About

I am a government professional with over a decade of data product consultant, leadership, project management, editing/writing and patent examination experience. I graduated in May 2018 with a Master’s of Science in Data Science from Indiana University. This blog covers a wide range of data topics presented with a non-technical audience in mind.


Laura H. Kahn

lkahn@indiana.edu | @LauraHKahn  | GitHub

Education

Indiana University, M.S. Data Science, January 2016 – May 2018
North Carolina State University, B.S. Textile Engineering and B.A. Spanish, August 1999 – May 2004
Universidad de Santander – Study Abroad, Spanish, September – December 2003
Universidad Technológica del Perú – Study Abroad, Spanish, June – August 2001

Experience

United States Patent and Trademark Office, Office of the Chief Information Officer (2007 – present)
Data product consultant responsible for disseminating bulk patent and trademark data (API, XML, TIFF and PDF images). Expert in extracting knowledge and insight from patent and trademark databases. Managed a team of technical librarians that assisted customers with electronic search tools.

United States Patent and Trademark Office, Patents Technology Center 3700 (2004-2007)
Conducted technology research for biomedical patent applications and communicated patentability findings to customers.

Indiana University (2016-2018)
Data science project experience in graduate program including Data Mining, Data Cleaning, exploratory Data Analysis, Statistics, Data Manipulation, Machine Learning, Feature Selection, Data Visualization.

  • Use of Artificial Neural Networks to Predict Poverty Indicators

Used multilayer perceptron neural networks to classify World Bank household survey features.

  • Algorithmic Trading of Coffee Futures with Machine Learning

Predicted daily coffee futures closing prices using Decision Tree Regression and Ridge Regression algorithms.

  • Morning Joe: Visualizing Coffee Rust, Production and Futures

Quantified and visualized the correlation between coffee rust, weather variables, production and futures prices in Brasil, Colombia and Papua New Guinea using regression techniques.

  • Show me the Money: Forecasting Economic Aid with Machine Learning

Predicted 2018 USAID economic aid disbursements with support vector machine, decision tree, Naive Bayes and k-NN classifiers; Poster presented at Jupyter Conference.

  • Spatiotemporal Twitter Analysis of the Venezuelan Food Crisis

Used phrase filtering and natural language processing techniques including Snowball stemmer, custom stopwords and Spanish corpus for annotating 1.32 million Tweets from Caracas, Venezuela and predicting the neighborhood where the Tweet originated; Research selected for SciPy Conference

Energy Use in the Middle East
Data munging, interpretation and visualization of energy consumption.

Data Science in 90 Seconds – YouTube monthly series that explains key data science principles in plain language for a general audience.

Skills

Python Numpy, Pandas, NLTK, Scikit-learn, Matplot, GGPlot and Pyplot libraries; Natural Language Processing; R; D3 JavaScript Library, Tableau and QGIS; Spark cluster computing system (ML library) analysis; R; SQL; Public Speaking; Customer Service.

Publications

Kahn, Laura H. “Spatiotemporal twitter analysis of the Venezuelan food crisis.” Journal of Food Processing Technology, 8:5. (2017): 51. Proceedings from the 2nd International Conference on Food Security and Sustainability. https://www.omicsonline.org/conference-proceedings/2157-7110-C1-062-011.pdf

 



DISCLAIMER

The findings, interpretations and conclusions expressed herein are those of the author. All content provided on this blog is for informational purposes only.

The author does not make representations as to the completeness of any information on this site or found by following any link on this site. The author will not be liable for any errors or omissions in this information nor for the availability of this information.

Many of the links on this blog will take you to sites operated by third parties. The author does not endorse these sites, their opinions, or any products they may offer. These third party links are offered to stimulate discussion and thinking on topics related to open governance, including, transparency, accountability, citizen participation and technology and innovation.

All of the content on this blog is intended for the personal, non-commercial use of our users.

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