Data scientist, technology practitioner and educator building a path toward hands-on STEAM learning — where AI, data, engineering and real-world problems become things students can understand, test and create.
About
I do not see teaching, data and technology as separate identities. They are different ways of doing the same thing: breaking down complexity, testing ideas with evidence and helping people build a clearer mental model of how the world works.
I teach by making difficult ideas visible, testable and connected to problems students can actually care about.
Outside work and study, I enjoy exploring new places, observing how environments shape experience and staying active. That same curiosity shows up in how I think about teaching: students learn better when learning feels like discovery, not only instruction.
My direction
I am not moving away from my previous experience. I am bringing it forward into a more student-facing direction — combining data thinking, technology practice and real teaching experience into a coherent STEAM path.
My professional background gives me a strong base in analysis, product thinking, experimentation and AI-enabled workflows. These are tools I want students to understand in practical, age-appropriate ways.
Through volunteer teaching, tutoring, discussion and coaching, I have learned that good teaching begins with attention, trust and clear communication — not just with content.
The direction I want to keep building is project-based, student-facing and grounded in real making: AI, data, engineering, design and reflection working together around meaningful problems.
Education
My formal training combines quantitative analysis, interdisciplinary policy study and strong campus-community experience. These two schools shaped both how I think and how I relate to people.
The Hong Kong University of Science and Technolog, Society Hub
Interdisciplinary graduate study connecting public policy, innovation, quantitative analysis and technology. This stage deepened my interest in turning research and systems thinking into practical learning experiences.
The Chinese University of Hong Kong, School of Data Science
An undergraduate foundation in probability, statistics and quantitative modelling, enriched by residential-college life and student leadership experience. It was also where my interest in combining data with people-centred work became much clearer.
Teaching
My teaching experience spans volunteer teaching, mathematics tutoring, coaching and project-based student selection. I focus on making students feel engaged first, then building understanding step by step through feedback, practice and reflection.
Led small-group interactions with children through reading, conversation, picture-card activities and outdoor games, focusing on participation, confidence and warmth in the learning experience.
One-to-one mathematics teaching across algebra, geometry and functions, using error analysis, targeted exercises, formative assessment and transfer practice.
Reviewed student applications, supported on-site selection and coached shortlisted students on structured communication, presentation and interview responses.
Besides volunteer teaching, I have also worked in selection, discussion and coaching settings that rely on attentive listening, clear questioning and structured feedback. These moments matter because teaching is not only content delivery — it is also helping people express their thinking more clearly.
Reading people, asking better questions and helping them articulate their ideas.
Feedback becomes far more powerful when it is timely, specific and genuinely human.
Industry
STEAM becomes more meaningful when students can see how ideas are used outside the classroom. My industry work gives me concrete examples of experimentation, AI, analytics, systems thinking and cross-functional problem solving.
Analyse product usage and user behaviour for the 360-camera line, and contribute to AI-enabled natural-language data querying. The work combines data interpretation, product thinking and applied AI in a physical consumer-technology context.
What this brings to STEAM: physical products, sensors, user behaviour, AI tools and evidence-based iteration.
Built metric frameworks and BI systems, designed funnel analyses and A/B evaluations, conducted user segmentation and contributed to AI assistant measurement and recommendation. One product optimisation stream improved a key conversion stage and supported measurable business growth.
What this brings to STEAM: experimentation, data literacy, AI systems, product design and multidisciplinary collaboration.
Supported data-platform performance governance, including optimisation of high-frequency query models and the codification of reusable SQL execution-plan diagnostic rules.
What this brings to STEAM: systems thinking, performance trade-offs and turning technical patterns into reusable methods.
Studied monetisation scale, first-purchase activation, repurchase behaviour and high-value user segments, using multi-dimensional analysis to understand how product behaviour connects to commercial outcomes.
What this brings to STEAM: human behaviour, consumer products and using data to test assumptions.
The Lab
Oliver STEAM Lab is not meant to be only a portfolio. It is a place to document experiments, classroom ideas and small interactive projects as they evolve.
A project-based learning concept around low-altitude technology, AI perception and robotics — designed to move students from a real-world mission to research, prototyping, testing and reflection.
Small browser-based experiments in data, AI and engineering. This section will become the interactive layer of the site as new teaching projects are built.
Ideas & Notes
Some of the concepts on this site come directly from lesson planning, mentoring conversations and my own ongoing thinking about how data, AI and STEAM should be taught.
Good STEAM learning is not four or five disconnected subjects placed side by side. It begins with a real problem that needs science, technology, engineering, mathematics and design thinking to work together.
I like using simple, visible design loops so students can experience iteration instead of treating wrong answers as failure.
I want students to see data not as a spreadsheet exercise, but as a way to notice patterns, test assumptions and justify decisions with evidence.
A low-altitude rescue project that connects embodied AI, computer vision, hardware-software integration and structured reflection — moving students from curiosity into building.
Toolkit
Teaching philosophy
Start with a strong mental model. Break complexity into visible, testable ideas before adding tools or terminology.
Turn knowledge into action through code, data, prototypes, experiments and real constraints.
Use evidence, feedback and iteration so students can explain not only what worked, but why it worked.
Selected moments
Beyond formal titles, these moments show the settings that matter to me most: education, campus life, movement, exploration and building real connection with people.
A graduation milestone that marks both growth and the confidence to connect technology with people and learning.
My undergraduate years were shaped not only by coursework, but also by the people and communities around me.
Running reminds me that growth is built through rhythm, resilience and steady progress — values I also bring into education.
I like being close to places that make you think bigger — a feeling I hope learning can also create.
A campus image I really like — dynamic, optimistic and a good visual metaphor for the next chapter taking flight.
I am developing Oliver STEAM Lab as a long-term home for teaching ideas, technology projects and hands-on learning experiments.