i build Systems & Tools for Analysis, Prediction, Visualization, & Simulation.
i also design, code, and deploy complete Machine Learning-based applications (e.g., anti-fraud filter, recommendation engine, monitoring/anomaly detectors), in the service layer, decoupled from the main app, but also distributed and (horizontally) scalable.
Machine Learning: in particular, recursive descent parser (CART/C4.5), Multi-layer Perceptron, 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, most recently using Apache Spark + HDFS + Kafka
Social Network Analysis & Visualization: using graph theoretic techniques for e.g., community detection, id of 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)
- apache spark
- Hadoop (v2. YARN)
- NumPy + SciPy + Matplotlib + pandas
- postgres + postgis
- HDF5 (& pytables, h5py)
- flask/werkzeug (python web framework)
d3.js (svg template primitives for rendering plots in the browser)
- git (& gitHub)
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