Jacob Nielsen

Jacob.jpg

Odense, Denmark

I am a computer scientist from the University of Southern Denmark (SDU), specializing in artificial intelligence and machine learning. Currently, I am employed as an industrial PhD student at Ordbogen A/S and SDU, supervised by Peter Schneider-Kamp, Lukas Galke and Melih Kandemir. I primarily work with efficient deep learning in all aspects from training to inference. I am interested in many aspects and keep getting inspired, but examples are extreme quantization aware training and inference, distributed training optimization, optimizers and understanding and using latent spaces in the optimization process.

news

Nov 15, 2025 💥 DeToNATION was accepted to AAAI 2026! Paper
May 15, 2025 🚀 Continual Quantization-Aware Pre-Training: When to transition from 16-bit to 1.58-bit pre-training for BitNet language models?, has been accepted to the ACL 2025 Findings!
Feb 01, 2025 I officially started as an industrial PhD at Ordbogen A/S and University of Southern Denmark.
Jan 24, 2024 I successfully defended my masters thesis titled: “Object Detection with Transformers from A Drone Perspective” graded 12 on the Danish grading scale (ECTS mark: A)

selected publications

  1. DeToNATION: Decoupled Torch Network-Aware Training on Interlinked Online Nodes
    Mogens Henrik From, Jacob Nielsen, Lukas Galke Poech, and 1 more author
    In Proceedings of the annual AAAI Conference on Artificial Intelligence, 2025
  2. ACL
    Continual Quantization-Aware Pre-Training: When to transition from 16-bit to 1.58-bit pre-training for BitNet language models?
    Jacob Nielsen, Peter Schneider-Kamp, and Lukas Galke
    ACL Findings, 2025
  3. CVPR
    Multiview Aerial Visual Recognition (MAVREC): Can Multi-view Improve Aerial Visual Perception?
    Aritra Dutta, Srijan Das, Jacob Nielsen, and 2 more authors
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024