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Projects

Selected technical work. Most platform code is proprietary, so the examples here are simplified sketches of the architecture rather than production source. For the healthcare platform, see Loom on the Research page.

Wine personalization engine

Firstleaf · Python, PyTorch, scikit-learn, PostgreSQL, Redis, AWS

A real-time recommendation platform running billion-parameter models 24/7 at millisecond latency. It helped drive 100× user growth, from thousands to 300K+ members.

  • 98%+ accuracy in wine-preference prediction from an ensemble ExtraTrees classifier (500 estimators)
  • Hybrid recommender: collaborative filtering, content-based models, and contextual bandits for exploration versus exploitation
  • Full MLOps/DevOps for continuous, real-time inference at millions of users with sub-second responses
  • Several industry awards for the personalization work
  • Rising customer satisfaction and retention year over year
Architecture sketch 
class PersonalizationEngine:
    def __init__(self, model_config):
        self.collaborative_model = CollaborativeFilteringModel()
        self.content_model = ContentBasedModel()
        self.contextual_model = ContextualBandits()
        self.feature_store = FeatureStore()

    def generate_recommendations(self, user_id, context):
        # Multi-arm bandit approach for exploration/exploitation
        user_features = self.feature_store.get_user_features(user_id)
        contextual_features = self.extract_context_features(context)

        # Ensemble of recommendation strategies
        collab_recs = self.collaborative_model.recommend(user_features)
        content_recs = self.content_model.recommend(user_features)
        contextual_recs = self.contextual_model.recommend(
            user_features, contextual_features
        )

        return self.ensemble_recommendations([
            collab_recs, content_recs, contextual_recs
        ])

Model explainability system

Tools that explain individual predictions by combining SHAP values, extracted decision paths, and natural-language summaries, so non-technical stakeholders can trust and act on model output.

Explainer sketch 
class ModelExplainer:
    def __init__(self, model, feature_names):
        self.model = model
        self.feature_names = feature_names
        self.explainer = TreeExplainer(model)

    def explain_prediction(self, instance):
        shap_values = self.explainer.shap_values(instance)
        decision_path = self.extract_decision_path(instance)
        explanation = self.generate_natural_language_explanation(
            shap_values, decision_path, instance
        )

        return {
            'prediction': self.model.predict(instance)[0],
            'confidence': self.calculate_confidence(instance),
            'explanation': explanation,
            'feature_importance': self.rank_feature_importance(shap_values),
            'decision_path': decision_path
        }

Utilities & infrastructure patterns

Representative building blocks from data-science and platform work.

# Dynamic Time Warping for time series analysis
class DTW:
    """Dynamic Time Warping implementation for chemistry time series."""

    def __init__(self, v1, v2, dist=lambda x, y: (x - y) ** 2):
        self.distance_matrix = self._calculate_distance_matrix(v1, v2, dist)
        self.cost_matrix = self._calculate_cost_matrix()
        self.distance = self.cost_matrix[-1, -1]

    @property
    def path(self):
        """Extract optimal alignment path."""
        return self._backtrack_optimal_path()

# Standardized data container for ML pipelines
@dataclass
class StandardizedData:
    preprocessor: preprocessing.StandardScaler
    data: pd.DataFrame
    standardized_data: pd.DataFrame = field(init=False)

    def __post_init__(self):
        self.preprocessor = self.preprocessor()
        self.standardized_data = pd.DataFrame(
            self.preprocessor.fit_transform(self.data),
            columns=self.data.columns,
            index=self.data.index
        )

# Kubernetes deployment pattern for an ML inference service
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ml-inference-service
spec:
  replicas: 3
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxUnavailable: 1
      maxSurge: 1
  template:
    spec:
      containers:
      - name: inference-api
        image: ml-inference:latest
        resources:
          requests:
            memory: "1Gi"
            cpu: "500m"
          limits:
            memory: "2Gi"
            cpu: "1"
        env:
        - name: MODEL_VERSION
          value: "v2.1.0"
        readinessProbe:
          httpGet:
            path: /health
            port: 8080
          initialDelaySeconds: 30

Technical writing

How I approach technical work

  1. Production first. Design for scale, reliability, and maintainability from day one.
  2. Measure everything. Rely on metrics, A/B tests, and impact measurement.
  3. Bridge capability and value. Connect engineering decisions to business outcomes.
  4. Keep learning. Adopt new tools deliberately, on top of proven patterns.