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 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.
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
-
CL-Splats: Continual Learning of Gaussian Splatting with Local Optimization
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.
Under Review
Website -
AIpparel: A Large Multimodal Generative Model for Digital Garments
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. Our novel tokenization scheme supports the generation of complex patterns beyond the capabilities of previous approaches, while the dedicated garment modality facilitates seamless iteration on garment designs.
Under Review
Arxiv | Website -
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