teaching

My approach to teaching

As a teacher, my overarching aim is to foster students’ understanding and appreciation of computational methods, seen not only as a valuable asset to be applied in a professional context, but as a powerful analytical framework with high educational value.

Advising. My mentoring experience has taught me that the main role of a good mentor is to stimulate students to pursue their own interests, while at the same time steering them away from potentially frustrating dead ends. As part of my teaching philosophy, I tailor and individualize my advising style to reinforce students’ strengths and address their weaknesses.

Classroom Teaching. I believe in the importance of striking a balance between giving students a solid theoretical basis and encouraging them to engage directly with problems requiring practical application. In addition to traditional lectures and textbooks, my classes seek to expose students to a range of carefully selected primary literature in order to promote a deeper understanding of the subject and a better sense of how scientific ideas take shape and evolve.

One of my goals as an instructor is to cultivate effective communication skills in my students—skills that are increasingly important as the scientific community becomes more interdisciplinary and international. Towards this end, I encourage students to give oral presentations so that they can receive feedback from both their peers and me. As a strategy to foster classroom discussions, I have students take turns in leading paper discussions and ask questions to the audience in almost all my classes.

From a computational perspective, I believe in the importance of “opening the black box”, by teaching students the importance of understanding how commonly available packages work. Some of my assignments or class projects involve the implementation of fundamental bioinformatics or machine learning algorithms from scratch, an exercise that empowers students to take full control over the computational aspects of a project and to become better informed users of already developed tools.

Courses taught at the University of Nebraska at Omaha:

-ACMP 4000: Data Analysis and Machine Learning

-BIOI 1000: Introduction to Bioinformatics

-BIOI 2000: Foundations of Bioinformatics

-BIOI 3500: Advanced Bioinformatics Programming

-BMI 8300: Public Health Genomics (new course)

-BMI 8850: Biomedicine for the Non Medical Professional (new course)

-BMI 8400: Linear Algebra for Advanced Computing and AI (new course)

-HONR 3040: Exploring Complex Systems with Computer Programs (new course)