Jake Brukhman
Founder & CEO at CoinFund
Miami, Florida, United States
Summary
👤
Senior🎓
Top SchoolJake Brukhman is a founder and CEO with 16 years of experience bridging rigorous mathematics, quantitative engineering, and product-focused technology leadership. He built and scaled trading and advertising systems at Highbridge and Amazon before founding CoinFund, where he leads investments and research at the intersection of blockchain, decentralization, and cryptoassets. His background in pure mathematics (NYU Courant, UCL, Rutgers) informs a data-driven approach to product strategy and quantitative research, and he has hands-on implementation experience—evident from a Go-based Naive Bayesian classifier he developed and tested. Based in Miami, he combines entrepreneurial insight with deep technical fluency, often translating complex crypto-economic concepts into investable technology opportunities.
16 years of coding experience
6 years of employment as a software developer
University College LondonMathematics, Mathematics at NYU, Courant Institute of Mathematical SciencesBachelor's degree Mathematics Computer Science, Bachelor's degree Mathematics Computer Science at Rutgers University
Russian
Stackoverflow
15,117reputation
3.3mreached
15answers
87questions
Github Skills (19)
algorithm10
algorithms10
probabilistic-programming10
data-modeling10
data-structure10
probabilistic-reasoning10
go10
statistical-models10
probabilistic-models10
data-structures10
unit-testing9
serialization9
data-serialization9
windows6
python6
Programming languages (10)
Github contributions (5)
jbrukh/bayesian
Nov 2011 - Mar 2020
Naive Bayesian Classification for Golang.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:60 commits, 16 PRs, 14 pushes in 8 years 5 months
Contributions summary:Jake primarily developed the core logic for a Naive Bayesian classifier in Go. Their work involved defining the classifier structure, implementing methods for learning from data, scoring documents, and calculating probabilities. Key changes include adding serialization capabilities, modifying the scoring mechanism, and adding checks and tests, demonstrating the implementation and testing of a probabilistic classification algorithm.
golangmachine-learningnaivebayesianclassification
jbrukh/sentiment
Nov 2011 - Dec 2011
Contributions:37 commits in 21 days
nlpclassifiersentimentsentiment-analysistwitter
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