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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.


  • Machine Learning: in particular,
    • decision tree (CART/C4.5)
    • multi-layer perceptron (aka neural network)
    • support vector machine (SVM/SVR)
    • kernel machines
    • kNN/kdtree
    • probabilistic graphical models (eg, Markov Random Field)

  • Dimension Reduction Techniques
    • spectral decomposition (PCA & kPCA, kLDA)
    • Kohonen Map (self-organizing map)
  • ***ETL pipelines, akka stream; Apache Spark + HDFS + Kafka
  • Social Network Analysis & Visualization: using graph theoretic techniques for e.g., community detection, identify members essential for network health/growth; identify nascent sub-communities; (particular fluency GraphViz, the premiere tool for graph layout/visualization, NetworkX, the primary network analysis library for python, and d3).
  • Analysis & Modeling of Time-Dependent Data
  • Optimization: Combinatorial Optimization and Constraint-Satisfaction Programming
  • Numerical Methods: e.g., matrix decomposition, Monte Carlo techniques, Gaussian quadrature, finite difference methods
  • Data Modeling for "Non-Relational" systems (in particularly Redis and RethinkDB) and for RDBMS, almost exclusively Postgresql
  • geo-spatial data modeling, persistence, & computation: e.g., postgis (storage); d3.js (geo map visualization)


  • scala
  • akka & akka-stream
  • julia
  • python
  • cython
  • apache spark
  • Hadoop (v2. YARN)
  • javascript
  • R
  • NumPy + SciPy + Matplotlib + pandas

  • redis

  • postgres + postgis
  • HDFS
  • graphviz
  • d3.js (svg template primitives for rendering plots in the browser)

  • saltstack

  • vagrant
  • docker

  • git (& gitHub)

  • travis ci

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