Decentralized AI for Rapid, Affordable Cancer Diagnostics

Hello! My name is Aadi Shah, and I’m a Cybersecurity and Network Infrastructure student at Purdue University. I’m reaching out to ask for your support on a critical research project that sits at the intersection of distributed AI and cancer diagnostics.

About the Research

I’m developing a system to distribute the computational load of deep learning models across multiple device nodes, rather than relying on a single, centralized server. By partitioning AI workloads—such as image preprocessing, feature extraction, and inference—across a scalable network of devices (e.g., Raspberry Pis, edge servers, and hospital workstations), we can dramatically reduce latency and hardware costs. This architecture is ideal for resource-constrained environments like community clinics and rural hospitals, where dedicated GPU clusters are impractical.

Why It Matters for Cancer Care

I’m applying this distributed-AI framework to automated analysis of histopathology images, training models to detect and classify malignant cells with high accuracy. Faster, decentralized inference means that hospitals can run complex diagnostics on-site—no internet dependency, minimal hardware investment, and real-time results at the patient’s bedside. Ultimately, this will help oncologists make quicker, more informed treatment decisions and expand access to early cancer screening in underserved areas.

The Funding Gap

Due to recent grant cuts, my budget has been reduced significantly. So far, I’ve raised over $10,000 through scholarships, part-time work, and private donations, which covered initial prototype development and pilot testing. To complete the next phase—optimizing model parallelism, expanding our device network, and conducting a full clinical validation study—we need an additional $35,000.

Here’s how your contribution will be used:

$15,000 for hardware (edge devices, networking equipment, backup power)

$10,000 for cloud compute credits and software licensing to train larger AI models

$10,000 for clinical collaboration and data-annotation stipends

How You Can Help

Donate: Every dollar accelerates our timeline and brings this technology closer to patient care.

Share: Amplify our campaign with colleagues in biotech, healthcare nonprofits, or philanthropists interested in global health.

Collaborate: If you represent a hospital, clinic, or research center, let’s partner on pilot deployments.

I’m deeply committed to translating this research into real-world impact. I’ll provide regular updates on model performance, deployment outcomes, and clinical feedback so you can see exactly how your support drives progress.

Thank you for considering this opportunity to empower hospitals with cutting-edge AI and improve cancer diagnostics for patients everywhere.

With gratitude and determination,

Aadi Shah

Purdue University, Cybersecurity & Network Infrastructure