doug
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About

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)

- ***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 - anomaly detection
- ***Optimization*** - combinatorial optimization - csp
- ***Numerical Methods*** - matrix decomposition - monte carlo techniques - Gaussian quadrature, - finite difference methods
- ***Persistence*** - redis - postgres
- ***Geo-Spatial Data Modeling, Persistence, & Computation*** - postgis (storage, query, computation)

toolchain:

  • scala
  • apache spark
  • apache kafka
  • akka & akka-stream
  • R
  • python
  • NumPy + SciPy + Matplotlib + pandas
  • git (& gitHub)
  • travis ci

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