I believe in using education to uplift individuals who can, in turn, serve their communities. To pursue this goal, I focus on building a learning community that goes beyond individual achievement, grounded in growth and shared purpose. I always ground my teaching and mentoring in the principles below.
Core Teaching Principles
- Creating a Collaborative Learning Environment. I strive to build a sense of community and belonging, where students feel valued and engaged in a shared mission.
- Establishing Clear Learning Goals. I articulate expectations and learning objectives to guide learning and ensure all students receive the same information and opportunities.
- Grounding Foundations in Applications. I create an environment where students do not see themselves as knowledge consumers but as future contributors to their fields.
- Assessing Learning Comprehensively. I evaluate both technical mastery and conceptual understanding while attending to students' individual needs. I put special emphasis on evaluating students' capacity to apply their knowledge to solve problems and communicate it to others.
- Championing Knowledge-Sharing. In both classroom teaching and individual mentoring, I emphasize knowledge-sharing as a core educational value.
I have participated in teaching across different institutions. At UC Berkeley, I will be teaching courses across control theory, machine learning, and artificial intelligence. Stay tuned for details soon!
Courses I have taught
- Distributed Model Predictive Control - Co-Instructor,
- AI Center Projects in Machine Learning Research - Co-Instructor,
- Robust Control Theory (CDS 231) - Teaching Assistant,
- Optimal Control and Estimation (CDS 112) - Teaching Assistant,
- Network Control Systems (CDS 141) - Teaching Assistant,
- Introductory Methods of Applied Mathematics (ACM 95/100) - Head of Teaching Assistants,
- Introduction to Probability Models (ACM 116) - Head of Teaching Assistants,