My name is Auke. I'm working on applications of generative models.

I'm especially interested in running open-weight models on consumer hardware or edge devices. For example, it's already possible to run medium-sized language models on Apple silicon.

I was previously a research scientist/engineer at Qualcomm AI Research, where I touched many topics: efficient autoregressive inference, neural data compression, on-device deployment and synthetic data for autonomous driving to name a few. Before that, I trained neural networks using Theano as data scientist at Scyfer (acquired by Qualcomm in 2017).

Public talks, workshops, open-source work

• Producing adversarial audio.
  How to trick OpenAI's Whisper into producing wrong transcriptions.
• mlx_transformers_benchmark
  Benchmarking local LLMs and mlx operators on Apple silicon.
• Outstanding reviewers CVPR 2025.
  Nice to be acknowledged (5.3% of reviewers).
• Efficient Large Vision Models workshop @ CVPR 2025.
  Renewed! Co-organizing.
• Efficient Large Vision Models workshop @ CVPR 2024.
  Co-organizer.
• TWIML AI podcast
  I was a guest at Sam Charrington's excellent ML podcast.
• Efficient transformers: towards deployment on edge devices.
  Invited talk @ Transformers at Work 2022.
• Efficient Neural Codecs.
  Invited talk @ CLIC 2022 (CVPR workshop).

Selected publications