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# ┌──────────────────────────────────────────────────────────────┐
# │           ahmed@research ~ $ python profile.py               │
# └──────────────────────────────────────────────────────────────┘

class AIQuantResearcher:
    def __init__(self):
        self.name     = "Ahmed"
        self.role     = "AI & Quantitative Researcher"
        self.location = "Egypt 🇪🇬"

    @property
    def research_stack(self) -> dict:
        return {
            "deep_rl"          : ["Policy Gradient Methods", "Actor-Critic Architectures",
                                  "Continuous Action Spaces", "e.g. DDPG, TD3, SAC"],
            "finance"          : ["Algorithmic Trading", "Market Microstructure",
                                  "LOB Dynamics", "Quantitative Strategy Design"],
            "probabilistic_ml" : ["Conformal Prediction", "Uncertainty Quantification",
                                  "Distribution-Free Inference"],
            "islamic_finance"   : ["Shariah-Compliant AI Strategies",
                                  "Riba-Free Portfolio Construction",
                                  "ML-Driven Equity Screening"],
            "llm_agentic"      : ["LangGraph", "LangChain", "PageIndex",
                                  "Agentic Pipelines", "RAG Systems"],
            "cloud_mlops"      : ["Google Cloud Platform (GCP)", "Cloud-native ML Workflows"],
            "competition"      : ["IMC Prosperity 4 — Active"],
            "game_theory"      : ["Ordinal Games", "Strategic Equilibria"],
        }

    def current_focus(self) -> str:
        return (
            "Quantification of Continuous Action Uncertainty in Reinforcement Learning "
            "and its Application to Islamic Finance Equity"
        )

ahmed = AIQuantResearcher()
print(f"👋 Welcome to {ahmed.name}'s profile")
print(f"🔬 Current Focus: {ahmed.current_focus()}")

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🧠 Research Interests

📈 Deep RL for Finance Policy gradient methods in continuous action spaces (e.g. actor-critic, deterministic & stochastic variants) Reward engineering for limit order book environments: liquidity gates, baseline formulations, mark-to-market terms Offline and online RL pipelines for algorithmic trading Implementation in JAX/Flax NNX with WandB experiment tracking 🔮 Probabilistic ML & Conformal Prediction Distribution-free uncertainty quantification via conformal prediction sets Coverage guarantees and calibration in non-exchangeable, financial time series settings Uncertainty-aware decision-making under model misspecification

☪️ Islamic Finance & Ethical AI Current Focus: Quantification of continuous action uncertainty in RL and its application to Islamic Finance equity Shariah-compliant strategy design: riba-free constraints, halal instrument selection ML-driven equity screening and portfolio construction for Islamic capital markets 🤖 LLM Agentic Systems Multi-agent orchestration with LangGraph and LangChain Retrieval-Augmented Generation (RAG) and PageIndex-based knowledge pipelines Cloud-native deployment of agentic workflows on GCP 📊 Quant Finance & Market Microstructure Reservation pricing, spread decomposition, and adverse selection modelling ML techniques for alpha generation, factor research, and systematic strategy design

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🛠️ Tech Stack

🐍 Languages

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🤖 ML / RL Frameworks

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🧩 LLM & Agentic Frameworks

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☁️ Cloud & MLOps

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🔬 Scientific Computing

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🏗️ Infrastructure & Tools

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📊 GitHub Stats

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🏆 Certifications

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_{Quantitative Finance · Deep RL · Islamic Finance Equity · Conformal Prediction · LLM Agentic Systems}