Jorge Silveyra

Assistant Professor, Computer Science
Mathematics & Computer Science
Trumbower Science Building


  • Ph.D., M.S., University of North Texas
  • B.A., Universidad Autonoma del Estado de Mexico

Teaching Interests

I love to teach a wide range of computer science classes. I have had the pleasure of teaching all levels of computer science classes and assisting students in their REU (capstone) projects. I also have taught multiple elective classes such as Natural Language Processing and Modeling and Simulation. I love to introduce my students to new and challenging topics in computer science.

I am also interested in creating courses that can help non-computer scientists to learn computer science concepts in a friendly and useful way. My goal is to teach them enough information for them to be able to create their own programs and understand the possibilities and limitations of using computers. For example, one of the ways that we have used data analytics in class is to figure out the best times to tweet. Another exercise was to analyze Star Wars movie databases to determine the amount of screen time that characters get in each movie and which ones appear less frequently.

Research, Scholarship or Creative/Artistic Interests

My research predominantly involves computational epidemiology, modeling and simulation; and computational immunology. These are multidisciplinary fields that use computer science, mathematics, public health, chemistry and biochemistry. Specifically, I am interested in investigating how much diseases are affected by a person’s physiology versus by their social and behavioral characteristics. In other words, I am interested in knowing the role an individual’s immune system and the ways that behaviors impact how an epidemic spreads.

Lately, I have been expanding my research to use natural language processing (computers that read human language) and machine learning (the science of getting computers to act without being programmed) to address issues related to epidemiology and political science. These types of programs look for patterns in text that might not be obvious to humans reading text.

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