AI-Ahmed (Ahmed Nabil)

Visits LinkedIn Kaggle Stack Overflow Discord

# ┌──────────────────────────────────────────────────────────────┐
# │ 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()}")

🧠 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