My teaching philosophy is grounded in the belief that students learn engineering most effectively when they build, test, question, and iterate. Robotics is inherently hands-on — students gain true understanding when they see algorithms controlling real hardware, observe sensors responding to the environment, and diagnose how design choices shape system behavior.

I aim to help students connect foundational theory to the messy realities of real-world engineering, while building the engineering judgment and communication skills required for research and industry. I'm especially committed to creating environments where students from all backgrounds feel confident experimenting and asking questions.

Teaching Assistant
IE 574 — Industrial Robotics & Flexible Assembly
Purdue University · 2022–2026
Led labs in CoppeliaSim, TMFlow, and Python-based perception. Mentored semester-long team projects requiring full sensing–control–execution integration.
Teaching Assistant
IE 370 — Manufacturing Processes
Purdue University · 2022–2023
Instructional support and labs connecting manufacturing workflows, process planning, and automation concepts to engineering decision-making.
Teaching Assistant
IE 472 — Imagine, Model, and Make
Purdue University · Design Studio
Supported CAD-based design iteration, prototyping, and fabrication — turning conceptual designs into tested physical systems.
Instructor — SolidWorks & Prototyping
Robotics Design Projects
HKUST · Cheng Kar-Shun Robotics Institute · 2019–2022
Taught SolidWorks modeling and mentored teams through iterative design, material selection, and rapid prototyping.
Research Mentor
Undergraduate & Graduate Mentorship
Purdue MARS Lab · 2022–2026
Mentored researchers on tactile sensing, robot experiments, perception pipelines, and scientific writing. Several mentees published at top robotics venues.

Students built complete perception–manipulation systems, integrating sensing, control, and decision-making into full robotic workflows.

Vision-Guided Pick-and-Place System
Perception-Driven Industrial Automation
Mobile Manipulation with Vision Integration
Feedback-Driven Robotic Assembly Task
CriterionRating
Clarity of explanations4.55 / 5
Effectiveness in answering questions4.50 / 5
Availability and willingness to help4.58 / 5
Fostering an inclusive learning environment4.50 / 5
Robotics & Robot Programming Mechatronics Dynamics & System Modeling Control Systems Manufacturing Automation Embedded Sensing & Perception Human–Robot Interaction Multimodal Sensing & Embodied Intelligence Tactile Sensing for Robotic Manipulation