Welcome to my homepage. I am a Deep Learning Researcher at Qualcomm AI Research working on efficient generative modeling.
I am interested in data and model compression, generative modeling, information theory, and bayesian inference.
I have a master degree in Artificial Intelligence from the University of Amsterdam.
You can find some of my work here, or check me out on social media:
Find some of my recent publications below.
Under review.
Co-developed and co-authored a quantization library for efficient large language models (LLMs).
Accepted at TMLR.
Accepted at ICLR 2021.
Accepted at CVPR CLIC workshop 2020.
Accepted at ICCV 2019.
Master thesis. Investigating how conditional variational autoencoder can be used for voice conversion with the goal of improving robustness of automated speech recognition (ASR) systems.
During an internship at the Keiser lab at the UCSF (University of California, San Francisco) I developed this framework to build various architectures of Graph Convolutional Networks, and run them on GPU.
Bachelor thesis, investigating the effect of electrical cortical stimulation on visual attention in humans.
Literature analysis about the mechanisms behind consistency in visual perception across eye-movements.
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