Skin‑lesion CNN by Vansh Malani
Sophisticated CNNs will classify 14 000 dermoscopic images into the seven standard skin‑cancer diagnoses. GPU credits let the team train, tune and deploy a high‑accuracy model that could accelerate melanoma screening and aid clinicians worldwide.
SAR Image Coloring by Shamba Chowdhury
Builds a custom diffusion model to transform grayscale Synthetic Aperture Radar data into realistic optical‑style images, giving scientists an intuitive view where photography is impossible. High‑VRAM GPUs power diffusion training and possible custom CLIP work, culminating in an open‑source tool.
Medical ML Workbench by Rounak Kumar Manjhi
A student team is tackling Alzheimer’s MRI, ECG‑based heart‑disease detection and stroke prediction on million‑record datasets. Deep networks run 50–500 epochs; daily experiments outstrip local hardware. GPU credits will cut each training cycle from hours to minutes and speed three forthcoming papers.
Prime‑Number Soundscape by Suryansh Shekhawat
GPUs sieve primes up to 1012, spot gap “constellations” (twins, quadruplets, …) and map each pattern to notes or chords, generating a 30‑second audio piece plus statistics on constellation frequencies. The project turns number theory into music you can hear.
RNA Structure Prediction by Soltani Abdellatif
Aims to bring AlphaFold‑style advances to RNA by assembling augmented secondary‑structure data and testing new deep architectures that work despite sparse alignments. GPU power enables large‑scale experiments toward accurate 2‑D and 3‑D RNA folding, unlocking insights into gene regulation and therapeutics.
Fast Gravitational-Wave detection through CNN by Yuvraj Sharma
Optimizes a separable‑CNN to spot eccentric binary‑black‑hole merger signatures in LIGO images, reducing detection latency for future alerts. Training on 450 k simulated‑waveform images with CUDA‑accelerated TensorFlow demands significant GPU capacity.
CLIP-Powered Design Search by Naman Singhal
Creates an intelligent image-search engine for a large Google Drive design library. CLIP embeddings are stored in a vector database and served via web UI, enabling queries like “red abstract pattern” without manual tags. GPUs accelerate bulk embedding generation and future collection updates.
Deep‑Learning Intrusion Detection by Madhi Madhan Libonce
Builds a hybrid supervised‑plus‑unsupervised deep‑learning engine that learns normal network behaviour and flags both known and novel cyber‑attacks in real time. Designed for cloud, enterprise and IoT networks, it adapts continuously to new threat patterns, beating static rule‑based IDS by cutting response times and preventing breaches.