Researched and developed successful machine learning/deep learning solutions for automated analysis and interpretation of biological signals. Prototyping of these solutions using python and pyTorch and final implementation into C# .Net software.

I successfully designed and integrated smart internal tools for data cleaning, effectively reducing the data processing and computational overhead. Notably optimising the internal graph reducing it's weight by over 50%, improving the overall accuracy of the proposed search results.

Responsible for data processing, specifically on the writing of automation script for the cleaning and integration of new transporter data. I also got a chance to work on the backend where I integrated new transporter APIs into the internal search engine, improving the diversity of the search results.