master's student at ETH Zurich, specializing in the intersection of Vision, Graphics, and Deep Learning. Soon, I will be joining Google Deepmind as an engineer.
Currently, I have the privilege of visiting Gordon Wetzstein's lab at Stanford University, where I am fortunate to be supervised by Guandao Yang.
Prior to this, I completed an internship at Google with Thabo Beeler's group and spent time as a visiting student at Peking University, working under Liwei Wang. At ETH Zurich, I completed one semester thesis with Christos Sakaridis and another with Songyou Peng. I earned my bachelor's degree from RWTH Aachen University, where I wrote my bachelor's thesis under the guidance of Leif Kobbelt.
Outside of research (but still cs), I really enjoy coding. During university, I had a great time exploring competitive programming, competing alongside my friends Vincent de Bakker, Lennart Ferlemann, and Viktor Körner as team ''r/wth''. I have also had the opportunity to apply my software engineering skills in large-scale projects during internships at Amazon AGI, Google Gemini, and Optiver D1.
Under Review
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
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.