Electronica AI’s team of data scientists work with big streams of data, transforming complex relationships into meaningful structures with high predictive accuracy.

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Alternative Advisory

Using our foundation in machine learning and finance, we develop systems to validate, train and deploy efficient analytics tools.


Data Science

We have developed a state of the art data science pipeline for analyzing dense time series data.


Machine Learning

Our algorithms get smarter everyday, learning to stay ahead of the curve and to operate on noisy data.

Our Team

  • Aristotle Andrulakis

    Founder | CEO

  • Paul Haefele


  • Jon Lorraine

    Lead ML Engineer

  • Alex Bath

    Full Stack Developer

  • Geoff Roeder

    ML Advisor

  • Moeen Bagheri

    Quantitative Developer

  • Nick Wright

    Corporate/Commercial Law Specialist


Stochastic Hyperparameter Optimization Through Hypernetworks

Machine learning models are often tuned by nesting optimization of model weights inside the optimization of hyperparameters. We give a method to collapse this nested optimization into joint stochastic optimization of weights and hyperparameters. Our process trains a neural network to output approximately optimal weights as a function of hyperparameters. We show that our technique converges to locally optimal weights and hyperparameters for sufficiently large hypernetworks. We compare this method to standard hyperparameter optimization strategies and demonstrate its effectiveness for tuning thousands of hyperparameters.