My teaching philosophy is rooted in the belief that education is a transformative process that empowers students to become critical thinkers, engaged citizens, and lifelong learners. I emphasize active learning, encouraging students to challenge traditional paradigms, apply practical skills, and cultivate intellectual curiosity. Through hands-on, project-based approaches in courses like EW200 (Introduction to Programming and Design), EW202 (Principles of Mechatronics), and EW453 (Introduction to Computer Vision), I integrate emerging technologies and real-world applications, such as AI-powered cameras and advanced 3D reconstruction vision sensors, to connect theoretical concepts with practical outcomes. Recognizing the challenges of active learning, I provide structured guidance and incremental opportunities to support diverse student readiness and ensure technical proficiency. Mentorship plays a central role in my teaching, as I guide students through interdisciplinary research projects, fostering skills in experimental design, data analysis, and scientific communication. Beyond the classroom, I am committed to expanding access to STEM education through outreach initiatives that inspire and diversify future engineers, creating an inclusive and dynamic learning environment that addresses both student development and societal needs.
Course Coordinator for Introduction to Computer Vision (EW453):
Fall 2025: Taught sections S3321 and S5541 (2-2-3), sole instructor.
Spring 2024: Taught sections S3321 and S5541 (2-2-3), sole instructor.
Fall 2024: Taught section S3321 (2-2-3), sole instructor.
Spring 2023: Coordinated 3 sections, taught section S4341 (2-2-3), 2 instructors.
Fall 2023: Taught sections S3321 and S5541 (2-2-3).
Lecturer for Introduction to Programming and Design (EW200):
Fall 2024: Taught section S5541 (3-2-4).
Fall 2023: Taught sections S3321 and S5541 (3-2-4).
Fall 2022: Taught section S4341 (3-2-4).
Fall 2021: Taught sections S3321 and S4341 (2-2-3).
Lecturer for Principles of Mechatronics (EW202):
Spring 2023: Taught section S6541 (2-2-3).
Spring 2022: Taught sections S3321 and S4341 (2-2-3).
Spring 2021: Taught sections S3321 and S4341 (2-2-3).
Co-Lecturer for Autonomous Marine Systems (EW282E):
Spring 2021: Taught section S5621 (2-0-1).
Note: (R-L-C) in course listings stands for Recitation Hours, Lab Hours, and Credit Hours.
Advisor for Midshipmen Independent Research (EW494, EW495):
Fall 2025, Spring 2024, Fall 2024, Fall 2023, Fall 2022: Supervised various independent research projects.
Advisor for Engineering Design Methods (EW401, EW404):
Fall 2024, Spring 2024, Spring 2023, Fall 2023, Spring 2022, Fall 2022, Fall 2021: Managed capstone and engineering design projects.
Fall 2019, MCEN 6848, independent study: Lecturer for Algorithms of Collective Motion
Fall 2018, ASEN 5014, Guest lecturer for Linear Control Systems on the topics of Linear Quadratic Regulator and Kalman Filter
Fall 2018, MCEN 6228, independent study: Lecturer for Network Reconstruction in Complex Systems
August 2012 to May 2015, Applying Mechatronics to Promote Science (AMPS) and MITSUI USA, Biomimetic and Bioinspiration
Spring and Fall 2011, Teaching Assistant for Quantitative Methods in Finance: assisted Instructor by performing teaching-related duties such as grading homework, proctoring exams, and providing recitations (review of lectures, tutoring, problem solving)
Fall 2011, Mentor for the Bloomberg System Certification
An educational app simulating fish behavior including individual behavior with tigmo-taxi, obstacle avoidance, fear response to predator, attraction to stimuli, fish social behavior (available on google play store). simUfish has bee tested in K12 school and shown effective to engage students in learning about fish as they were able to relate it to an aquarium visit. (see the published paper: Mwaffo et al., zebrafish, 2017).
A vision and voice controlled self-balancing robot designed for in class lab experiments or for outreach activities to introduce students to the notion of (i) feedback control of inverted pendulum using either PD, PID, LQR, and/or Kalman filter, and also to (ii) Machine Learning and Artificial Intelligence through neural networks or reinforcement learning.