Yuliana Zamora

Yuliana (Yulie) Zamora is completing her PhD in Computer Science at the University of Chicago. Yulie is a 2017 fellow at the CERES Center of Unstoppable Computing at the University of Chicago and a National Physical Science Consortium (NPSC) graduate fellow.

Yulie has worked at the Los Alamos National Laboratory and interned at Argonne National Laboratory. At Los Alamos National Laboratory, she optimized the Higrad Firetec code used for simulating wildland fires and other atmospheric physics for some of the top high performance computing systems. At Argonne National Laboratory, she worked at the intersection of high performance computing and machine learning. She has worked on projects ranging from performance prediction on NVIDIA GPUs to machine learning surrogate models for scientific applications.

Yulie developed and taught an Introduction to Computer Science course for incoming University of Chicago students. She incorporated many of the basic concepts of parallel computing fundamentals into the material. The course was so successful, she was asked to teach it again and again. Wanting to gain more teaching experience, she volunteered for a teaching assistant position for an Advanced Distributed Systems course at the University of Chicago.

Yulie’s Bachelors degree is in Civil Engineering from Cornell University. She finished her Masters of Computer Science from University of Chicago and will soon complete her PhD in Computer Science, also from the University of Chicago.