I'm a sophomore at River Islands High School, dual-enrolled at San Joaquin Delta College. I specialize in the integration of AI and machine learning into engineering disciplines — mechatronics and aerospace in particular — along with the mathematics, simulation, and control that has to hold underneath for that integration to mean anything.

I like problems that stay interesting after the first correct answer: Is this number real, or is my instrument lying to me? Which assumption breaks first? What does the system do the moment it does? That's why I work where ML meets hardware. A model that scores well on a benchmark and a model you would trust to run a physical system are not the same object, and the distance between them is where the engineering actually lives.

Member of the Computer Science and Artificial Intelligence Laboratory, assisting the development of a system that changes the way data can be visualized. The interesting part of the problem sits underneath the interface: what a system has to compute, and how it has to represent what it knows, before a person can look at the result and see something they couldn't see before.

Machine learning applications in robotics, and what it actually takes to move them into the real world. My current project asks whether a quadruped can recover its gait after an actuator or sensor fault by learning a small correction online — without retraining the policy underneath. So far the work has been as much about building trustworthy measurement as building the method: the first substantive result was discovering that the recovery criterion was crediting recoveries that never happened.

Conducting an independent research project under the mentorship of Fernanda-Maria Lugo-Bolanos of Brown University. I was accepted into the program with a merit-based scholarship for a revolutionary research idea proposal video. In this project, I am currently investigating whether a skin lesion classifier trained on the ISIC dataset relies genuinely on actual lesion features or inadvertently keys off confounding artifacts like surgical ink marks, rulers, and hair.

flowchart LR
    OBS["observation<br/><b>34-dim</b>"] --> PI["π_base<br/>PPO · <b>frozen</b><br/>5 seeds · gait-gated"]
    OBS --> RES["residual Δa<br/><i>updated online</i>"]
    PI --> SUM(("+"))
    RES -.-> SUM
    SUM --> ACT["action<br/><b>12-dim</b> · 60 Hz"]
    ACT --> ENV["PyBullet quadruped<br/>12 joints · 240 Hz physics"]
    ENV --> OBS
    FAULT["⚠ fault injected<br/>@ step 200"] --> ENV

    classDef core fill:#1f6feb,stroke:#58a6ff,stroke-width:1px,color:#ffffff
    classDef wip fill:#7c3aed,stroke:#a78bfa,stroke-width:1px,color:#ffffff,stroke-dasharray:4 3
    classDef env fill:#0f766e,stroke:#2dd4bf,stroke-width:1px,color:#ffffff
    classDef bad fill:#b91c1c,stroke:#f87171,stroke-width:1px,color:#ffffff
    class OBS,PI,ACT,SUM core
    class RES wip
    class ENV env
    class FAULT bad

The seized joint stays seized. The residual redistributes the gait around it:

$$a_t ;=; \underbrace{\pi_{\text{base}}(o_t)}_{\text{frozen}} ;+; \underbrace{\Delta_\theta(o_t)}_{\text{online}}, \qquad \theta \leftarrow \theta - \eta,\nabla_\theta \mathcal{L}\big(\tau_{t-k:t}\big)$$

Measured against two bounds — no adaptation (Baseline A) and full retraining (Baseline B).

Open to research collaborations and AI/ML internships.

robotics · machine learning · mathematics · research · CAD

"Anyone can make it work once. Engineering is knowing which assumption breaks first — and what the system does the moment it does."