I'm actively building across AI, machine learning, and software engineering — driven by curiosity for how intelligent systems can solve real-world problems at scale and transform user experiences.
Over the past year I've shipped end-to-end projects — a brain tumor segmentation system using Swin-UNETR on BraTS 2021, a hardware-aware ML optimizer achieving 88% configuration accuracy across 20 real hardware environments, and a generative AI platform built for Samsung retailers. I don't just study concepts — I build with them.
I work with Python, PyTorch, TensorFlow, scikit-learn, OpenCV, FastAPI, and React — across computer vision, NLP, full-stack development, and ML systems.
Currently seeking internship and full-time opportunities where I can contribute to impactful products, gain real industry exposure, and work alongside teams building things that matter.
EMAIL: princehooda61@gmail.com
Open to: Internships · Full-time · Research collaborations
| Project | Description | Stack |
|---|---|---|
| Samsung Ad-Forge | Generative AI platform for hyper-local Samsung retailer video ads · Samsung PRISM Hackathon | Python · AWS Lambda · LLM · Text-to-Video · Flutter |
| EPMO | Hardware-aware ML system predicting optimal PyTorch DataLoader config · 88% accuracy across 20 environments | Python · scikit-learn · XGBoost · PyTorch |
| TumorVision | 3D brain tumor segmentation using Swin-UNETR on BraTS 2021 with interactive visualization | PyTorch · MONAI · Gradio |
| Deepfake Detector | Video deepfake detection using MTCNN + FaceNet embeddings + heuristic analysis | PyTorch · FastAPI · OpenCV |
| LifeVault | Live deployed intelligent personal workspace with smart notes and file vault | React · Firebase · Cloudinary · Vercel |
| Ryser Parallel Permanent | Optimized #P-hard matrix permanent with Gray code + CPU parallelism | Python · NumPy · multiprocessing |
More projects available in repositories