Spring 2012

Spring 2012
by ani

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2

doug

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)
    • 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
    • Kafka/Zookeeper
  • 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
  • Analysis & Modeling of Time Series
    • decomposition
    • forecasting
    • schema design
  • Optimization
    • combinatorial optimization
    • csp
  • Numerical Methods
    • matrix decomposition
    • monte carlo techniques
    • Gaussian quadrature,
    • finite difference methods
  • Data Modeling
    • redis
    • rethinkDB
    • postgres
  • Geo-Spatial Data Modeling, Persistence, & Computation
    • postgis (storage, query, computation)
    • d3.js (geo map visualization)

toolchain:

  • 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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