Farshad Kamalinejad

I was born on a cold winter night in 1993 in Tehran, Iran. As a kid, I loved painting, playing, and staring at the stars. Even now, I still feel a deep sense of peace under clear, dark skies.

My interest in astronomy started in high school. I earned the silver medal in Iran’s 6th National Astronomy and Astrophysics Olympiad. The competition covered both theory and observation, with topics like celestial mechanics, astrophysics, and cosmology.

For my bachelor’s degree, I studied chemical engineering, but I couldn’t let go of my passion for astronomy. That’s why I decided to minor in physics. Later in my master’s, I shifted my focus to cosmology.

Over the last six years, I’ve been working on my Ph.D. in Gainesville, Florida, with Dr. Zachary Slepian. My research has been about how the neutrino mass impacts galaxy summary statistics, focusing on the two-point and three-point correlation functions (2PCF and 3PCF) and their Fourier space analogue, the power spectrum and the bispectrum.

My research primarily focuses on the large-scale structure (LSS) of the Universe, with a particular emphasis on higher-order statistics. Using galaxy surveys, I explore fundamental physics, including the nature of dark matter and dark energy, neutrino mass, and the laws of gravity. My work integrates mathematical modeling and data analysis techniques to uncover the wealth of information contained in the LSS.

During my Ph.D., I have been modeling the effects of massive neutrinos on galaxy summary statistics (power spectrum and bispectrum) and developing methods to detect their signatures in observational data. I conduct this research as part of the Dark Energy Spectroscopic Instrument (DESI) collaboration.

Beyond theoretical modeling, I have dedicated significant effort to Fisher forecasting, providing insights for future surveys such as DESI, Roman, and SPHEREx. My broader interests in LSS also include reconstruction techniques, Baryon Acoustic Oscillation (BAO) measurements, and the cross-correlation of the LSS with gravitational lensing.

In addition to cosmology, I have a strong interest in the application of machine learning, deep learning, and computer vision in cosmological research. I earned a Machine Learning (ECE) Certificate from the University of Florida, during which I implemented various machine learning and deep learning algorithms such as neutral networks, data preprocessing, clustering and ... to do either classification and regression tasks.

I am particularly interested in applying these techniques to cosmology, especially in using neural networks for simulation-based inference (SBI), parameter estimation, and reconstruction.

I also enjoy rock music, Persian pop, and traditional Persian music. I also do astrophotography whenever I find myself under a clear dark sky. 

My personal website is here and you can reach me at f.kamalinejad@ufl.edu