class AIResearcher:
def __init__(self):
self.name = "Anuruddha Paul"
self.role = "Computer Science Student & AI Researcher"
self.institution = "KIIT University"
self.cgpa = 9.8
self.location = "India 🇮🇳"
self.research_interests = [
"Computer Vision",
"Deep Learning",
"Vision Transformers",
"Object Detection (YOLO)",
"Edge AI & IoT",
"Large Language Models"
]
def say_hi(self):
print("Thanks for visiting! Let's innovate together in AI! 🚀")
me = AIResearcher()
me.say_hi()Specializing in Computer Vision, Deep Learning, and AI Systems Development
I'm a passionate Computer Science student at KIIT University with a strong focus on cutting-edge AI research. My work spans hybrid neural architectures (combining Vision Transformers with YOLO), edge deployment on resource-constrained devices, and building production-grade AI applications. Currently maintaining a 9.8+ CGPA while actively contributing to research in computer vision and machine learning.
|
Advanced object detection system combining YOLO architecture with transformer-based attention mechanisms for agricultural applications. Tech Stack:
|
Agentic AI system using LangGraph and Google Gemini to automatically generate research papers from arXiv papers. Tech Stack:
|
|
Ensemble LLM system using Groq API for question answering with multiple reasoning models. Tech Stack:
|
Multimodal AI assistant with real-time vision, voice interaction, and intelligent conversation capabilities. Tech Stack:
|
| Area | Focus | Status |
|---|---|---|
| Computer Vision | Vision Transformers, Hybrid Architectures (CNN+ViT) | 🔬 Active Research |
| Object Detection | YOLO-based systems, Real-time detection | 🚀 Production |
| Edge AI | Raspberry Pi deployment, Model optimization | ⚡ Ongoing |
| LLM Systems | Multi-model ensembles, Reasoning evaluation | 💡 Experimental |
| Vision-Language Models | Multimodal understanding, VQA systems | 📚 Learning |
📄 Research Papers & Projects
-
Hybrid YOLO-Transformer Architecture for Agricultural Object Detection
- Combining YOLO efficiency with Transformer attention mechanisms
- Focus on HARVEST dataset and real-world deployment
-
Multi-Model LLM Evaluation Framework
- Comparative analysis of reasoning capabilities across models
- Using Groq API for efficient inference
-
Wildlife Classification with Cross-Dataset Validation
- Deployed on edge devices (Raspberry Pi)
- 100+ GB dataset processing
-
AlexNet & CNN Architectures from Scratch
- Educational implementations in TensorFlow/Keras
- Complete mathematical derivations
%%{init: {'theme':'dark'}}%%
timeline
title My AI/ML Journey
2023 : Started Deep Learning
: Built first CNN models
2024 : Advanced Computer Vision
: YOLO & Vision Transformers
: Research Paper Contributions
2025 : Production AI Systems
: Edge AI Deployment
: LLM & Agentic AI
: 9.8+ CGPA Achievement
- [✅] 🎓 Maintain 9.8+ CGPA at KIIT University
- [✅] 📝 Publish research paper on Hybrid YOLO-Transformer Architecture
- [✅] 🚀 Deploy 3+ production-grade AI applications
- 🌟 Contribute to major open-source AI projects
- 🏆 Win competitive programming competitions
- 💻 Master LangGraph & Agentic AI systems
- [✅] 📊 Build comprehensive ML portfolio with 20+ projects
- [✅] 🤝 Collaborate with AI research labs
🧠 Machine Learning & Deep Learning
Core Competencies:
- Neural Network Architectures (CNNs, RNNs, Transformers)
- Vision Transformers (ViT, Swin Transformer, DEIT)
- Object Detection (YOLO, Faster R-CNN, SSD)
- Model Training & Optimization
- Transfer Learning & Fine-tuning
- Data Augmentation & Preprocessing
- Model Evaluation & Ablation Studies
Advanced Topics:
- Hybrid Architectures (CNN+ViT, CNN+RNN)
- Attention Mechanisms
- Model Compression & Quantization
- Edge AI & Mobile Deployment
- Cross-Dataset Validation
🔍 Computer Vision
Techniques:
- Image Classification
- Object Detection & Tracking
- Semantic Segmentation
- Instance Segmentation
- Pose Estimation
- Image Enhancement
- Video Analysis
Tools & Libraries:
- OpenCV, PIL/Pillow
- Albumentations
- YOLO (v5, v8)
- MMDetection
- Detectron2
🤖 Large Language Models & AI Agents
Frameworks:
- LangChain & LangGraph
- Hugging Face Transformers
- OpenAI API, Groq API
- Google Gemini
Applications:
- Agentic AI Systems
- RAG (Retrieval Augmented Generation)
- Multi-Model Ensembles
- Vision-Language Models
- Tool-using Agents
⚡ Edge AI & IoT
Platforms:
- Raspberry Pi (4/5)
- NVIDIA Jetson
- Google Coral
Technologies:
- TensorFlow Lite
- ONNX Runtime
- Model Optimization
- Real-time Inference
💻 Software Engineering
Full Stack Development:
- Frontend: React, HTML/CSS/JS
- Backend: FastAPI, Flask, Node.js
- Databases: MongoDB, PostgreSQL, MySQL
- Cloud: Google Cloud, AWS
- DevOps: Docker, Git, CI/CD
Best Practices:
- Clean Code & Design Patterns
- API Design & RESTful services
- Testing & Debugging
- Documentation
I'm always open to:
- 🔬 Research collaborations in AI/ML
- 💼 Interesting project opportunities
- 🎓 Academic discussions
- 🤝 Open-source contributions
- 📚 Knowledge sharing
Feel free to reach out! ✉️
- 🔥 Building Production-Grade AI Agents with LangGraph
- 🎯 YOLO vs Vision Transformers: A Comprehensive Comparison
- ⚡ Deploying ML Models on Raspberry Pi: A Complete Guide
- 🤖 Multi-Model LLM Ensembles: Theory and Practice
- 🌟 From Research to Production: My AI Journey
Bachelor of Technology in Computer Science | 2023 - 2027
CGPA: 9.8+ | Top Performer
Relevant Coursework:
- Machine Learning & Deep Learning
- Computer Vision
- Data Structures & Algorithms
- Database Management Systems
- Operating Systems
- Computer Networks
- Cloud Computing
Achievements:
- 🏆 Consistent Dean's List awardee
- 🌟 Top 5% in Computer Science program
- 📝 Research paper contributions
- 💻 Multiple hackathon participations
const aboutMe = {
code: ["Python", "Java", "C++", "JavaScript"],
askMeAbout: ["AI", "ML", "Computer Vision", "Deep Learning", "Edge AI"],
technologies: {
frameworks: ["PyTorch", "TensorFlow", "LangChain"],
tools: ["Docker", "Git", "CUDA"],
cloud: ["Google Cloud", "AWS"]
},
currentFocus: "Building Agentic AI Systems",
funFact: "I debug with print statements and I'm proud of it! 🐛"
};