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Email: KLIMENKO.DNK@gmail.com
One of my Research:
Breakthroughs in
inspired by Neuroscience.
Mimic birds revealed insights for explaining flight principles that led to the invention of airplanes.

As a Data Researcher, I mimic Computational Neuroscience hypotheses and apply scientific / engineering methods to present new solutions for cracking challenging AI/ML/Quant tasks.
Aiming to solve diverse Quantitative tasks focusing on delivering advanced algorithmic trading strategy for financial markets. Including data mining, asset allocation, risk management, robustness validation, etc.

Biological neural networks are very different from AI-based neuron networks. Still, the discovered paradigm shift revealed solutions to many classical AI-challenging tasks efficiently solved in the Brain. Moreover, revealed technological insights highlighted a way for AGI's practical implementation in a very appropriate environment of financial markets.
A few of the Research models:
Novel AI Model
Novel AI Model to perform reinforcement, unsupervised continuous learning by exploring limited environment data and modeling the internal representation of the world. Able to generate a sequence of actions. Fast adaptation to changes in the environment.
Artificial bio-Neuron Model
AI neurons are significantly different from Biological neurons. Developed models of bio-Neuron open new possibilities to tackle practical problems in entirely new ways.

The AI neuron computes the output value as a weighted summation of inputs transformed by an activation function. Backpropagating prediction errors update weight parameters.

Today's AI has accomplished many remarkable tasks. But AI neurons and deep neural networks have problems like gradient vanishing, fixed topology, long and expensive training, large training data sets, etc.

The insights received from a detailed analysis of biological approaches make it possible to rethink the practical implementation of Deep-learning by redesigning the calculation flow. Aiming to amplify the advantages of both appraoches.
Evolutionary Solutions
Self-improvement of AI-model blueprints by evolving logic architecture powered by genetic algorithms.
A predefined genetic-like code data structure describes component parameters and structural architecture. Practical usage in storing networks and distributed computation parameter synchronizations.

Developed algorithms for mutation, crossover, and selection operations on multiple levels of the AI system.

A solution for direct estimation of various components' optimal parameter values.