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Bruno Villasenor

Telephone: (+1) 831 212 3832
Email: bvillasen@gmail.com, brvillas@ucsc.edu
GitHub: https://github.com/bvillasen


University of California, Santa Cruz                            ( August 2016 - Expected Jun 2022 )
Master of Science, Expected Ph.D. in Astronomy and Astrophysics
Department of Astronomy and Astrophysics.
Advisor: Brant Robertson

Universidad Nacional Autonoma de Mexico, UNAM            ( August 2010 - June 2016 )
Bachelor of Science in Physics. Final Grade: 9.4 / 10
Thesis: “On the kinematics of the stellar component of satellite galaxies as tracer of their dark matter distribution”
Advisor: Vladimir Avila-Ress

Technical Skills

Software         Python, C/C++, CUDA, MPI, OpenMP, Julia

Work Experience

Summer Intern at Fermilab,   Fermi National Lab,    Illinois,    Summer 2010
Fellow at the Internship for Physics Majors at Fermi National Accelerator Laboratory. Developed software to analyze events from the Tevatron collider and applied a new method to select Higgs Boson events from the WW decay, this algorithm was latter applied in the Higgs detection pipeline.
Advisor: Eric James.

Summer Intern at Fermilab,   Fermi National Lab,   Illinois,   Summer 2011
Received a “come back” offer for further development of data analysis work done during the previous summer for the Higgs boson detection. Analyzed the Higgs Thrust from monte-carlo simulations and optimized the selection criteria for Higgs events from the Tevatron collider.
Advisor: Eric James & Sergo Jindariani

Teaching Experience

Teacher Assistant: Intro. to Scientific Computing, Astronomy Dept. UCSC. Winter 2016, Spring 2017
Teacher Assistant: Computational Physics, 7th semester, UNAM. Semester: 2016-1.
Teacher Assistant: Electromagnetism I, 4th semester, UNAM. Semester: 2012-2.
Teacher Assistant: Scientific Computing Using GPU’s, Advanced, UNAM. Semester: 2012-1.
Teacher Assistant: Computation, 1st semester, UNAM. Semesters: 2010-2, 2011-1, 2011-2, 2013-1.

Mentoring Experience

Python Bootcamp Instructor for the Lamat 2020 Participants, UCSC, January 2020:
Participated as the instructor for the 2020 Python Bootcamp. A one week long intensive program where the participants of the 2020 Lamat summer program learned the basics of scientific programming using Python so that they will be prepared for conducting scientific research in astrophysics during the summer.

Graduate Student Instructor for Introduction to Research (ASTR 9), UCSC, January 2019 - June 2019:
Mentored a team of four first year undergraduate students through an astronomy research project that I designed. We ran and analyzed a set of dark matter cosmological simulations, located the dark matter halos and studied their density profile.

Honors and Awards

  • Excellence in Teaching: UCSC Astronomy Department Award Recipient, 2017.
  • Second place at the III Mexican Olympiad of Astronomy, INAOE, Mexico, 2007.
  • Participant at the XXXVII International Physics Olympiad, Iran, 2007.
  • Participant at the XXXVI International Physics Olympiad, Singapore, 2006.
  • Silver medal at the X Ibero-American Physics Olympiad, Uruguay, 2005.
  • First place at the XVI Mexican Physics Olympiad, Merida, Mexico, 2005.
  • Second place at the XV Mexican Physics Olympiad, Zacatecas, Mexico, 2004.


B. Villaseñor, R. Zamora-Zamora, D. Bernal, and V. Romero-Rochín, ”Quantum turbulence by vortex stirring in a spinor Bose-Einstein condensate”, 2014, Phys. Rev. A 89, 033611.

Additional Education

  • First Mexican AstroCosmoStatistics School, Guanajuato, Mexico, 2016
  • Sixth Mexican Summer School of Nuclear Physics, ICN, UNAM, Mexico, 2010.
  • Mexican delegate at the 2005 National Youth Science Camp, West Virginia, U.S. 2005.

   Online Courses Taken:

  • Algorithms and Data Structures, Microsoft. (EdX).
  • Learn to Program in Java, Microsoft. (EdX).
  • Object Oriented Programming in Java, Microsoft. (EdX).
  • Designing a Technical Solution, Microsoft. (EdX).
  • Algorithms: Design and Analysis, Part 1, Coursera. (Stanford).
  • Algorithms: Design and Analysis, Part 2, Coursera. (Stanford).
  • Heterogeneous Parallel Programming, Coursera. (University of Illinois).
  • Intro to Parallel programming, Udacity. (NVIDIA).
  • Machine Learning, Coursera. (Stanford).
  • Machine Learning Foundations: A Case Study Approach, (U Washington).
  • Coding the Matrix: Linear Algebra through CS Applications, Coursera.
  • Differential Equations in Action, Udacity.


Main projects that I had worked on: