My work explores one recurring question: How do intelligent systems represent, reason, imagine, understand, learn, and build under uncertainty? I build products, frameworks, and research to explore different parts of that question.
My projects begin with questions that remain difficult even after the first answer.
How do we know learning has occurred when learning itself cannot be directly observed?
How should behavioral signals be interpreted when their meaning changes across context?
How do we build reliable systems when truth, outcomes, and constraints remain uncertain?
When is understanding sufficient to stop studying and begin building?
Long-running projects where engineering, software, research, and uncertainty meet.
An immigrant-support platform designed to make information, housing, community knowledge, and practical resources easier to find, understand, and trust.
A public engineering and systems-thinking lab where I explore mechanical systems, robotics, CAD, software, and technical ideas through first-principles reasoning and documented building.
A credibility-first short-form learning platform exploring how technical content can be ranked using behavioral evidence rather than popularity alone.
Questions developed through literature, experimentation, system modeling, and technical inquiry.
A completed academic investigation using EPA air-quality data to examine the difference between observing environmental conditions through monitoring and extending those observations through modeling.
An investigation into how learning can be inferred when the internal process itself is not directly observable and feelings of clarity, attention, or engagement may provide weak evidence.
Working models for reasoning about learning, systems, evidence, and sufficient understanding.
An epistemic decision framework for determining when understanding is sufficient to begin acting, building, testing, or deciding under uncertainty.
A systems-thinking framework for defining a system, tracing its inputs and governing interactions, identifying its outputs, exposing assumptions and constraints, and preserving latent uncertainty before analysis or design begins.
An evidential interpretation framework for organizing observable behaviors according to how strongly they may support the inference that learning has occurred.
Essays and research notes documenting how the questions and systems are evolving.
An investigation into how behavioral signals should influence credibility-oriented ranking when each signal differs in intentionality, ambiguity, context, and evidential strength.
An investigation into why views, likes, retention, and virality should not be treated as direct evidence of credibility—and which behavioral signals might cautiously reduce uncertainty about a technical video’s perceived usefulness.
A revision of the original Evidence of Learning framework explaining why Observation was removed and why Recall and Reflection were introduced in version 1.1.
Videos, presentations, talks, demonstrations, and public explanations.
A public engineering series documenting the progressive design of a robotic hand through SolidWorks, mechanical reasoning, electronics, and control systems.
A short-form series questioning how someone can know that learning occurred rather than merely assuming it from attention, completion, or familiarity.
A developing talk examining why the feeling of learning can be mistaken for evidence that learning has actually occurred.
What I am currently building, researching, reading, preparing, and trying to understand.
Reach out about engineering, research, learning systems, speaking, collaboration, or an idea connected to the work.