The VIPER Scraper is an open-source system for scraping and ingesting multi-model data from Twitter and Instagram. The system includes You Only Look Once (YOLO) real-time object detection integration for Twitter, allowing users to analyze image data as it comes off the data stream. This is useful for uncovering relations between image data and natural language or other metadata, all of which is easily accessable with the VIPER system. The VIPER scraper was developed to support projects at the University of Oklahoma Data Analytics Lab, including the Visual Inspection of Personal Exposed Records (VIPER) project.
Worked as a part of the Dr. Yihan Shao Research Group from January
‘17 to August ‘17 to develop a deep neural network capable of predicting the
atomization energies of organic molecules. Traditionally, the calculation of a
molecule’s atomization energy is computationally expensive, slowing down the work
of researchers. However, by using DNNs it is possible accurately predict atomization energies, thereby freeing
computational resources and improving the workflow of the research team.
The neural network was developed using Python and Google Tensorflow and is accurate within a 2% error over the chosen set of organic molecules. Research problems include mapping coordinate representations of molecules into data formats suitable for program input, improving the structure and performance of the network, and effectively handling large data sets for training.
Take a look at the source code here.
AskUTD is an Alexa skill for UTD students providing live parking information,
crisis hotline numbers, contact information for various offices, and fun facts about UTD.
AskUTD was developed with Node.js, AWS Lambda, and the Alexa Skills Kit.
This hack won first prize for the OIT-AWS challenge!
GeoTwit is a data analysis workflow for temporal and geographic Twitter data. It uses Python and Tweepy to gather Twitter data, and uses Kepler.gl for visualization.