3rd Year Undergraduate Researcher at NIT Kurukshetra (India)
I am an undergraduate researcher focused on efficient deep learning architectures for vision and scientific AI.
My research spans 3D medical image segmentation, neuromorphic liquid neural networks, and graph-based learning systems, with applications in healthcare and hyperspectral analysis.
Research Highlights:
- 📚 Publications: Springer & Elsevier journals
- 🛠️ Open Source: Contributor to PyTorch Lightning & OpenCV & SnnTorch
- 🔬 Focus: Building architectures that deliver higher segmentation accuracy than existing state-of-the-art approaches with lower computational costs
Tech Stack: PyTorch, CUDA, Medical Imaging Libraries
Contribution: A lightweight transformer-based model achieving higher segmentation accuracy than existing state-of-the-art approaches while reducing GFLOPs and parameter count
Impact: Strong results on BraTS & Synapse datasets
Tech Stack: Torch, Fourier Operators, Spectral Analysis
Contribution: Physics-aware causal framework for hyperspectral super-resolution
Impact: Benchmarking against DiffuHSI and RAWformer
Tech Stack: PyTorch, SpikingNN, Differential Equations
Contribution: Combines liquid neural networks with transformer dynamics for adaptive temporal modeling
Impact: Neuromorphic computing applied to event-based vision
Tech Stack: PyTorch Geometric Temporal, NetworkX
Achievement: +15% over baselines on large-scale Wikipedia dataset (5M+ nodes)
Status: NeurIPS 2025 target
- 📚 Published in Springer & Elsevier journals
- 🌟 Contributor to PyTorch Lightning & OpenCV & SnnTorch
- 🔬 Targeting top-tier Core A* conferences (NeurIPS, CVPR, ICLR)
- 💡 Developed novel architectures in 3D vision, neuromorphic AI, and hyperspectral learning
Currently Seeking: Summer 2025 AI/ML Research Internships
Interests: Computer Vision • Neuromorphic AI • Graph Learning • Medical Imaging