Richard Csaky
Affiliated Scientist (AI)
Budapest, Hungary
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 (47)
conversation10
dialog10
seq2seq10
dialogue-systems10
tensorflow10
tensor2tensor10
transformer10
wrapper10
entropy10
chatbot10
clustering-algorithm9
reinforcement-learning9
evaluation9
machine-translation9
hdbscan9
Programming languages (4)
Github contributions (5)
ricsinaruto/Seq2seqChatbots
Oct 2017 - May 2020
A wrapper around tensor2tensor to flexibly train, interact, and generate data for neural chatbots.
Contributions:4 releases, 232 commits, 10 PRs in 2 years 7 months
tensor2tensorchatbotdialogneural-networksseq2seq
ricsinaruto/dialog-eval
May 2019 - Aug 2020
Evaluate your dialog model with 17 metrics! (see paper)
Contributions:28 commits, 26 pushes, 1 branch in 1 year 2 months
dialogchatbotentropyembeddingsevaluation
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