Group
During my academic journey, I had the pleasure of mentoring a number of bright and curious undergraduate students on research projects. I’m always looking for new students working on projects in cosmology, galaxy formation, and machine learning. If you’re a student at American University and interested in research, don’t hesitate to reach out to me.
Current Students
Alexandra Wells
Alex is investigating how accurately we can determine the properties of dark energy and cosmic structure growth from measurements of the distribution of galaxies and gravitational lensing. For this work, we specifically focus on the measurements on small, so-called non-linear scales. She analyses simulated “mock” observations constructed from cosmological simulations and tests how well we can recover the input values.
Former Students
Alexandra Doytcheva
Alex joined the group as a first-year student. Initially, we investigated the relation between supermassive black holes and galaxies in the IllustrisTNG simulation. Later, we explored the use of control variates to increase the statistical precision of galaxy clustering predictions from hydrodynamical simulations. This work resulted in a paper with Alex and Filomela as the first two authors.
Filomela Gerou
Filomela explored the relationship between galaxies and dark matter halos using neural networks. We investigated to what extend different halo properties can explain the galaxy assembly bias effect. Filomela also contributed to the project exploring the use of control variates to increase the predictive power of hydrodynamical simulations of galaxy formation.
Gilad Pifko
Gilad worked with data from the Kilo-Degree Survey, measuring the gravitational lensing effect, a prediction of Einstein’s theory of general relativity. He investigated whether the amount of dark matter surrounding galaxies depends on their size. He was able to confirm theoretical predictions from earlier studies in the literature.
Simon Wu
During our project, under the guidance of Prof. Lange, I focused on quantifying the contribution of dark matter subhalos to the gravitational lensing signal using the TNG300 hydrodynamical simulation. We explored the impact of satellite galaxies on galaxy-galaxy lensing, comparing different modeling approaches to assess subhalo contributions to the overall lensing effect. It was a great experience working on this interesting project with Prof. Lange and I really learned a lot!
Garv Shah
Garv studied the use of different neural network architectures for problems in Bayesian inference. Specifically, he investigated the performance of the nautilus code when using advanced networks he built with TensorFlow library. Ultimately, his results informed many design decisions for the widely-used nautilus code.
Juliana Karp
I investigated the causes of anisotropic satellite galaxy quenching (ASGQ) in two state-of-the-art cosmological simulations of galaxy formation, UniverseMachine and IllustrisTNG. I showed that ASGQ is probably not caused by outflows from the central galaxy’s supermassive black hole, but rather likely a natural consequence of hierarchical structure formation.