┌─────────────────────────────────────────────────────────────────────────┐
│ > Connecting to github.com/Gerrux... │
│ > ML Engineer | Computer Vision & LiDAR │
│ > Building automated forest inventory @ LLC Open Forest │
└─────────────────────────────────────────────────────────────────────────┘
class MLEngineer:
def __init__(self):
self.name = "Ilya Kalinin"
self.role = "ML Engineer"
self.company = "Открытый Лес"
self.passion = "Performance ⚡ & Model Quality 🎯"
def current_focus(self):
return {
"🌲": "Point Cloud Segmentation & Classification",
"🤖": "Tree Species Classification (LightGBM, CNNs, Gradient Boosting)",
"📊": "Forest Inventory Automation (ABA/ITC methodologies)",
"⚡": "Algorithm Speed & Performance Optimization",
"🔧": "Production ML Pipeline Engineering"
}
def ml_approaches(self):
return [
"Gradient Boosting (LightGBM, CatBoost)",
"Deep Learning (CNNs, Multi-view architectures, PointNet)",
"Classical ML (Random Forest, SVM)",
"Ensemble Methods",
"Transfer Learning"
]
def achievements(self):
return [
"Migrated forest inventory pipeline: R → Python",
"Optimized TreeIso algorithm: C++ → Python",
"Improved tree classification accuracy by 10%",
"Accelerated point cloud processing with chunking optimization",
"Built end-to-end LiDAR processing system (200k points in ~600ms)"
]
def optimization_mindset(self):
return "Balancing speed and accuracy - fast algorithms with high-quality results 🚀🎯"💻 View Tech Arsenal
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Python |
PyTorch |
Django |
FastAPI |
Next.js |
TypeScript |
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PostgreSQL |
Redis |
Docker |
Git |
Linux |
Tailwind |
Philosophy: "Fast algorithms with accurate results enable the best user experiences"
Performance Optimization:
- Chunking for large-scale data processing
- Algorithm complexity reduction
- Efficient data structures
- Code profiling and bottleneck elimination
- Parallel processing where applicable
Quality Assurance:
- Rigorous model evaluation and validation
- Confusion matrix analysis and bias correction
- Cross-validation and ground truth comparison
- Continuous accuracy improvement iterations
- Production-grade error handling
Recent Achievements:
- Point cloud processing: ~600ms for 200k points ⚡
- Classification accuracy: +10% improvement 🎯
- Pipeline migration with performance gains
- Real-time inference with maintained accuracy