Teaching & Mentorship
My teaching emphasizes hands-on learning, reproducible practices, and computational thinking across diverse student populations. Five years of production software engineering before the PhD inform how I connect classroom work to real deployment.
Teaching Interests
Core Courses: Machine Learning • Computer Vision • Data Science & Analytics • Human Factors Engineering • Industrial & Systems Engineering • Programming for Engineers (Python)
Advanced Topics: Geometry-Informed Machine Learning • Computer Vision for Safety-Critical Systems • Intelligent Transportation Systems • Human-Automation Interaction
Workshops & Tutorials
Certified Instructor with The Carpentries, teaching foundational coding and data science skills to researchers across disciplines.
- Software Carpentry: Python Programming
- Software Carpentry: CZI Accelerate Precision Health
- Software Carpentry: Python for Researchers
- Machine Learning Workshop
- WOC Code Summer Boot Camp: Introduction to Programming
Conference Presentations
- A Geometry-Informed Computer Vision Method for Detecting and Examining Overtaking Vehicles from a Bicycle
- Hybrid Physics-Data Modeling for Sustainable Transportation: Bridging Classical Models and Modern AI
- A Comparative Analysis of Acceleration and Deceleration Profiles for Aggressive Driving Styles and Fuel Economy Test Cycles
- An Autonomous Driving System: Dedicated Vehicle for People with ASD and their Caregivers
Invited Talks & Guest Lectures
- DigitwiML: Open-Source Digital Twin of C. elegans in Space (NASA SpaceApps 2023 Experience)
- Autonomous Driving Systems for Accessibility
Teaching Assistantships (Undergraduate)
- Artificial Intelligence
- Probability and Queuing Theory
- Transforms and Partial Differential Equations
- Systems Software Laboratory
Mentorship & Advising
Instructor and mentor with Women of Color Code and The Carpentries, supporting students from diverse backgrounds in computing and engineering.