Teaching through applied problems
I bring research and enterprise software experience into programming, data science, AI, and information systems instruction.
My teaching résumé documents more than five years of experience as an Instructor and Teaching Assistant, alongside more than eighteen years in software and IT.
Academia Resume (PDF)Programming & software
Python, Java, C/C++, SQL, application development, and the software development lifecycle.
Data science & AI
Data preparation, visualization, regression, model evaluation, computer vision, and deep learning.
Information systems
Databases, systems analysis, enterprise architecture, and business process modeling.
Robotics & research
ROS 2, sensor integration, experimental design, reproducible analysis, and technical writing.
Sample lesson · Validate sensor data with Python
An illustrative 30-minute activity for students who know lists and functions. The goal is to detect missing and implausible temperature readings, then calculate a mean from valid observations.
- Inspect the readings and identify which entries should be reviewed.
- Write a function that accepts numeric readings between 10°C and 40°C for this exercise.
- Calculate the mean and explain how filtering changes the result.
- Discuss why limits should come from the measurement context.
readings = [22.1, None, 22.4, 90.0, 21.8]
def valid_temperature(value):
return isinstance(value, (int, float)) and 10 <= value <= 40
valid = [value for value in readings if valid_temperature(value)]
mean = sum(valid) / len(valid) if valid else None
print(mean)Instructor notes and answer
The valid values are 22.1, 22.4, and 21.8°C. Their mean is 22.1°C. Ask students to test an empty list and an entirely invalid list, then explain why an unusual reading may warrant investigation before exclusion.