Daniel Gaytán-Villarreal

¡Hola! – I’m Daniel, a Physics PhD Candidate at Carnegie Mellon University advised by Prof. Matteo Cremonesi. My research focuses on developing cutting-edge Machine Learning (ML) models, particularly Graph Neural Networks (GNN), to reconstruct particles in particle detector simulations and to search for Dark Matter (DM) at the Large Hadron Collider (LHC) using the Compact Muon Solenoid (CMS) experiment at the European Organization for Nuclear Research, more popularly known as CERN.

A little bit about me

I was born and raised in Hermosillo, the capital of the state of Sonora, México, a beautiful city in the middle of the Sonoran dessert (it can get a bit hot during the summer, up to 45°C or 113°F!). I attended my state university, the University of Sonora, where I completed my Bachelors in Physics. Even though it’s a relatively small institution, it provided me with great opportunities and allowed me to meet great mentors who helped me find my career path by introducing me to the fields of particle physics and ML!

After graduating, I leveraged the research skills I had learned to get a job as a Data & AI Consultant at Deloitte, one of the “Big 4”, a nickname used to refer to the four biggest consultancy companies in the world. As a consultant, I applied data science, analysis & engineering and designed ML models to solve real-world problems for various clients. This was an amazing experience for which I’m very thankful, as it taught me a lot of valuable industry skills and set me to a great start in my data career!

After a year and a half of working in industry, I decided to go to grad school to pursue higher studies in the fields I’m most passionate about: particle physics and ML. I was very fortunate to get accepted at Carnegie Mellon University, a great place to do physics and one of the best places in the world for ML!