About me

Welcome to my homepage. I am a Deep Learning Researcher at Qualcomm AI Research working on data compression.

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:

Recent Work

Find some of my recent publications below.

2023 Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set

Accepted at TMLR.

2021 Overfitting for Fun and Profit: Instance-Adaptive Data Compression

Accepted at ICLR 2021.

2020 Lossy Compression with Distortion Constrained Optimization

Accepted at CVPR CLIC workshop 2020.

2019 Video Compression With Rate-Distortion Autoencoders

Accepted at ICCV 2019.

2019 ASR Personalization by Voice Conversion

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.

2016 Keras Neural Graph Fingerprint

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.

2013 The effect of alpha stimulation of the visual cortex on visual search efficiency.

Bachelor thesis, investigating the effect of electrical cortical stimulation on visual attention in humans.

2012 Visual stability: Spatiotopic maps or transsaccadic remapping?

Literature analysis about the mechanisms behind consistency in visual perception across eye-movements.

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