SBIR Phase I: Feature Engineering Automation via Human Insight Integration and End to End Optimization

Pyxeda, Inc, Santa Clara CA

Investigators

Abstract

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to enable individuals with minimal mathematics and programming knowledge to learn and build AI, and through that success open a pathway to greater STEM engagement and success. This innovation helps by automating a key component of the AI lifecycle, Feature Engineering (FE), within a full lifecycle framework. Industries can apply AI to business solutions faster, at lower cost, and with existing employees. By focusing on enterprise data, this technology enables broad corporate use and subsequent economic benefit. This Small Business Innovation Research (SBIR) Phase I project addresses a key limitation of commercial AI solutions, the human expert driven and largely manual Feature Engineering (FE) stage. The innovation is a novel approach to FE automation within a full lifecycle context, with three differentiations: (a) embedding human insight via auto-generated and human annotated Knowledge Graphs, (b) structuring the complex FE process as a series of sub-stages for dynamic combination of innovations, further tuned via Knowledge Graph insight and (c) iterative optimization via quantitative end-to-end feedback. The approach integrates expert intuition, enabling a wide selection of potential FE transformations and explorable state space via the Knowledge Graph and feedback. The research ensures translational viability by testing against real-world scenarios and utilizing customer datasets that directly expose realistic FE problems, addressing a significant limitation of past research efforts. The project strives to prove the feasibility and benefits of this approach in tackling Feature Engineering problems in production enterprises. The anticipated result is a solution that can be easily commercialized to automate Feature Engineering for a wide range of business usages using numerical, categorical, text and time series data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.