Albert Farkhutdinov

Developed a service that improved the accuracy of seismic trace processing by up to 93%. Optimized and expanded a service that improves the resolution of processed seismic images, reducing processing cycle times by more than 5 hours per week.

Launched 3 trading strategies that improved trading performance and profitability for clients. Engineered robust scheduled data processing pipelines, achieving a 95% reduction in human error rates.

Designed a mathematical model that increased client revenue by approximately 40%. Orchestrated the creation of a robust microservices architecture, consolidating 8 disparate systems into one cohesive solution.

Authored a comprehensive Python framework to simulate heating and cooling phenomena using molecular dynamics, resulting in 7 scientific publications. Implemented advanced machine learning models to predict structural properties, reducing computational time by more than 15 times.

Upgraded 2 probabilistic process models that improved predictive accuracy and decision-making capabilities. Developed unique tools that resulted in the collection of approximately 10,000 records per month, ensuring continuous availability of relevant input parameters.

Created a Python package to calculate crystalline electric field parameters, accelerating research by 10 hours per week. Streamlined the initial parameter fitting process by automating procedures, reducing setup time from 60 to 5 minutes per experiment.

Pursued advanced studies in Radiophysics, deepening knowledge in the field. Focused on complex theoretical and practical aspects of radiophysics.

Completed undergraduate studies in Radiophysics, building a foundational understanding of the subject. Engaged in core coursework and initial research.