Anna Shcherbacheva

• Applied and evaluated a variety of deep learning and machine learning models to process large-scale point cloud and raster data for semantic, instance, and panoptic segmentation, as well as object classification, in forestry and urban planning applications.
• Designed and prototyped a high-performing machine learning-based object classification tool, optimized for integration into commercial software.
• Leveraged scientific expertise to adapt and implement cutting-edge research into practical industry solutions.
• Developed polished, effective visualizations to communicate the results of complex data analyses.
• Continuously monitored advancements in relevant scientific fields to identify and integrate innovative methods and insights into workflows.o-date techniques for data processing using AI.

• As data scientist, my responsibility is to use supervised and unsupervised Machine Learning and Deep Learning algorithms to analyze data collected by different laser scanning platforms. The goal of data sets, which are mostly made up of point clouds of forest environments that have been laser scanned together with attributes that have been measured by scanners, is to make inferences about dynamics and state of forest.

I was working on a project to study how molecular clusters form in the atmosphere. It was my responsibility in this position to develop and put into practice an addition to the existing Bayesian data analysis framework for parameter estimation that was based on Markov Chain Monte Carlo.

In this role, I was participating in number of projects, which included designing of agent-based statistical models and implementation of numerical simulators aimed at prediction of financial markets behavior and forecasting spread of infectious diseases.