Teaching Dossier
Preparing future-ready engineers and computer scientists for the AI era through innovative, student-centered, and ethically-grounded pedagogy.
Core Educational Pillars
My teaching philosophy overhauls traditional AI curricula to prepare students for the agentic era, where AI acts as a proactive, goal-oriented collaborator.
I design and develop novel pedagogical tools and curricula that bridge foundational theory with hands-on experience in building and deploying modern AI systems.
My goal is to cultivate independent, high-impact thinkers and builders who possess the technical skills and ethical framework to lead complex AI projects responsibly.
Online Course Hub
Hands-on Tutorials
Six research-informed, student-centered tutorials showcasing innovative pedagogy across AI education—from building autonomous agents to navigating the complete AI technology stack.
From Prompt to Autonomous System
Interactive session progressing through Bloom's Taxonomy—from basic AI concepts to creating a fully functional autonomous agent with debugging and ethical reasoning.
Programming Fundamentals in the ChatGPT Era
Modern intro programming lesson teaching students to use AI as a learning partner—moving from prompt-dependent beginners to independent programmers.
Making Reinforcement Learning Intuitive
Constructivist approach starting with play, building intuition through experience, then formalizing RL concepts. Students construct understanding before encountering algorithms.
Why Math Still Matters When ChatGPT Can Code
Students discover WHY we need to understand gradient descent when AI can write code. Through intentional struggle and Socratic dialogue, they build deep conceptual understanding.
From Vague Ideas to Production-Ready Code
Learn to engineer complete, tested, deployable applications using AI as a development partner—progressing from vague prompts to production-quality systems.
From ML Foundations to Cloud AI Platforms
Map the complete AI technology landscape—from foundational ML/DL through GenAI to production cloud platforms. Learn which tools to use when and how they integrate.
Optimizing VRAM for Training Large Models
Master the art of fitting large models in memory. Learn batch size limits, gradient checkpointing, mixed precision training, and how these techniques enable distributed training at scale.
When Your Model Fails Without Errors
Explore insidious failure modes in production ML—models that silently degrade without crashes. Learn to detect data drift, training-serving skew, feedback loops, and data leakage through hands-on monitoring implementation.
Generative AI in the Classroom
I believe in using AI not just as a subject of study, but as a powerful tool to enhance learning itself. A key part of my pedagogy involves creating custom GPTs that act as specialized 'co-explorers' for students, providing tailored guidance and feedback.
This assistant helps students brainstorm narrative structures for data-driven reports, guiding them through the principles of effective data communication.
Launch Assistant(Requires ChatGPT Plus Subscription)
A specialized tool designed to help students bridge the gap between a finished Tableau dashboard and a compelling, presentation-ready story.
Launch Assistant(Requires ChatGPT Plus Subscription)
Teaching Philosophy
Agent-centric pedagogy is an instructional approach that prepares learners to design, reason about, and responsibly deploy agentic AI systems. Rather than treating AI as a static tool, this pedagogy teaches students to think in terms of agents, behaviours, goals, and multi-agent dynamics.
At its core, it integrates three key dimensions:
- Technical Agency: How agents plan, act, use tools, and coordinate (orchestration, observability, safety).
- Human Agency: Designing systems that respect human values, oversight, and interpretability ("human-in-the-loop").
- Socio-Technical Agency: Analyzing impacts on communities, data sovereignty, and Indigenous knowledge frameworks.
The goal is to cultivate graduates who can architect, evaluate, and govern agentic systems responsibly—shifting from model-centric to system-centric thinking.
Evidence of Teaching Excellence
Quantitative Student Feedback
4.8 / 5.0
Teaching Effectiveness Rating
(vs. college average of 4.2)
92.5%
Clarity of Content
Students 'always' finding content understandable
4.1 → 4.8
Increased Student Interest
Self-reported interest level over a semester
Top 10%
Instructor Ranking
Consistently ranked in the top decile college-wide
Qualitative Student Feedback
"Always very kind, patient, helpful and truly cares about students. Your use of visual aids enhances students' understanding and engagement throughout the classes. Keep it up."
— Anonymous Student Feedback, Fall 2023
"Gratitude flows effortlessly for the guiding light. Her wisdom shapes minds, her kindness nurtures spirits, and her dedication paints the canvas of our future with hues of inspiration. In her presence, learning becomes a journey, and every lesson is a gift. Thank you for being the beacon of knowledge and warmth that lights our educational path."
— Anonymous Student Feedback, Fall 2023
"Her classes are mostly fun... Gives plenty of time to understand each topic, makes it easier to cope-up with the course."
— Anonymous Student Feedback, Winter 2024