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 | 97.3% | 2026-01-25 |
| ⚡ Inference | 🟢 Optimal | Latency | 44ms | 2026-01-25 |
| 📦 Deployment | 🟢 Stable | Uptime | 99.9% | 2026-01-25 |
| 💾 Data Pipeline | 🟢 Running | Samples Processed | 508K+ | 2026-01-25 |
| 🚀 Active Projects | 🟢 Growing | Count | 10+ | 2026-01-25 |
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
The knowledge graph above provides an interactive visualization of my projects, categorized by AI and connected based on shared technologies and themes. Click nodes to explore, drag to rearrange, and discover the relationships between different projects.
Features:
- 🎨 AI-Categorized: Projects automatically categorized using machine learning
- 🔗 Smart Connections: Related projects linked by shared languages and technologies
- 📊 Data-Driven: Node sizes represent project popularity (stars)
- 🎯 Interactive: Click, drag, zoom, and explore in real-time
📊 View Full Graph • [🔄 Last Updated: 2026-01-25]
|
Introducing MocapViewer3D, a groundbreaking interactive 3D/2D motion capture visualization tool that revolutionizes the way researchers and animators interpret and manipulate motion capture data. By seamlessly combining real-time camera perspective adjustments and skeleton pose viewing, this innovative tool unlocks unprecedented levels of depth and insight, allowing users to explore and analyze motion capture data in ways previously unimaginable. The potential applications of MocapViewer3D are vast, ranging from advancing computer vision and animation research to enhancing performance capture and simulation in the film and gaming industries, ultimately driving innovation and creativity in the digital arts. By marrying cutting-edge technology with intuitive user interface, MocapViewer3D is poised to redefine the field of motion capture and 3D graphics. 💡 Why Featured This Week: MocapViewer3D deserves to be featured this week because it showcases a groundbreaking technical innovation in the intersection of computer vision and 3D graphics, enabling researchers and animators to seamlessly visualize and manipulate motion capture data in a fully interactive environment. This tool's practical value lies in its ability to accelerate research and workflow efficiency, making it an invaluable resource for the computer vision and animation communities. |
📊 Project Stats
🔗 Quick Links |
🤖 AI-selected and described • Updated weekly
|
Published: January 25, 2026 In the realm of natural language processing (NLP), one of the most significant challenges is effectively handling the hierarchical structure of docume... Tags: |
💻 System Components
AI/ML Framework:
- Hugging Face Inference API
- Multi-model ensemble (6+ models)
- Automatic fallback system
- Rate limiting & retry logic
Automation:
- GitHub Actions (CI/CD)
- Python 3.11+
- Scheduled workflows (cron)
- Manual trigger support
Data Processing:
- GitHub API v3
- PyGithub library
- JSON data structures
- Markdown generation
Models in Ensemble:
- Qwen/Qwen2.5-7B-Instruct (Primary)
- meta-llama/Llama-3.2-3B-Instruct
- mistralai/Mistral-7B-Instruct-v0.3
- microsoft/Phi-3-mini-4k-instruct
- google/gemma-2-9b-it- ✅ Fault Tolerance: Automatic model fallback on failures
- ✅ Rate Limiting: Smart queue management for API calls
- ✅ Error Recovery: Exponential backoff with retries
- ✅ Data Validation: Schema validation for all inputs/outputs
- ✅ Backup System: Automatic README backups before updates
- ✅ Logging: Comprehensive logs for debugging
- ✅ Metrics: Performance tracking and monitoring
🔄 Workflow Process
sequenceDiagram
participant GH as GitHub Actions
participant AG as Agent
participant HF as Hugging Face
participant RE as README
GH->>AG: Trigger (Daily/Manual)
AG->>AG: Load Configuration
AG->>GH: Fetch Repository Data
loop For Each Model (until success)
AG->>HF: Request Analysis
alt Success
HF->>AG: Return Insights
else Failure/Timeout
AG->>AG: Try Next Model
end
end
AG->>AG: Validate & Format
AG->>RE: Update README
AG->>GH: Commit Changes
AG->>AG: Update Metrics
GH->>GH: Create Artifact
- Trigger: Daily at 00:00 UTC (customizable)
- Duration: ~5-15 seconds average
- Retry Window: Up to 2 minutes with fallbacks
- Timeout: 120 seconds per API call
Want to see the magic in action?
Steps:
- Click the badge above
- Select "Run workflow"
- (Optional) Enable debug mode
- Click "Run workflow" button
- Watch real-time logs
- See README update in ~10 seconds!
Week 1: ████████████████████ 100%
Week 2: ███████████████████░ 95%
Week 3: ████████████████████ 98%
Week 4: ████████████████████ 100%
| Time Range | Percentage | Status |
|---|---|---|
| < 5s | 45% | 🟢 Excellent |
| 5-10s | 40% | 🟢 Good |
| 10-20s | 12% | 🟡 Acceptable |
| > 20s | 3% | 🔴 Slow |
| Model | Usage | Success Rate |
|---|---|---|
| Qwen 2.5 | 78% | 98.5% |
| Llama 3.2 | 15% | 96.2% |
| Mistral 7B | 5% | 94.8% |
| Others | 2% | 93.1% |
|
99.9% Uptime Multi-model fallback ensures continuous operation even if primary models fail • Automatic recovery • Smart retries • Error handling • Health monitoring |
Sub-10s Execution Optimized for speed with efficient API usage and parallel processing • Cached responses • Batch operations • Async processing • Load balancing |
Context-Aware AI Deep understanding of code patterns, development trends, and team dynamics • Semantic analysis • Trend prediction • Pattern recognition • Actionable insights |
Interested in building your own AI agent?
This entire system is open source and well-documented!
Tech Stack: Python • GitHub Actions • Hugging Face • AI/ML • DevOps
This AI agent showcases the intersection of Machine Learning Engineering, DevOps, and Automation.
Core Technologies: Multi-Model AI Ensemble • GitHub Actions CI/CD • Hugging Face Transformers • Python Async • REST APIs
Key Concepts: Fault Tolerance • Load Balancing • Rate Limiting • Error Recovery • Automated Testing • Performance Monitoring
🤖 This section is autonomously maintained by an AI agent
System Status: 
📊 View Logs • ⚙️ Configure • 🐛 Report Issue • 💡 Suggest Feature
- 🚀 Published release Aegis v0.1.0 — Initial Internal Release in RyoK3N/Aegis
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 & Experimentation
Research 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
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())
