# pip install curiosity==∞
from datetime import datetime
from pydantic import BaseModel
class Keyhan(BaseModel, frozen=True):
"""
ML Engineer who treats research like spelunking—
you don't know what you'll find until you're deep enough
that turning back costs more than pushing forward.
"""
power_on: datetime = datetime(1995, 1, 7)
coords: tuple[float, float] = (32.6539, 51.6660)
role: str = "ML Engineer & Researcher"
fuel: str = "single-origin espresso, 18 g, 25 s, 93 °C"
principle: str = "Theory is the map; implementation is the territory."
operating_mode: str = "10-12 hours/day, no weekends. It's not work; it's the game."
focus: list[str] = [
"Deep Reinforcement Learning (DRL) for complex control",
"High-Performance Computing (HPC) & Custom Kernel Optimization",
"Financial AI & Quantitative Systems",
"Autonomous Agents & LLM Orchestration",
"Scalable MLOps, Data Pipelines & Production Deployment",
"Neural Architecture Search (NAS) & HPO",
]
stack: dict[str, list[str]] = {
"Languages": ["Python", "C++"],
"ML Frameworks": ["PyTorch", "TensorFlow"],
"RL Toolkits": ["Gymnasium", "RLlib", "Stable-Baselines3", "Tianshou", "CleanRL"],
"HPO & NAS": ["Optuna", "Custom NAS Frameworks", "Ray Tune"],
"Data Orchestration": ["Pandas", "NumPy", "Numba", "Parquet", "HDF5", "Apache Spark"],
"Databases": ["MongoDB", "InfluxDB", "SQL"],
"Deployment & MLOps": ["FastAPI", "Docker", "Git", "CI/CD", "Pydantic"],
"Web Automation": ["BeautifulSoup", "Playwright"],
"OS": ["Linux"],
}
def philosophy(self) -> str:
return """
The gap between a brilliant paper and a production-ready system is a chasm
of broken dependencies, hidden bottlenecks, and flawed assumptions. My work
lives in that chasm. I write the code that bridges it—transforming theoretical
edge into tangible, performant, and maintainable software.
"""| Category | Tools |
|---|---|
| Languages | |
| ML Frameworks | |
| RL Toolkits | |
| LLM & AI | |
| Data & Compute | |
| Databases | |
| MLOps & Optimization | |
| Deployment | |
| Web & Automation |
This chart shows how my projects build upon and connect with each other, forming a cohesive research and development ecosystem.
%%{init: {'theme':'base', 'themeVariables': { 'primaryColor':'#667eea','primaryTextColor':'#fff','primaryBorderColor':'#7C4DFF','lineColor':'#F8B229','secondaryColor':'#764ba2','tertiaryColor':'#1e3a8a','background':'#0f172a','mainBkg':'#1e293b','secondaryBkg':'#334155','tertiaryBkg':'#475569','textColor':'#f1f5f9','fontSize':'16px','fontFamily':'ui-monospace'}}}%%
graph TD
subgraph CoreLib["🔧 Core Libraries"]
A[Proxy Rotator]:::shipped
B[Tick Vault]:::shipped
C[SpaX]:::shipped
end
subgraph Frameworks["⚡ Frameworks & Guides"]
D[Lightning HPO Playbooks]:::polishing
E[LLM Benchmarking]:::planned
end
subgraph Applications["🚀 Application & Research"]
F[Financial Env]:::planned
G[CV/CL Agent]:::planned
H[Clean TS]:::planned
end
C -.->|Config & HPO| D
C -.->|Config & HPO| H
C -.->|Experiment Design| F
D -.->|Training & Eval| H
D -.->|Training & Eval| F
E -.->|Model Selection| G
A ==>|Resilience| G
B ==>|Data Source| F
classDef shipped fill:#4CAF50,stroke:#2E7D32,stroke-width:3px,color:#fff,font-weight:bold
classDef polishing fill:#FFC107,stroke:#F57F17,stroke-width:3px,color:#000,font-weight:bold
classDef planned fill:#64748b,stroke:#475569,stroke-width:2px,color:#fff
style CoreLib fill:#1e3a8a,stroke:#3b82f6,stroke-width:3px,color:#fff,stroke-dasharray: 5 5
style Frameworks fill:#7c2d12,stroke:#f97316,stroke-width:3px,color:#fff,stroke-dasharray: 5 5
style Applications fill:#581c87,stroke:#a855f7,stroke-width:3px,color:#fff,stroke-dasharray: 5 5
| Legend: 🟢 Shipped • 🟡 Polishing • ⚫ Planned |
| # | Project | Description | Status |
|---|---|---|---|
| 1 | Proxy Rotator | A production-ready library that seamlessly integrates proxy rotation into httpx clients (sync/async). Built for resilience and simplicity in web scraping and API automation workflows. |
🚀 Shipped |
| 2 | Tick Vault | High-fidelity financial tick data scraper for Dukascopy Bank (Swiss). Extracts raw, sub-second precision market data for quantitative analysis and backtesting. | 🚀 Shipped |
| 3 | SpaX | Pythonic, type-safe search space configuration for HPO, NAS, and ML experiment tracking. Declarative configs with conditional parameters, automatic validation, and zero boilerplate. Pydantic-based with native Optuna integration. | 🚀 Shipped |
| 4 | Lightning HPO Playbooks | Industry-standard examples and guides for model training, optimization, and research using PyTorch Lightning. Covers SOTA practices for NAS, HPO, distributed training, and production-ready ML pipelines. | 🔨 Finishing Touches |
| 5 | CV+CL Agent | An agentic framework that auto-generates tailored CVs and cover letters optimized for specific job postings. Combines LLM orchestration with structured outputs for efficient application workflows. | ⏳ Up Next |
| 6 | Financial Env | A blazingly fast, parallelized Gymnasium environment for algorithmic trading. Includes a placeholder reward function—the real one stays private (years of research aren't free). Built for large-scale RL training; production readiness TBD pending capital for full-scale experiments. | 📝 Planned |
| 7 | LLM Benchmarking | A practical guide for building use-case-specific LLM evaluation pipelines. Generic benchmarks mislead; this teaches how to design reliable, domain-aware benchmarks that actually reflect real-world performance. | 📝 Planned |
| 8 | Clean-TS | A modular, Pythonic reimplementation of canonical time-series architectures. Traditional TS codebases are archaic and opaque—this makes them readable, extensible, and reproducible. Requires ~1 month of polish before release. | 📝 Planned |