Primary research areas:

Computational astrophysics
Time domain
Higher dimensions, geometry of spacetime
Cosmology and the evolution of galaxies

I'm a lot interested in the dynamical history of our Milky Way - especially halo formation and the disruption of globular clusters and satellites. Also, I have a general interest in the capabilities arising from high-performance compputing, machine-learning algorithms and astrostatistical inference.

This has led me to various topics, among them the development of a general-relativistic raytracer, reverberation mapping of quasars in the SDSS survey, the classification of variable sources in large all-sky surveys and Milky Way structure and dynamics.

My current work is focused on understanding the spatial Galactic halo density and kinematics by getting high-precision velocities for RR Lyrae stars in the Milky Way. This is based on my preceding work that led to a catalog of variable sources for the the Pan-STARRS survey, among them more then 44,400 RR Lyrae stars covering 3/4 of the sky and extending to as much as 130 kpc.

Me giving a brief "science teaser" about Machine-Learning for next-(and our)generation Astronomy.

Me talking as part of the AAS Journal Author Series about further implications for RR Lyrae star observations with LSST as presented in the publication Hernitschek+2022 (N. Hernitschek, K. G. Stassun, The Impact of Observing Strategy on Reliable Classification of Standard Candle Stars: Detection of Amplitude, Period, and Phase Modulation (Blazhko Effect) of RR Lyrae Stars with LSST, ApJS 258, 2 (2022) [AJ] [arXiv:2109.13212 [astro-ph.SR]]).

Special expertises:

statistical methodology in astronomy:

3D modeling:

programming skills: