Graph-Theoretic Computation in Python
Channel: PyCon Thailand
Date: 2018-08-08

PyCon Thailand: https://2018.th.pycon.org/

Graph-Theoretic Computation in Python


Speaker: Poomjai Nacaskul, PhD, DIC, CFA

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Description:
Playing with Social Network, Geo-Spatial, Financial Flow, and Banking System Data: Graph-Theoretic Computation in Python
Abstract: Data scientists increasingly encounter problems whereby data objects exhibit network-like inter-connectivity. Graph-theoretic computation enables feature extraction from relationship graphs. This talk shows how easy it is to leverage readily available Python libraries/functions to perform such tasks
Description: Much of 'Big Data' revolution has to do with dealing with non-traditional datatypes and feeding machine learning engines with non-numerical variables. Just as Convolutional Neural Networks excel at object recognition task precisely because the convolution layers expressly capture grid-array information embedded in the 2-dimensional visual field, analysis involving network-patterned objects (individuals in an online social network, transportation nodes, banking accounts with payment network, etc.) stands to benefit from the ability to extract graph-theoretic properties from the network connectivity patterns, either to provide insights in and of themselves, or as pre-processing to further machine learning task down the line.


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Bio:
- (Bachelor's) Physics & Economics (double major), Case Western Reserve University
- (Master's) Operations Research (minor in Finance), Weatherhead School of Management
- (Doctorate's) Computational Intelligence & Operational Research, Imperial College, London
- (Bank of Thailand) Head of Quantitative Models & Financial Engineering, Financial Supervision Group
- (SCB - current) FSVP, Quantitative Models & Enterprise Analytics, Business Intelligence, Transformation Group