Teaching Experience

2003–present

I have taught mathematics courses for nearly two decades across multiple institutions, including Universidad Central de Venezuela (UCV), Universidad Simón Bolívar (USB), Bowling Green State University (BGSU), Oregon State University (OSU), Worcester Polytechnic Institute (WPI), Yeshiva University (YU), and currently CUNY—New York City College of Technology (City Tech). I have experience with small, medium, and large classes, including hybrid and fully online teaching during the pandemic.

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Teaching at City Tech (CUNY)

Since joining City Tech in Fall 2022 as a tenure-track Assistant Professor, I have taught courses including MAT 1275, MAT 1372 (Introduction to Statistics), MAT 1375 (Precalculus), MAT 2440 (Discrete Structures), Introduction to Computer Science, and independent studies. My teaching emphasizes conceptual understanding, mathematical reasoning, and careful implementation: students work from paper-and-pencil derivations to computational experiments and analysis.

MAT 2440 (Discrete Structures) is one of my favorite courses to teach because it gives students a structured way to build valid arguments and practice different proof techniques. Topics such as the Euclidean algorithm, solving linear congruences, and base conversion are especially engaging and offer concrete opportunities to connect theory to computation. I also design collaborative activities so students can learn from one another and develop confidence working as a team.

• Numerical methods foundations: Students develop finite-difference approximations (forward and central differences) and study classical root-finding methods such as bisection and Newton’s method. I have students carry out iterations by hand first so they understand the mechanics, then implement the methods and compare accuracy and order of convergence.
• Machine learning workflows and applications: Students build and evaluate machine learning frameworks using publicly available LiDAR/3D point cloud data. Projects include exploratory data analysis and visualization, confusion matrices, and PCA (e.g., plotting data in the first two principal components). Students also gain introductory exposure to PyTorch, neural networks, and autoencoders.
• Independent studies and research seminar mentoring (City Tech–BYU collaboration): I have supervised independent studies connected to the City Tech–BYU Data Science workshops and seminar series, mentoring students through research-style workflows (problem formulation, data preparation, exploratory analysis, modeling, evaluation, and communication of results). Student outcomes have included posters, talks, and written reports.
• From fundamentals to full pipelines: Students progress from basic programming concepts (lists, nested loops, algorithmic thinking) to implementing and comparing numerical methods and building complete ML pipelines—connecting foundational algorithms (e.g., bisection vs. Newton’s method) to modern machine learning practice.

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Courses taught (selected)