i build Systems & Tools for Analysis, Prediction, Visualization, & Simulation.

i also design, code, and deploy complete distributed and (horizontally) scalable Machine Learning-based applications (e.g., anti-fraud filter, recommendation engine, monitoring/anomaly detectors), often in the service layer decoupled from the main app.

Techniques:

• Machine Learning
  • decision tree (CART/C4.5) & random forest
  • deep learning (multi-layer perceptron)
  • support vector machine (SVM/SVR)
  • kNN/kdtree
  • probabilistic graphical models (eg, Bayesian Net, Markov Random Field)

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• Dimension Reduction Techniques
  • spectral decomposition (PCA & kPCA, kLDA)
  • Kohonen Map (self-organizing map)

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• ETL pipelines
  • akka stream
  • Apache Spark
  • Kafka/Zookeeper

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• Social Network Analysis & Visualization
  • using graph theoretic techniques for
    • community detection
    • identify members essential for network health/growth
    • identify nascent sub-communities
    • particular fluency in NetworkX, GraphViz

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• Analysis & Modeling of Time Series
  • decomposition
  • forecasting
  • anomaly detection

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• Optimization
  • combinatorial optimization
  • csp

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• Numerical Methods
  • matrix decomposition
  • monte carlo techniques
  • Gaussian quadrature,
  • finite difference methods

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• Persistence
  • redis
  • postgres

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• Geo-Spatial Data Modeling, Persistence, & Computation
  • postgis (storage, query, computation)

toolchain:

• scala
• scalaz
• akka & akka-stream
• python
• NumPy + SciPy + Matplotlib + pandas
• cython

• R

• vagrant

• git (& gitHub)

• travis ci

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