Richard Csaky
Affiliated Scientist (AI)
Budapest, Central Hungary, United States
Summary
👤
Senior🎓
Top SchoolRichard Csaky is an Affiliated Scientist and ML–neuroscience researcher with a decade of experience building brain-decoding models, real-time gesture interfaces, and transformer-based dialog systems. He holds a PhD from Oxford where he scaled group decoding and developed GPT2MEG-style generative approaches for EEG/MEG, and his recent Foresight Institute–funded work produced large-scale foundation models for electrophysiology trained on 500+ hours of MEG. Equally comfortable shipping production ML pipelines (real-time MMG gesture control with sub-300ms latency) and open research tooling, he has open-sourced platforms for scalable brain-model training and novel datasets from earlier dialog work. Based in Budapest, he blends deep theoretical interests in agency, information, and perception with hands-on systems engineering across academia and industry. Notably, his research repeatedly leverages synthetic-data pretraining and subject embeddings to improve cross-participant generalization, reflecting a rare mix of experimental neuroscience insight and modern foundation-model practice.
10 years of coding experience
4 years of employment as a software developer
Master's degree, Artificial Intelligence, Master's degree, Artificial Intelligence at KU LeuvenDoctor of Philosophy - PhD, Machine Learning and Neuroscience, Doctor of Philosophy - PhD, Machine Learning and Neuroscience at University of OxfordMaster's degree, Computer Science Engineering, Master's degree, Computer Science Engineering at Budapest University of Technology and Economics
English, French, Hungarian, Romanian
Github Skills (52)
conversation10
clustering-algorithm10
dialog10
autoencoder10
tensorflow10
tensor2tensor10
transformer10
wrapper10
chatbot10
seq2seq9
datasets9
hdbscan9
pipeline9
neural-network9
dialogue-systems9
Programming languages (4)
Github contributions (5)
ricsinaruto/MEG-transfer-decoding
Mar 2023 - May 2024
Explore the differences between sliding window and full-epoch models on MEG data and use PFI to uncover neuroscientific insights.
Contributions:1 release, 5 PRs, 54 pushes in 1 year 2 months
decodingmachine-learningmegneuroimagingneuroscience
ricsinaruto/gutenberg-dialog
Aug 2019 - Feb 2021
Build a dialog dataset from online books in many languages
Contributions:75 commits, 3 PRs, 50 pushes in 1 year 5 months
online-booksdialogpipelineconversationdataset
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