Data Analytics &
AI Engineer.

# Problem: Manual research processing is slow & leak-prone.
# Solution: Multi-Agent Local Pipeline (Llama 3.1 via Ollama)

def generate_whitepaper(topic, data_sources):
    # Parallelized Agent Allocation
    planner_node = Agent(role="Planner", llm=local_model)
    research_node = Agent(role="Researcher", tools=[web_search])
    writer_node = Agent(role="Analyst", llm=local_model)

    if len(data_sources) > 100:
        optimize_context_window(data_sources)
        
    workflow = LangGraphSchema([
        planner_node, 
        research_node, 
        writer_node
    ])
    
    return workflow.run(depth="comprehensive")
                        

// Problem: Double-spending votes in digital scopes.
// Solution: Cryptographic signature mapping

pragma solidity ^0.8.0;

contract Election {
    struct Candidate {
        string name;
        uint256 voteCount;
    }

    mapping(address => bool) public voters;
    mapping(uint => Candidate) public candidates;

    function vote(uint _candidateId) public {
        require(!voters[msg.sender], "Already Voted.");
        require(_candidateId > 0, "Invalid Candidate");

        voters[msg.sender] = true;
        candidates[_candidateId].voteCount++;
    }
}
                        

# Pattern validation via deep learning
class PulseLSTM(nn.Module):
    def __init__(self, input_size, hidden_size):
        super().__init__()
        self.lstm = nn.LSTM(input_size, hidden_size, 
                            batch_first=True, 
                            dropout=0.2)
        self.fc = nn.Linear(hidden_size, 1)

    def forward(self, x):
        out, _ = self.lstm(x)
        return self.fc(out[:, -1, :])

# Extracting feature transparency
import shap

def generate_explanation(model, X_train, user_data):
    # Generate SHAP values for business logic
    explainer = shap.TreeExplainer(model)
    shap_values = explainer.shap_values(user_data)
    
    return {
        "impact_scores": calculate_impact(shap_values),
        "baseline": explainer.expected_value
    }