I'm Vera Dureke, an incoming Computer Science PhD student at the University of Illinois Chicago, where I'll be researching algorithmic fairness advised by Prof. Abolfazl Asudeh. I'm supported by a GEM Fellowship and focused on the ways ML systems encode and amplify bias — and how to fix that.
This summer I'm wrapping up an AI/VR internship at NASA Glenn Research Center's GVIS Lab, where I built an interactive VR exhibit for Meta Quest 3 featuring an AI astronaut guide, and developed an exoplanet detection ML system using Kepler light curve data.
I hold a double major in Data Science and Mathematics from Indiana University East (May 2026). Outside research I lead as President of Blacks in Technology Chicago, Marketing Lead for Out in Tech Chicago, and Fellow with Break Through Tech AI.
- LinkedIn: linkedin.com/in/vera-dureke
- Email: veradureke@gmail.com
ML system for detecting planetary transits in Kepler stellar light curves. Random Forest + 1D CNN pipeline with SMOTE oversampling and precision-recall threshold tuning. Built during NASA Glenn internship. Planet recall improved from 0% to 100% after addressing class imbalance.
Multiclass classification of technology products using XGBoost. Includes tokenization of product descriptions and precision-focused feature engineering.
Exploratory data analysis of global deforestation patterns. Geospatial analysis, data visualization dashboards, and insights for environmental sustainability.
Predictive analytics for a craft brewing company. Demand forecasting, time-series analysis, and market trend visualization.
Algorithmic fairness, data ethics, ML bias auditing, responsible AI. Particularly interested in how fairness constraints interact with model performance across demographic groups, and the policy implications of biased automated systems.
