CAREER: Intrinsic and Extrinsic Conditions in Several Complex Variables

This project concerns complex analysis, which combines complex numbers with the theory of calculus. Complex analysis is a fundamental tool in many applications. In particular, it is used in physics (for instance: studying the flow of air past an airfoil and dispersion relations in optics), engineering (for instance: signal processing and control theory), and computer science (for instance: image processing and quantum computation).

Complex analysis of a single variable is a classical and well understood mathematical subject, but when additional variables are introduced many mysteries remain. In this project, the PI will further the theoretical understanding of complex analysis of several variables. This project also has a substantial educational component: The PI will develop a research experience in data science for first year undergraduate students at Louisiana State University (LSU).

This program will prepare participants for a career in STEM, solidify their knowledge of basic mathematics, and give them an opportunity to learn about data science.

In the research part of this project, the PI will study bounded domains in complex Euclidean space and relate intrinsic geometric conditions (e.g. the geometry of the Kaehler-Einstein metric) to extrinsic geometric conditions (e.g. the CR-geometry of the boundary). Many classical results in the subject consider bounded domains in complex Euclidean space, assume that the boundary is smooth, and make assumptions about the CR-geometry of the boundary.

For domains with non-smooth boundary, the PI will prove new variants of classical results by assuming geometric conditions on the Kaehler-Einstein metric instead of conditions on the boundary. Using this approach, the PI will be able to study classes of domains which are typically outside the reach of the standard analytic methods and also make progress on old problems.

This approach is motivated by the great success of geometric group theory where metric space techniques applied to group theory not only lead to progress on old problems, but also many new and interesting examples.

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.