I've always been drawn to teaching and mentoring. At Universidad de Los Andes I coordinate and teach courses in applied statistics. At Cornell University, I've contributed as both a guest lecturer and a teaching assistant across graduate and undergraduate courses.
I've also collaborated with DeepLearning.AI as a curriculum author, helping design engaging short and full-length courses, and AI specializations tailored for a global audience.
Here are some of the roles I've held:
Lead Teaching Assistant and Guest Lecturer
An undergraduate course on statistics and data analysis tailored for engineering students. The course introduces core concepts in probability, statistical inference and regression.
Lead Teaching Assistant
An undergraduate course that introduces students to engineering and business economics, with a focus on evaluating and comparing investment alternatives.
Lead Teaching Assistant and Guest Lecturer
A graduate-level course on discrete choice modeling, covering foundational and advanced techniques for analyzing individual decision-making. Topics include the conditional logit, probit, latent class, and mixed logit models, as well as methods for simulation-based inference and Bayesian estimation.
Curriculum Author
An online beginner-level course on Python that introduces students to core programming concepts while demonstrating how to integrate AI tools for data-related tasks. The course also emphasizes using AI for writing, debugging, and understanding code, helping learners develop both coding skills and AI-assisted workflows.
Curriculum Author
A beginner-level online course focused on understanding text embeddings and their practical applications, using Vertex AI from Google Cloud for hands-on implementation.
Curriculum Author
An intermediate-level online specialization consisting of three courses focused on generative adversarial networks (GANs), covering both theoretical foundations and practical implementation.
Curriculum Author
An intermediate-level online specialization consisting of four courses on natural language processing (NLP), covering topics from basic vector space models to advanced transformer architectures.
Professor
This undergraduate course provides an introduction to probability and statistics for data-driven decision-making under uncertainty. Students learn core probability concepts and develop practical statistical skills to describe, analyze, and interpret data. Emphasis is placed on understanding randomness and variability, modeling risk and uncertainty, and applying quantitative reasoning to real-world problems using data and basic probabilistic and statistical models.
Professor
This graduate-level course provides an introduction to statistical Design of Experiments (DOE) for engineering and applied research. Students learn how to design efficient experiments, develop appropriate statistical models, analyze experimental data, and present results clearly and rigorously. By the end of the course, students will be able to plan, execute, analyze, and communicate well-founded experimental studies in engineering and other scientific domains.