Olesya Bondarenko

San Diego, CA · obondarenko@ucsd.edu

Researcher with 10+ years experience in the areas of photonics and semiconductors, and almost 6 years of product development experience for seed and pre-seed startup companies. Expert in R&D project management, product prototyping, process development and metrology. Experienced in statistical analysis, predictive modeling, data mining, data management, and visualization. Collaborated in multidisciplinary teams of scientists and engineers; initiated and led R&D projects; established new connections with the outside groups.

Experience

Data Scientist

Material Mind AI
  • Development of an analytical tool for automated feature extraction from material electronic band diagrams to predict their spin-Hall conductivity with a 100 % remote team
  • Proposed, planned and built document database (MongoDB) for dynamic aggregation and storage of processed and updated band diagrams
  • Created WSGI Flask demo application to showcase the material property predictor and feature finder tool to potential clients and investors
February 2020 - Present

Data Science & Web Development

Tangible AI
  • Development of Elasticsearch semantic search engine for an intelligent open source virtual assistant
  • Built a dockerized Django application for the search engine and the virtual assistant
  • Repositories
February 2020 - Present

Scientist - Electrical Engineering

Pacific Integrated Energy, Inc.
  • Started and led a variety of projects developing cutting-edge thin-film photovoltaic materials for cost-effective solar cell manufacturing
  • Collected, analysed and managed data from various sources using Python’s Pandas, StatsModels, Matplotlib, Scipy and Pymongo libraries
  • Developed and managed MongoDB database for heterogeneous data aggregation and centralized access by other team members
  • Analysis of state-of-the-art literature, idea generation, coordination of a multidisciplinary team of scientists and engineers
July 2014 - December 2019

Graduate Researcher - Electrical Engineering

University of California San Diego
  • Led research on extreme miniaturization of lasers for telecommunication and sensing devices
  • Developed analytical electromagnetic and thermal models in COMSOL and Matlab
  • Authored and co-authored multiple peer-reviewed publications and conference presentations
January 2009 - June 2014

Education

University of California San Diego

Data Science and Visualization Certificate Program
January 2019 - August 2019

University of California San Diego

Doctor of Philosophy & Master of Science
Electrical Engineering - Optics and Photonics
January 2009 - June 2014

Skills

Programming Languages & Tools
Tools
  • Databases: MongoDB, ElasticSearch, MySQL, SQLite
  • Machine learning: Scikit-Learn, Tensorflow, Keras, PyTorch, NLTK
  • Visualization: Matplotlib, Seaborn, Plotly, Tableau
  • Data collection & analysis: Pandas, StatsModels, Numpy, Requests, BeautifulSoup, Scrapy
  • Web development: Django, Flask, Bootstrap, Plotly-Dash, Docker, AWS

Selected Projects

  • Twitter Data Mining Sentiment Analysis: Collected Twitter posts of 2020 presidential candidates and financial influencers to observe any correlation between the overall tweet sentiment and the stock market movements.
  • Forecasting of Wait Times on Street Intersections: Predicted traffic wait times on busy intersections using a dataset provided by BigQuery-Geotab on Kaggle. Machine learning methodologies include linear regression, random forests, gradient boosted decision trees, Bayesian optimization, cross-validation and parameter grid search.
  • Place - University of Colorado Boulder - Emerging Tech Competition 2009
  • Exoplanet Classification: Applied k-means clustering, support vector machines, random forests and CNN-based deep learning approach to classify space objects detected by NASA’s Kepler space telescope and identify exoplanets. Machine learning performed using support vector machines, random forests and convolutional neural networks.
  • Biodiversity App: Built full stack Flask application for exploration and visualization of bacterial populations in human navels. The interactive visualization dashboard is built using Javascript and Bootstrap/HTML. The app is deployed to Heroku server.
  • Mission To Mars App: Built full stack Flask application which uses Splinter and Beautiful Soup libraries to scrape the latest news related to Mars from NASA, USGS, Twitter and other websites on user’s request
  • Full Stack Visualization App - Google Store: Built full stack Flask application for interactive visualization of trends in Google Store applications.
  • ETL Project: Collected data and explored correlation between socio-economic statistical data from Census API and public health data, provided by Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health for 500 US cities. Transformed the data in Pandas and loaded to MySQL database using the Python SQLAlchemy library. Executed SQL inner join to merge the datasets.