Arsen Mamikonyan
Founder & CTO at LoopedIn Health
Yerevan, Armenia
Summary
👤
Senior🎓
Top SchoolArsen Mamikonyan is a Founder & CTO and machine learning engineer with 12 years of experience turning theoretical Bayesian inference and mathematical models into production-grade data products and teams. He built and led engineering organizations across fintech and consumer finance—shipping recommendation engines, algorithmic research, and data ingestion pipelines—and founded a self-sustaining ML consulting firm that ran proprietary crypto trading and portfolio services. An MIT-trained engineer (MEng, CS) and former MIT research assistant, he contributed Variational Bayes outlier detection to MacroBase and brings uncommon depth in probabilistic modeling applied at scale. Based in Yerevan, he combines hands-on backend development and productionization with teaching experience at university level, and now leads LoopedIn Health as CTO, bridging research insights to commercial healthcare products.
12 years of coding experience
11 years of employment as a software developer
Master of Engineering (MEng), Computer Science, 5.0/5.0, Master of Engineering (MEng), Computer Science, 5.0/5.0 at Massachusetts Institute of TechnologyBachelor of Arts (BA), Physics, Bachelor of Arts (BA), Physics at University of Cambridge
English, Armenian, Russian, Spanish
Github Skills (13)
javas10
postgresql10
java10
k10
data-analysis10
python9
machine-learning9
data-visualisation9
data-visualization9
data-visualizations9
bayesian8
variational-inference8
bayesian-inference8
Programming languages (8)
Github contributions (5)
stanford-futuredata/macrobase
Jan 2016 - Jan 2017
MacroBase: A Search Engine for Fast Data
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:199 commits, 84 PRs, 250 pushes in 1 year
Contributions summary:Arsen primarily contributed to the back-end functionalities of the `MacroBase` project, including the implementation of configurations for database access, such as user credentials. The contributions involved modifications to SQL loading, data transformation, and the addition of outlier detection algorithms, such as KDE. Furthermore, the user worked on implementing new features related to data analysis and created scripts to plot and compare different outlier detection models.
dataframesmacrosquery-languagesearch-enginedatabase
mamikonyana/kubernetes
Jun 2015 - Jan 2016
Contributions:29 pushes, 12 branches in 7 months
gcpcontainer-clusterdockerkubernetescluster-manager
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