Python & Machine Learning

Research: synthetic data with diffusion.

My own research paper (in progress) on generating synthetic data using a diffusion model built from an MLP and an encoder.

~95%+
Model confidence after adding synthetic data, up from ~80%
4
Stage pipeline: data → encoder → diffusion → synthetic

Language

Python, with pandas and matplotlib
01 · How it works

From scarce data to useful data.

STEP 01Real data
STEP 02Encoder
STEP 03MLP diffusion model
STEP 04Synthetic data
Why it matters

Synthetic data helps when real data is scarce, private or imbalanced. It also strengthens models trained on very little data, such as rare diseases. In one test it raised model confidence from ~80% to ~95%+.

Stack
Python · core language
MLP + Encoder · model architecture
Diffusion model · data generation
pandas · data handling
matplotlib · visualizing results
Status

Paper in progress. I'm working on this whenever I can find and get my hands on more testing data, making small incremental upgrades. Mail me to ask about it!

Contact

Want to talk about the research?

I'm open to roles, collaborations and new teams. Reach out and I'll reply.

tahaaleem885@gmail.comLinkedIn ↗← All my work