Email: 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.
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 my
semester thesis with Christos Sakaridis. 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.
Do Efficient Transformers Really Save Computation?
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. However, we also identify a simple criterion that enables efficient Transformers to operate even more effectively.
International Conference on Machine Learning (ICML), 2024
Paper | ArxivMaskomaly: Zero-shot Mask Anomaly Segmentation
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.
British Machine Vision Conference (BMVC), 2023 (oral)
Paper | Arxiv