Botos Csaba
Research Fellow at University of Oxford
Oxford, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolBotos Csaba is a machine learning researcher and engineer with 11 years of experience bridging academic rigor and production-grade vision systems, currently a Research Fellow at the University of Oxford working on neural and behavioral models of human gameplay. His work spans continual and self-supervised learning under tight computational constraints, efficient semi-supervised fine-tuning, and architecture design for high-frequency trading, reflecting both theoretical depth and applied impact. He has contributed to high-profile open-source projects such as facebookresearch/maskrcnn-benchmark—improving instance segmentation, dataset support and evaluation—and has practical backend experience improving API clients. A versatile mentor and communicator, he organizes seminars, teaches at summer schools, and leads outreach programs to demystify complex ML concepts. Notably, his interdisciplinary background includes work with MEG/fMRI data and low-cost hardware automation in neuroscience labs, showing a knack for finding practical, overlooked solutions.
11 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Machine Learning, Doctor of Philosophy - PhD, Machine Learning at University of OxfordBS, Computer Science, 4,94 - first class honours with distinction, BS, Computer Science, 4,94 - first class honours with distinction at Pázmány Péter Katolikus Egyetem
English, Hungarian
Github Skills (9)
mask-rcnn10
debugging10
computer-vision10
pytorch10
machine-learning10
deep-learning10
instance-segmentation10
python10
api-design9
Programming languages (7)
Github contributions (5)
facebookresearch/maskrcnn-benchmark
Feb 2019 - Oct 2019
Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.
Role in this project:
ML Engineer
ML EngineerContributions:38 commits, 45 PRs, 18 pushes in 8 months
Contributions summary:Botos primarily contributes to the core functionality of the Mask R-CNN benchmark, with a focus on improving the instance segmentation capabilities. Their work includes refactoring and enhancement of binary mask handling, with support for RLE and polygon-based segmentation masks. The user also optimized image processing within the model by implementing internal interpolation functions. Additionally, the user added and integrated Cityscapes dataset support and corresponding evaluation metrics.
instance-segmentationobject-detectionpytorch
fbchat-dev/fbchat
Mar 2017 - Mar 2017
Facebook Chat (Messenger) for Python
Role in this project:Back-end Developer
Back-end DeveloperContributions:9 commits, 1 PR, 1 comment in 1 day
Contributions summary:Botos primarily focused on fixing and refactoring the `getThreadInfo` and `getThreadList` functions within the `fbchat/client.py` file. Their commits addressed issues related to deprecated parameters, incorrect logic, and limitations in the API. The user's contributions involved modifying the parameters used in the methods and updating assertions to reflect changes in the API's behavior.
pythonfacebook-messengerfacebook-messenger-bot
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