dvukolov / dvukolov Goto Github PK

I'm a data analyst passionate about coding in Python, R, and Go.

Some of my projects include:

• Conducted in-depth analysis of an uncertainty calibration algorithm for Bayesian neural networks.
• Identified the advantages of the approach and its modes of failure.
• Received the highest grade and an invitation to continue research at the Harvard Data to Actionable Knowledge lab.

• Used TensorFlow to estimate the shape and brightness of simulated galaxies.
• Responsible for data generation, exploratory data analysis web app, neural architecture search, and denoising pipelines.
• Got the top grade and an opportunity to continue research.

• Performed statistical modeling of potential bias in the NBA that grants teams in one conference an easier path to success due to the differences in travel and schedule.
• Wrote web scrapers, feature engineering, and most of the analysis code in R.
• Built linear regression models, ran diagnostics, authored around 70% of the report.

• Implemented in Python streaming data collection from weather instruments.
• The code has a suite of unit tests and is deployed at a university meteorological station.
• Serves as the basis for experimental studies of turbulence by five scientific institutions.

• Contributed with bug fixes and documentation updates to open source projects such as Apache Arrow, scikit-learn, ThunderSVM (GPU accelerated SVM), Picasso (sparse regression algorithm), Gap Statistic (clustering metric).