Carnegie Mellon University
5000 Forbes Ave
Pittsburgh, PA 15213
+1 (954) 557-6541
alejands@andrew.cmu.edu
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Alejandro Sanchez

Physics Graduate Student

  • High Energy Physics
  • CMS Experiment
  • CERN

About me

Hi! My name is Alejandro Sanchez and I am a graduate student at Carnegie Mellon University. My research interests are in high energy particle physics. I currently work for the Compact Muon Solenoid (CMS) experiment at CERN, located in Geneva, Switzerland. At CERN, we use the Large Hadron Collider (LHC) to accelerate protons very close to the speed of light and collide them, creating new particles.
My programming skills include Python, C++, Java, Bash, ROOT, LaTeX, and knowledge of the CMS Software (CMSSW) framework.

Research Experience

PREVIOUS PROJECTS

GRADUATE RESEARCH ASSISTANT

ADVISOR: MANFRED PAULINI, PHYSICS DEPARTMENT, CARNEGIE MELLON UNIVERSITY
CMS EXPERIMENT
AUGUST 2017 - PRESENT
  • Collaborating as a member of the Compact Muon Solenoid (CMS) experiment at CERN in Geneva, Switzerland to search for new physics, including supersymmetry and dark matter
  • Developing a deep learning multi-classifier as a novel way to discriminate between particles using an image-based, end-to-end approach
  • Maintaining, debugging, and developing the data quality monitoring (DQM) software, plots, and GUI for the CMS electromagnetic calorimeter (ECAL) subdetector
  • Stationed at CERN during summer 2018, collaborating with CMS ECAL Detector Performance Group (DPG) in person

UNDERGRADUATE RESEARCH ASSISTANT

ADVISOR: HARRISON PROSPER, PHYSICS DEPARTMENT, FLORIDA STATE UNIVERSITY
CMS EXPERIMENT
JANUARY 2015 - MAY 2017
  • Implementing deep machine learning methods used to facilitate the training of handwriting recognition neural networks to generate discriminants of high energy particle collider events from raw data without having to create clever variables while retaining accuracy
  • Studied neural networks and various training techniques to look for more efficient and accurate forms of particle collider data analysis
  • Wrote and tested my own version of a deep Bayesian neural network code from scratch and generalized to allow the customization of the number of hidden layers, input weights, and potential of implementing GPU parallelization for computationally intensive sections

Education

ACADEMIC CAREER

PH.D. IN EXPERIMENTAL HIGH ENERGY PHYSICS

CARNEGIE MELLON UNIVERSITY
EXPECTED GRADUATION DATE: MAY 2022

Potential Thesis Title: Search for Dark Matter at the Large Hadron Collider in a Model Independent Approach using Image-Based Deep Learning Classifiers

BACHELOR OF SCIENCE IN PHYSICS WITH HONORS, MINOR IN MATHEMATICS

FLORIDA STATE UNIVERSITY
GRADUATED IN MAY 2017

Honors in the Major Thesis: Exploration of Deep Learning Methods for Vector Boson Fusion Event Discrimination
Dean’s List: 5 Semesters; Overall GPA: 3.691/4.0; Cum Laude