Jan Ackermann
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.
Lecture Recommendations
I have thoroughly enjoyed the following lectures and highly recommend them to anyone interested in these fields. While some are specific to the universities I have attended, others are accessible online. (And yes, I have heard multiple lectures on the same topics!)- Linear Algebra - Gilbert Strang's Linear Algebra lectures are essential for anyone exploring machine learning. He takes the time to develop a deep intuition for each topic, making the material incredibly engaging and insightful.
- Topology & Geometry - This lecture series by Tadashi Tokieda is one of my absolute favorites. While introducing the field, he masterfully demonstrates the elegance of visual problem-solving. Don't forget to draw pictures!
- Computer Vision - Andreas Geiger's publicly available lecture series is my top recommendation for Computer Vision. It provides a solid introduction to the field while also covering more advanced and contemporary topics.
- Big Data - Ghislain Fourny's lecture at ETH Zurich is a joy to follow for anyone keen on Big Data. It thoroughly addresses the core topics of storing, retrieving, and manipulating large-scale datasets.
- Operations Research - This was the most enjoyable lecture I attended during my time at Aachen. Marco Lübbecke and his team excelled at blending theoretical concepts with practical applications of problem modeling techniques. Even the homework was fun!
Development Setup
- Text Editor: Neovim
- Laptop: Apple M1 Macbook Pro 16''
- Keyboard: Glove 80
- CPU: Intel Core i7-13700K
- GPU: NVIDIA GeForce RTX 4080
- RAM: 64GB DDR5
- Storage: Samsung 990 Pro 2TB NVMe SSD
- Monitor: 27'' LG Ultragear WQHD Monitor
- Operating System: Debian 12
Publications
-
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 | Arxiv -
Maskomaly: 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