Teaching
Courses and Educational Interests
My teaching emphasizes mathematical foundations, computational implementation, and the connection between theory and scientific practice.
Current Teaching Areas
- Quantum Computing
- Introduction to Computation and Simulation Analysis
- Discrete Structures
- Scientific Computing
- Computational Modeling
- Artificial Intelligence and Machine Learning
- Quantum Information Science
Quantum Computing
My quantum computing teaching integrates linear algebra, quantum mechanics, probability, and computer science with computational exercises in Python and Qiskit. Topics include quantum states, tensor products, measurement, entanglement, quantum gates and circuits, Grover’s search algorithm, and Shor’s factoring algorithm.
Computational Learning
Across my courses, I emphasize reproducible computational practice, numerical reasoning, and the ability to translate a mathematical model into working code. Depending on the course, students may work with:
- Python
- NumPy
- Jupyter Notebooks
- Qiskit
- MATLAB
- Numerical simulation tools
Educational Goal
Students should leave a course able to do more than repeat definitions or run provided code. They should be able to explain the underlying model, identify assumptions, interpret computational output, and evaluate whether a result is scientifically reasonable.