Keyvan Sadeghi
Old Toronto, Ontario, Canada
Summary
👤
Senior🎓
Top SchoolKeyvan Sadeghi is a VP of Software Engineering and serial founder with 13 years building AI-first products that span NLP, agent-driven interfaces, decentralized infrastructure, and AI hardware. He has led and scaled teams from early-stage startups to production deployments—cofounding Functionland (1,000+ nodes, petabyte-scale distributed storage) and Assister.ai—and now runs riahub.ai to simplify SOTA ML for software-defined radio. A researcher-turned-executive, he contributed Bayesian network structure learning to the OpenCog AGI project and partnered with leading ecosystems like Protocol Labs and Techstars to turn research into real-world systems. Comfortable shifting between deep technical work and fundraising/partnerships, he blends systems architecture, probabilistic modeling, and product strategy to deliver emergent AI capabilities with an eye toward data sovereignty.
13 years of coding experience
12 years of employment as a software developer
BSc, Software Engineering, BSc, Software Engineering at Azad University (IAU)MSc, Artificial Intelligence, MSc, Artificial Intelligence at University of Southampton
English, persian/farsi
Stackoverflow
853reputation
573kreached
13answers
4questions
Github Skills (17)
python10
scikit10
machine-learning10
statistical-models10
scikit-learn10
bayesian-network10
data-analysis10
data-science9
numpy9
transpose6
neo4j6
nodejs6
functional-programming6
spidermonkey6
office-js6
Programming languages (13)
Github contributions (5)
opencog/opencog
Aug 2012 - Jul 2014
A framework for integrated Artificial Intelligence & Artificial General Intelligence (AGI)
Role in this project:
Data Scientist
Data ScientistContributions:179 commits, 11 comments, 1 issue in 1 year 11 months
Contributions summary:Keyvan primarily contributed to the development of Bayesian Network structure learning within the opencog repository, focusing on data analysis and probabilistic models. They implemented and refined a scoring function, specifically the Bayesian Information Criterion (BIC), and adapted code to work with the UCI adult dataset. Furthermore, the user refactored code, implemented formulas, and experimented with various model training approaches, leveraging statistical and machine learning techniques.
unsupervised-learning-algorithmsroboticsnatural-language-understandingartificial-general-intelligenceintegrated
keyvan-m-sadeghi/assister
Aug 2018 - Dec 2019
Contributions:106 pushes, 9 branches in 1 year 4 months
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