class MachineLearningEngineer:
"""
AI Researcher & ML Engineer specializing in Computer Vision,
Deep Learning, and 3D Graphics | Building intelligent systems
that bridge perception and cognition
"""
def __init__(self):
self.name = "Reiyo"
self.role = "Machine Learning Engineer"
self.company = "@Synexian-Labs-Private-Limited"
self.location = "New Jersey, USA"
self.education = {
"field": "Computer Science & AI",
"focus": ["Deep Learning", "Computer Vision", "NLP"]
}
@property
def technical_expertise(self):
return {
"computer_vision": [
"2D/3D Pose Estimation",
"Motion Capture Analysis",
"Object Detection & Tracking",
"3D Reconstruction"
],
"deep_learning": [
"Transformer Architectures",
"Graph Neural Networks",
"Curriculum Learning",
"Topic Modeling"
],
"specialized_areas": [
"Reinforcement Learning",
"Advanced NLP",
"3D Computer Graphics",
"MLOps & Production ML"
]
}
@property
def current_focus(self):
return {
"research": [
"Graph Transformers for Pose Estimation",
"Topic-Modeled Curriculum Learning",
"3D Motion Capture Visualization"
],
"development": [
"Production-scale ML systems",
"Real-time CV applications",
"Interactive 3D visualization tools"
],
"learning": [
"Advanced RL algorithms",
"Transformer optimizations",
"3D rendering techniques"
]
}
def get_current_work(self):
return """
🔬 Research: Advancing pose estimation with Graph Transformers
🏗️ Building: Scalable ML pipelines for CV applications
🎨 Creating: Interactive 3D motion capture visualization tools
🤝 Collaborating: Open-source AI projects & research initiatives
"""
def life_philosophy(self):
return "Merging technology with creativity to build intelligent systems 🚀"
# Initialize
me = MachineLearningEngineer()
print(me.get_current_work())
print(f"\n💡 Philosophy: {me.life_philosophy()}")Building intelligent systems that understand and interact with the world through advanced computer vision and deep learning
🔥 Core Technologies
Programming Languages
ML/DL Frameworks & Libraries
MLOps & Cloud Infrastructure
Development & Tools
graph LR
A[📊 Data Collection] -->|Preprocessing| B[🔧 Feature Engineering]
B -->|Transform| C[🧠 Model Training]
C -->|Validate| D[📈 Evaluation]
D -->|Optimize| E[🚀 Deployment]
E -->|Monitor| F[🔄 Feedback Loop]
F -->|Retrain| C
style A fill:#667eea,stroke:#333,stroke-width:3px,color:#fff
style B fill:#764ba2,stroke:#333,stroke-width:3px,color:#fff
style C fill:#f093fb,stroke:#333,stroke-width:3px,color:#fff
style D fill:#4facfe,stroke:#333,stroke-width:3px,color:#fff
style E fill:#43e97b,stroke:#333,stroke-width:3px,color:#fff
style F fill:#fa709a,stroke:#333,stroke-width:3px,color:#fff
🎯 Pipeline Stages Breakdown
|
Data Collection
• Web scraping • API integration • Dataset curation • Data augmentation Tools: NumPy, Pandas, OpenCV |
Feature Engineering
• Feature extraction • Normalization • Dimensionality reduction • Feature selection Tools: Scikit-learn, TensorFlow |
Model Training
• Architecture design • Hyperparameter tuning • Transfer learning • Distributed training Tools: PyTorch, Keras, JAX |
Evaluation
• Performance metrics • Cross-validation • A/B testing • Benchmark comparison Tools: MLflow, TensorBoard |
Deployment
• Model optimization • API development • Containerization • Cloud deployment Tools: Docker, AWS, FastAPI |
Monitoring
• Performance tracking • Data drift detection • Model retraining • Continuous improvement Tools: Prometheus, Grafana |
| Stage | Status | Metric | Value | Last Updated |
|---|---|---|---|---|
| 🧠 Model Training | 🟢 Active | Accuracy | 96.4% | 2025-12-30 |
| ⚡ Inference | 🟢 Optimal | Latency | 43ms | 2025-12-30 |
| 📦 Deployment | 🟢 Stable | Uptime | 99.9% | 2025-12-30 |
| 💾 Data Pipeline | 🟢 Running | Samples Processed | 511K+ | 2025-12-30 |
| 🚀 Active Projects | 🟢 Growing | Count | 14+ | 2025-12-30 |
🎯 Key Workflow Features
Automation & Efficiency:
- ✅ Automated data preprocessing pipelines
- ✅ Continuous model training and validation
- ✅ Real-time performance monitoring
- ✅ Automated hyperparameter optimization
Scalability & Performance:
- ✅ Distributed training on multi-GPU clusters
- ✅ Model quantization and optimization
- ✅ Horizontal scaling for inference
- ✅ Efficient batch processing
Production Ready:
- ✅ CI/CD integration for ML models
- ✅ A/B testing framework
- ✅ Model versioning and rollback
- ✅ Production monitoring and alerting
Research & Development:
- ✅ Experiment tracking with MLflow
- ✅ Reproducible research workflows
- ✅ Collaborative development environment
- ✅ Documentation and knowledge sharing
Data & Processing: NumPy Pandas OpenCV Pillow Albumentations
ML Frameworks: PyTorch TensorFlow Keras Scikit-learn JAX Hugging Face
Experiment Tracking: MLflow Weights & Biases TensorBoard Neptune.ai
Deployment: Docker Kubernetes FastAPI Flask Streamlit
Cloud Platforms: AWS SageMaker Google Cloud AI Azure ML Paperspace
Monitoring: Prometheus Grafana ELK Stack CloudWatch
📉 Detailed Performance Metrics
Key Insights:
- 📊 Peak Accuracy: Achieved 97.2% on validation set (Week 48)
- 📉 Training Stability: Loss reduced by 85% over 50 epochs
- 💾 Dataset Scale: 500K+ samples across 10+ categories
- 🚀 Inference Speed: Optimized to 42ms average latency
- 🎯 Current Focus: Improving edge case performance and model robustness
| Experiment | Model | Accuracy | Loss | F1-Score | Status |
|---|---|---|---|---|---|
| GTransformer-v3 | Graph Transformer | 95.8% | 0.042 | 0.961 | ✅ Deployed |
| PoseNet-Enhanced | CNN + Attention | 93.2% | 0.068 | 0.945 | 🔄 Training |
| Vision-RL-Agent | RL + Vision | 89.5% | 0.115 | 0.902 | 🧪 Experimental |
| BaselineNet | ResNet-50 | 87.3% | 0.142 | 0.888 | 📊 Baseline |
🎨 Visualization Features
Auto-Updating Charts:
- ✅ Daily Updates - Charts refresh automatically every 24 hours
- ✅ SVG Format - Crisp, scalable vector graphics
- ✅ GitHub Actions - Fully automated via CI/CD pipeline
- ✅ Custom Styling - Matches your profile theme
- ✅ Real Data - Can connect to MLflow, WandB, or TensorBoard
Tracked Metrics:
- 🎯 Model accuracy across training epochs
- 📉 Training & validation loss curves
- 💾 Dataset growth and composition
- 🗣️ Programming language usage
- 🚀 Inference latency benchmarks
- 📊 Comprehensive performance dashboards
Charts automatically updated via GitHub Actions • Last updated: 2024-12-30
Python 12 hrs 45 mins ████████████░░░░░░░░ 55.2%
C++ 4 hrs 32 mins ████░░░░░░░░░░░░░░░░ 19.7%
Jupyter 3 hrs 15 mins ███░░░░░░░░░░░░░░░░░ 14.1%
Markdown 1 hr 23 mins █░░░░░░░░░░░░░░░░░░░ 6.0%
Other 1 hr 10 mins █░░░░░░░░░░░░░░░░░░░ 5.0%
|
Graph Transformer for Pose Estimation
⭐ Star | 🔬 Research Paper |
Interactive 3D Motion Capture Visualization
⭐ Star | 📖 Documentation |
|
2D Human Pose Estimation Pipeline
⭐ Star | 🚀 Demo |
Advanced Training Methodology
⭐ Star | 📄 Paper |
|
Collection of AI/ML Experiments
⭐ Star | 🔍 Explore |
Model Conversion for iOS
⭐ Star | 📱 Deploy |
current_role:
position: "Machine Learning Engineer"
company: "Synexian Labs Private Limited"
location: "New Jersey, USA"
focus_areas:
- Computer Vision Systems
- Deep Learning Model Development
- 3D Graphics & Visualization
- Production ML Pipeline Design
expertise:
computer_vision:
- Human Pose Estimation (2D/3D)
- Motion Capture Analysis
- Real-time Object Detection
- 3D Scene Understanding
deep_learning:
- Transformer Architectures
- Graph Neural Networks
- Curriculum Learning Strategies
- Model Optimization & Deployment
research:
- Published work in ML/CV
- ORCID: 0009-0002-8456-7751
- Conference presentations
- Open-source contributions
technical_skills:
advanced:
- PyTorch Deep Learning
- Computer Vision (OpenCV)
- 3D Graphics Programming
- NLP & Transformers
proficient:
- Cloud Infrastructure (AWS/GCP/Azure)
- MLOps & Model Deployment
- Distributed Training
- A/B Testing & ExperimentationResearch Interests:
- 🧠 Graph Neural Networks for Structured Prediction
- 🏃 Human Pose Estimation & Motion Analysis
- 📚 Curriculum Learning & Training Optimization
- 🎨 3D Computer Vision & Graphics
- 🤖 Reinforcement Learning for Robotics
Current Research:
- Graph Transformer architectures for human pose estimation
- Topic-modeled curriculum learning for neural network training
- Real-time 3D motion capture visualization systems
| Q1 2025 | Q2 2025 | Q3 2025 | Q4 2025 |
|---|---|---|---|
| ✅ Launch GTransformer | 🚧 Publish Research Paper | 📝 Conference Submission | 🎯 Open Source Release |
| ✅ MocapViewer3D v1.0 | 🚧 Advanced RL Projects | 📝 Production ML Pipeline | 🎯 Community Building |
| 🚧 Curriculum Learning | 📝 3D Vision Systems | 🚧 Industry Collaboration | 🎯 Knowledge Sharing |
Key Objectives:
- 🔬 Publish research in top-tier ML/CV conferences
- 🌟 Contribute to major open-source ML projects
- 🏗️ Build production-grade ML systems
- 👥 Mentor aspiring ML engineers
- 📚 Share knowledge through blogs & tutorials
Research Collaboration • Open Source Projects • ML Engineering Roles • Speaking Engagements
def reach_out():
interests = {
"collaborate_on": ["Research projects", "Open source ML tools", "Production systems"],
"discuss_about": ["Computer Vision", "Deep Learning", "3D Graphics", "MLOps"],
"available_for": ["Technical consulting", "Speaking", "Mentoring", "Code review"]
}
contact = {
"email": "reiyo1113@gmail.com",
"linkedin": "linkedin.com/in/reiyo06",
"portfolio": "oreiyo.space"
}
return "Let's build something amazing together! 🚀"
print(reach_out())
