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
- 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)
- akka & akka-stream
- apache spark
- Hadoop (v2. YARN)
NumPy + SciPy + Matplotlib + pandas
- postgres + postgis
d3.js (svg template primitives for rendering plots in the browser)
git (& gitHub)
- travis ci
Top Network Posts
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