Teaching

Graduate tutoring (volunteer) for first-year Master’s students in the Big Data program, covering inferential statistics and optimization.

Inferential Statistics

• Hypothesis testing
• Confidence intervals
• Likelihood-based inference
• Asymptotic results

Optimization

• Unconstrained and constrained optimization
• Lagrange multipliers
• KKT conditions
• Gradient-based methods
• Convex optimization

Delivered secondary-level instruction in mathematics and physics, with emphasis on conceptual understanding, analytical reasoning, and systematic problem-solving.

Mathematics

• Probability and combinatorics
• Set theory and proof techniques
• Polynomial, rational, and homographic functions
• Limits, continuity, and differentiability
• Arithmetic and geometric sequences
• Complex numbers

Physics

• Kinematics and Newtonian dynamics
• Free fall and motion under gravity
• Oscillations and wave phenomena
• Electrostatics and electric circuits
• AC and RLC circuits
• Thermodynamics