<p>i build Systems &amp; Tools for Analysis, Prediction, Visualization, &amp; Simulation.</p>
<p>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.</p>
<p><b>Techniques:</b></p>
<ul>
<li><em><strong>Machine Learning</strong></em></li>
<li>decision tree (CART/C4.5) &amp; random forest</li>
<li>deep learning (multi-layer perceptron)</li>
<li>support vector machine (SVM/SVR)</li>
<li>kNN/kdtree</li>
<li>probabilistic graphical models (eg, Bayesian Net, Markov Random Field)</li>
</ul>
 <hr />
 - ***Dimension Reduction Techniques***
  - spectral decomposition (PCA & kPCA, kLDA)
  - Kohonen Map (self-organizing map)
<hr />
 - ***ETL pipelines***
  - akka stream
  - Apache Spark
  - Kafka/Zookeeper
<hr />
 - ***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*
<hr />
 - ***Analysis & Modeling of Time Series***
  - decomposition
  - forecasting
  - anomaly detection
<hr />
 - ***Optimization***
  - combinatorial optimization 
  - csp
<hr />
 - ***Numerical Methods***
  - matrix decomposition
  - monte carlo techniques
  - Gaussian quadrature, 
  - finite difference methods 
<hr />
 - ***Persistence***
  - redis
  - postgres
<hr />
 - ***Geo-Spatial Data Modeling, Persistence, & Computation***
  - postgis (storage, query, computation)

<p><b>toolchain:</b></p>
<ul>
<li>scala</li>
<li>apache spark</li>
<li>apache kafka</li>
<li>akka &amp; akka-stream</li>
<li>R</li>
<li>python</li>
<li>NumPy + SciPy + Matplotlib + pandas</li>
<li>git (&amp; gitHub)</li>
<li>travis ci</li>
</ul>
<p>no recruiters</p>

doug