Building intelligent systems that bridge data and decision-making.

# Pipeline overview
def seizure_pipeline(eeg_signal):
    # 1. Bandpass filter (0.5–50 Hz)
    filtered = bandpass_filter(eeg_signal, lo=0.5, hi=50)
    
    # 2. Feature extraction — spectral power + wavelet coefficients
    features = extract_features(filtered)
    
    # 3. Inference — 1D-CNN + BiLSTM classifier
    prediction = model.predict(features)  # → {seizure: 0.95, normal: 0.05}
    
    return prediction

# Workflow trigger → process → notify
@app.post("/webhook/process")
async def handle_event(payload: EventPayload):
    # 1. Validate & enrich incoming data
    enriched = await enrich_payload(payload)
    
    # 2. Execute business logic pipeline
    result = await pipeline.run(enriched)  # latency: -40%
    
    # 3. Fan-out notifications
    await notify(channels=["slack", "notion"], data=result)
    
    return {"status": "processed", "id": result.id}

class MultiHeadAttention(nn.Module):
    def __init__(self, d_model=512, n_heads=8):
        super().__init__()
        self.d_k = d_model // n_heads
        self.W_q = nn.Linear(d_model, d_model)
        self.W_k = nn.Linear(d_model, d_model)
        self.W_v = nn.Linear(d_model, d_model)
    
    def forward(self, Q, K, V, mask=None):
        # Scaled dot-product attention
        scores = (Q @ K.transpose(-2, -1)) / math.sqrt(self.d_k)
        if mask is not None:
            scores = scores.masked_fill(mask == 0, -1e9)
        attn = F.softmax(scores, dim=-1)
        return attn @ V

# SPARQL query — infer all instances of a class via reasoning
PREFIX ont: <http://example.org/ontology#>

SELECT ?entity ?type WHERE {
    ?entity rdf:type ?type .
    ?type rdfs:subClassOf* ont:IntelligentAgent .
    FILTER(?type != owl:Nothing)
}
ORDER BY ?type