Many real-world problems involve parsing massive amounts of data, however, not every enterprise can sustain the budget or resources required for complex modeling. Nevertheless, accurate results are important. A solution may be to reduce the size of the sample group in order to ease the strain on resources; however, this also results in diminished accuracy. Carlotta Domeniconi’s research strives to solve this problem of scalability by combining parallel algorithms with ensemble and boosting to achieve closer accuracy with a smaller sample. The added strengths of new frontiers in machine learning (artificial intelligence), data mining, pattern recognition, and bioinformatics build a robust new rubric by which researchers are already applying standards. Domeniconi shares this knowledge in a challenging classroom environment for both undergraduate and graduate students. Her research laboratory in machine learning and data mining has received numerous grants from the National Science Foundation and the US Army.