Born and raised in Syria, my family and I fled to Lebanon in 2012 due to the Syrian Civil War. After graduating high school in Lebanon, I moved to the U.S. for college.

I am currently a fourth-year physics Ph.D. candidate at Stanford University. I am co-advised by Prof. Susan Clark and Prof. Chao-Lin Kuo at the Kavli Institute for Particle Astrophysics and Cosmology (KIPAC) at Stanford. The focus of my Ph.D. work is a data-driven approach to modeling the three-dimensional structure of magnetic fields and dust in our Galaxy. This is an important problem with several applications in the fields of astrophysics and cosmology that I tackle by developing and using different techniques in computer vision, signal processing, and statistics.



Below is a list of some of the projects I have worked on or am currently working on, including links to publications, codes, visualizations, posters, etc.

Data Science Internship

Developed machine learning models for studying dose adjustment patterns and their effects on IVF outcomes and statistical tests to alert clinics when one of their doctors performs better or worse than the average doctor on different metrics.

Imprints of Local Bubble and Dust Complexity on Magnetic Fields

Modeled the effects of the Local Bubble geometry and 3D dust complexity on magnetic field tracers.

Filamentary Dust Polarization and Neutral Hydrogen Morphology

Improved computer vision algorithms to study the relationships between magnetic fields, neutral hydrogen, and dust.

Spherical Harmonic Convolutional Hough Transform

Developed algorithm in Python, which achieved 3000x runtime speedup and 5x decrease in memory consumption over previous algorithm.

Cross-correlating CMB & ISM Datasets for Characterizing Dust

Developed statistical tests in Python and MATLAB for quantifying the dust contribution of different components and measuring the dust properties in a certain sky patch.

Magnetic Misalignment of Interstellar Dust Filaments

Developed one of the algorithms used in this analysis, fixing spurious correlation in previous algorithm, and made figures confirming one of the conclusions of this analysis.

Bayesian Inference on Vansyngel Dust Model

Implemented the Vansyngel dust model in Python and performed Markov Chain Monte Carlo methods to obtain the parameters' posteriors.

Machine Learning for Stochastic Generation of Observed Galaxy Properties

Developed a conditional Wasserstein generative adversarial neural network with gradient penalty (cWGAN-GP) in PyTorch to generate observed galaxy properties in wide-field surveys.

Machine Learning for Modeling the Transfer Function of Galaxy Detection

Developed a deep learning model for predicting a galaxy's detection by the Dark Energy Survey (DES) given that galaxy's properties and the observing conditions.

Machine Learning to Search for 2-$\nu$ Double-$\beta$ Decay of $^{136}$Xe to the Excited State of $^{136}$Ba

Developed a Long Short-Term Memory neural network in Keras in Python to search for this decay in EXO-200 data.

Machine Learning for Heavy-Flavor Jet Classification at RHIC

Developed a model made of a concatenation of Long Short-Term Memory (LSTM) and fully-connected networks on a list of discriminators to classify charm, bottom, and light jets.

Machine Learning for Centrality Determination with the STAR EPD

Developed a model to identify the centrality of a collision, based on which of the detector tiles are hit during a given collision.

Performance Analysis of the STAR Event Plane Detector

Analyzed data collected by the detector that was installed for the RHIC 2017 run, colliding protons and gold ions, to optimize its final design.

Analysis of R&D Test Setups for the nEXO Experiment

Analyzed data we collected from test setups we built and operated to understand complicated electron emission phenomena when high voltage is applied in liquid xenon.

Cosmic Ray & Radioactive Source Testing of the Event Plane Detector

Performed calculations, digitized signal (as shown in diagram), and quantified the quality and uniformity of the different sectors of the detector.


Dust grains are organized into filaments that are well aligned with the magnetic field. Therefore, the light they emit is polarized perpendicular to the magnetic field orientation. In the following figure, I run two different computer vision algorithms on toy dust filaments to show their emitted polarization patterns under the assumption of perfect alignment between those filaments and the magnetic field.