Current Research

I use numerical analysis, PDE theory, uncertainty quantification and applied statistics to study inverse problems. In particular, my work focuses on sensitivity analysis and optimal experimental design (finding sensor placements) for Bayesian inverse problems.

Publications

• Isaac Sunseri, Joseph Hart, Bart van Bloemen Waanders, and Alen Alexandarian. A computational framework for quantifying the relative importance of data sources and physical parameters in PDE-based inverse problems. In preparation, 2019.

• Alen Alexanderian, Noemi Petra, Georg Stadler, Isaac Sunseri. Marginalized A-optimal design of experiments for large-scale Bayesian linear inverse problems. In preparation, 2019.

Talks and Presentations

• Seminar Talk. Isaac Sunseri. Bayesian Approach to Inverse Problems: An Introduction with Applications. North Carolina State University, Raleigh NC. Presented to the Applied Math Graduate Student Seminar (AMGSS). November 2018.

• Seminar Talk. Isaac Sunseri and Alen Alexandarian. Bayesian Approach to Inverse Problems. North Carolina State University, Raleigh NC. Presented to NSF Research Training Group in Randomized Numerical Analysis. October 2018.

• Seminar Talk. Isaac Sunseri. The Kryptos Cipher. North Carolina State University, Raleigh NC. Presented in the Undergraduate Research Seminar. May 2017.

Technical Reports

• Isaac Sunseri and Alen Alexandarian. On marginals of Gaussian random vectors. North Carolina State University, 2018. [ view pdf ]