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

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