- 🎓 B.Tech in Computer Science & Engineering — BTKIT, Uttarakhand (2021–2025)
- 🧠 AI/ML Engineer specializing in Healthcare AI and Medical Imaging
- 📊 Research focus on brain tumor classification & segmentation using MRI
- 🔍 Strong interest in explainable AI, clinical reliability, and cross-dataset generalization
- 🌍 Open to relocation and visa sponsorship
- Pant, K., Pramanik, P. K. D., Zhao, Z.
A Robust ConvNeXt-Based Framework for Efficient, Generalizable, and Explainable Brain Tumor Classification on MRI
Bioengineering (MDPI), 2026
🔗 https://doi.org/10.3390/bioengineering13020157
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Clinically Explainable Hybrid CNN–Transformer Model for Efficient Brain Tumor Segmentation Using MRI
The Visual Computer (Springer), 2025 -
Global–Local Feature Fusion: A Robust Hybrid Deep Learning Model for Multiclass Brain Tumor Classification
Egyptian Informatics Journal (Elsevier), 2025
Languages & Tools
- Python · Git · GitHub
Deep Learning & CV
- PyTorch · TensorFlow · CNNs · ConvNeXt · Transformers
Medical Imaging & Explainability
- OpenCV · Grad-CAM++ · SHAP · timm
Healthcare AI
- Medical image classification & segmentation
- Model validation · Explainable AI · Reproducibility
Generative AI
- LLMs · RAG · LangChain · Hugging Face
MLOps & Experimentation
- Weights & Biases · TensorBoard
Platforms
- Google Colab · Kaggle
- Achieved 99.86%, 99.69%, and 99.83% cross-dataset accuracy
- Conducted ablation studies, statistical validation, and Grad-CAM++ explainability
🔗 https://github.com/ConvNeXt-Base
- Developed global–local feature fusion models for multi-class classification & segmentation
- Achieved up to 99.64% accuracy with strong generalization
- LangChain-based agentic AI system for mathematical reasoning and problem-solving
🔗 https://github.com/MathAIAssistant
- AI-powered healthcare agent using PubMed & OpenFDA for disease and medicine information
🔗 https://github.com/HealthAssistant
- Developed PDF-based Retrieval-Augmented Generation (RAG) system
- Designed a two-stage retrieval pipeline achieving ~85% accuracy
- Worked on LLM integration, system scalability, and enterprise AI workflows
- Exposure to GenAI, DevOps, and SAS-based AI systems
- Merged 5+ PRs across C, Java, and AI/ML repositories
- Built agentic AI systems and contributed to DSA and system design projects
💬 “Building reliable and explainable AI systems for real-world healthcare.”