Hi! I'm a machine learning scientist with a software engineering & mathematics background.
Currently working at Plumerai on efficient privacy-preserving AI and preparing to defend my PhD thesis on hyperparameter optimization for deep learning (pursued at CWI & TU Delft).
I'm excited to apply my expertise to problems that matter, using whichever tool fits best: classic ML, deep learning, or generative AI.
My hobbies include reading (non-fiction / fantasy / diverse), hiking, poetic translation, and discussing weird ideas. Happy to chat, so get in touch :)
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To Be Greedy, or Not to Be - That Is the Question for Population Based Training Variants [TMLR]
TL;DR Bayesian PBTs optimize the greedy objective more effectively than non-Bayesian PBTs, this can be good or bad (depends on the task & hyperparams) | Paper | Code
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Transformer explanations: a collection
Transformer is a powerful architecture that can be difficult to understand. There are many great explanations on the web, each approaching the subject in a different way. Here I link the explanations I liked, and mention who I believe the target audience is for each one.
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PBT-NAS: mix architectures during training
Based on our paper “Shrink-Perturb Improves Architecture Mixing during Population Based Training for Neural Architecture Search” (ECAI 2023)
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Install nvcc without root
I needed to install
nvccon our group server, where I lack root privileges. I found a nice guide, in this post I will slightly expand on it by explicitly mentioning every step I had to take. -
ENCAS: Search cascades of neural networks in any model pool
Based on our paper “Evolutionary Neural Cascade Search across Supernetworks” (Best Paper award @ GECCO 2022)
TL;DR Search for cascades of neural networks in model pools of hundreds of models, optionally using Neural Architecture Search to efficiently generate many diverse task-specific neural networks. Get dominating trade-off fronts on ImageNet and CIFAR-10/100, improved ImageNet SOTA (of publicly available models).
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Document recognition with Python, OpenCV, and Tesseract
Recently I’ve conducted my own little experiment with the document recognition technology: I’ve successfully went from an image to the recognized editable text.
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- Cities where I lived at least a month: St. Petersburg, Cologne, Berlin, Amsterdam, Leiden
- Favorite color: Orange
- What DEFINITELY DID NOT influence my decision to move to 🇳🇱: my favorite color
- Most steps walked in a day: 35,228
- An octopus has more neurons in its tentacles than in its brain 🐙