cv

Bachelor of Science in Physics

• studying focus
  • Theoretical statistical physics and atomic physics.
  • Astronomy and computational physics (1.3 in german notes).

Master of Science in Physics

• studying focus
  • Astronomy and Astrophysics.
  • Machine learning and computational physics.

Observation and data analysis of a ground based 70cm telescope

• In this small project, I have worked with a ground based 70cm telescope at the state Observatory in the main aim of learning the observational techniques, operational precedure of a ground based telescope, and also the data reduction and analysis of the raw data. I have successfully observed several open clusters and variable stars, and reduced the raw data to obtain calibrated photometric measurements.

Trapping and cooling of Rb atoms to the Doppler limit using a self built MOT trap

• In this small project, we built a 3D Magneto-Optical Trap (MOT) from scratch and finetuned it to cool and trap Rubidium (Rb) atoms to the Doppler limit. I was majorly responsible for the optical setup and alignment of the laser beams, and gained hands-on expereience with experimental techniques in optics and atomic physics.

Bachelor Thesis, Spatially resolved Balmer decrement of galaxies

• In my bachelor thesis, I have worked in the Data Science group with Dr. Ivelina Momcheva, under the formal supervision of Prof. Dr. Hans Walter Rix. I've woked with slitless spectroscopy data from archival HST grism data of the CLEAR survey and the ongoing JWST NIRISS pure parallel program Outthere. By utilizing the grism emission line maps, I have conducted a spatially resolved analysis of Balmer decrements on indivcidual galaxies, instead of the traditional stacked measurement. This work provided a better constraints on the scatter of the spatial dust attenuation witin galaxies at cosmic noon, and the correlation of dust central concentration as a function of galaxy mass.

A multi-modal self-supervised learning approach to JWST imaging and spectroscopic data

• In this ongoing project, I further extend my experience with observational data from JWST NIRISS/NIRCam, and experiment with noval machine learning technique to imaging and spectroscopic data. Instead of traditional supervised learning, we are exploring of adopting self-supervised learning (SSL) techniques to moderate sized JWST datasets, and experiments with multi-modal learning via CLIP (Contrastive Language-Image Pre-Training) - like approach to fuse imaging and spectrocopy into one shared representation. This project is aimed to provide a new way of infering physical properties of high z galaxies with high tolerance on missing data.

• Scholarship of the Disney Aspire Program