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Iaroslav Omelianenko

CTO/Research Director at NewGround LLC

NewGround Advanced Research

Biography

Iaroslav Omelianenko is a CTO and Research Director at the NewGround LLC. His research interests include human-computer interaction, genetic algorithms, neuroevolution of augmented topologies, reinforcement learning, control & optimization, and Neurobiology.

He leads the Research and Development team, which applies genetic algorithms to create artificial neural networks with a minimal footprint to solve a variety of control & optimization tasks as well as do research in brain-computer interfaces.

He has more than 30 years of experience with software design, implementation, and project management. He actively participates in open source projects. He presented research papers as an author at international conferences.

He is an author of the book Hands-On Neuroevolution with Python now available on Amazon. Also, he co-authored the book Machine Learning and the City: Applications in Architecture and Urban Design.

Interests

  • Artificial Intelligence
  • Genetic Algorithms and Neuroevolution
  • Synthetic Cognitive Systems
  • Brain-Computer Interface
  • Cooperative Robotics
  • Neurobiology and Neuroscience
  • Mobile Computing

Education

  • Master of Engineering, Industrial Process Management, 1999

    Ukrainian State University of Food Technologies

Recent Posts

Creation of Autonomous Artificial Intelligent Agents using Novelty Search method of fitness function optimization

The Novelty Search optimization seems like a natural fit for Neuro-evolution family of genetic algorithms producing elegant custom …

Self Replication to Preserve Innate Learned Structures in Artificial Neural Networks

It’s interesting to investigate combination of deep neuro-evolution and self-replication to evolve Artificial Neural Networks (ANNs) …

Neuroevolution

The neuroevolution methods of ANN training allows us to start with a very simple synthetic organism and evolve it to produce a unit of …

Projects

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GO NEAT

This project provides GOLang implementation of Neuro-Evolution of Augmented Topologies (NEAT) algorithm. The neuroevolution methods of …

SSVEP Brain Hash Function

Our goal is to create Brain Hash Algorithm able to produce robust distinction between EEG signals of different humans under …

Psistats

The project had a goal to provide the working implementation of psycho-demographic profiling algorithm, which can be used to profile …

Recent & Upcoming Talks

UkrPROG 2024 paper presentation

I presented my research paper "Application of the coevolution strategy to solve the problem of autonomous navigation through the …

ICICT 2024 paper presentation

I presented my research paper "Design of cluster-computing architecture to improve training speed of the Neuroevolution …

FTC 2017 paper presentation

I presented our research paper "Applying Deep Machine Learning for Psycho-Demographic Profiling of Internet Users using O.C.E.A.N. …

Recent Publications

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Design of cluster-computing architecture to improve training speed of the Neuroevolution algorithm

In this paper, we review the key features and major drawbacks of the Neuroevolution of Augmenting Topologies (NEAT) algorithm, such as …

Artificial swarm intelligence

In this article, we explore how artificial swarm intelligence evolve through evolutionary algorithms aimed at reducing the system’s …

Simulation of the Autonomous Maze Navigation using the NEAT Algorithm

The article deals with the problem of finding a solution for the navigational task of navigating a maze by an autonomous agent …

Machine Learning and the City: Applications in Architecture and Urban Design

Machine Learning and the City: Applications in Architecture and Urban Design delivers a robust exploration of machine learning (ML) and …

Hands-On Neuroevolution With Python

Neuroevolution is a form of artificial intelligence learning that uses evolutionary algorithms to simplify the process of solving …