Co-Founding Partner at Interpretable AI
Dr. Daisy Zhuo is a Co-Founding Partner at Interpretable AI. She has extensive experience solving industry problems using advanced predictive and prescriptive analytics and AI systems, especially in the fields of health care and banking. She has a PhD in Operations Research at MIT, during which she has developed a range of cutting-edge machine learning techniques such as Optimal Imputation and Robust Classifications, with publications in major academic journals. Prior to MIT, Daisy was an analyst at an economics and health care consulting company.
Track: Data Mining
Tuesday, April 16, 11:30am–12:20pm
Optimal Imputation: Automated Data Quality Assurance and Improvement
Data quality issues such as missing values and outliers remain a key roadblock to deriving value from big data and machine learning deployments. We developed Optimal Imputation, a novel framework to impute missing data by jointly optimizing the values and the model (KNN, SVM, or decision-tree based models) on the data. In large-scale synthetic and real data experiments, we show Optimal Imputation produces the best overall imputation in the majority of all datasets benchmarked against state-of-the-art methods, with an average reduction of imputation errors by 10-15%. It further leads to significant improvement in regression (0.05 increase in R2) and classification (2% improvement in accuracy) tasks. We demonstrate the impact in real-world applications in insurance, banking, and health care settings.