About me

I'm Arel, a 16-year-old rising senior at Adlai Stevenson High School in Illinois. I taught myself to code and started learning machine learning by working on these projects. This site collects the experiments, their results, and the apps and demos that came out of them.

Why medicine? That part is personal. When my 13-year-old cousin was diagnosed with leukemia, I couldn't wrap my head around how that happens to a kid, so I did the one thing I know how to do: I tried to understand it through code, by building a model that classifies leukemia cells. She beat it and is doing well now — but it stuck with me. It also sits right where I come from. We're a family of four: my dad is a computer entrepreneur and my mom works in medical labs and the biological sciences, so the place where computing and the life sciences meet has always felt like home.

My first leukemia model scored 99.8%, but the dataset had no patient IDs. I couldn't tell whether cells from the same patient appeared in both training and testing. A later experiment with a patient split scored about 81%. That changed how I test my models: I check the split before trusting a score and include the errors in the write-up. These projects are learning exercises, not medical devices.

I'm working toward studying Computer Science + Bioengineering — the one path where I don't have to choose between the two things I love. More on that here →

About this portfolio

The portfolio covers skin images, ECG, EEG, blood cells, stock volatility, and a reinforcement-learning security sandbox. The source repository contains preparation, training, evaluation, and export scripts, along with five Flutter apps.

The write-ups explain decisions that affected each project: how the data was split, which metrics I used, where export tools failed, and how the model performed on a different dataset. Checkpoints and evaluation files are available alongside the code.

Methodology

• Training — PyTorch on local NVIDIA GPU hardware, using public research datasets.
• Deployment — on-device inference wherever feasible (TensorFlow Lite, ONNX Runtime), so demonstrations run offline and no user data leaves the device.
• Applications — Flutter for mobile, static web pages for browser demonstrations.
• Evaluation — every project reports metrics on held-out data and states its limitations explicitly.

Source code

The full source code for these projects is available on GitHub: github.com/anotherhuman5345/rlcapstone. Questions and corrections are welcome there.