Hello DSSG Solve community!

I was a 2018 Data Science for Social Good Fellow at the University of Chicago, who worked with the Department of Labor in Chile to improve workplace safety by collaborating within a team to build a system that allows them to proactively identify those facilities that are likely to have labor law violations. I found the experience enriching and it was instrumental in determining my current work and research interests.

Currently, I am a Data Scientist and Applied NLP Researcher at Booz Allen Hamilton in Washington, D.C. In my position, I primarily work on machine learning and deep learning projects using unstructured data to solve various tasks for clients. My research focus is on the applications of attention-based models and determining local and global explainability in NLP models.

In my PhD, my research focuses on applied machine learning methods for macroeconomic policy questions, including NLP for central bank communication, combining econometric and machine learning methods to better forecast macroeconomic outcomes, and studying whether behavioral biases exist in trading in financial markets.

Personal information
Full name: Ancil Crayton
User name: ancilcrayton
Organization memberships
Not a member of any organization.
Awards
1+ task completed
First 1000 volunteers
Volunteer background
Education: PhD on Economics (University College Dublin)
LinkedIn profile
Github profile
Portfolio
Volunteer availability
Start date: None
End date: None
Hours available per week: 10
Volunteering interests
No stated preferences.
Skills
Skill
Beginner
Intermediate
Expert
Python
Computer Science (Algorithms)
Text Data (NLP)
Data in Text Files
Network/Graph Data
SQL
Data in Relational Databases
Social Science (Economics, Sociology, etc.)
Experience working on real-world problems using data
Data Visualization
Data Analysis
Time Series Models
Neural Networks / Deep Learning
Causal inference
Decision Trees
SVMs
Random Forests
Unsupervised models
Semi-Supervised models
Regression Discontinuity
Instrumental Variables
Matching (e.g., Propensity Score Matching )
Stata
Matlab
R

Volunteer projects

Project name Organization name Social impact area Project summary Task name Task status
Amphan: analyzing experiences of extreme weather events using online data International Water Management Institute Education Cyclone Amphan made landfall in South Asia on May 20, 2020. It was the most damaging storm in the history of the Indian Ocean, rendering hundreds of thousands of people homeless, ravaging agricultural lands and causing billions of dollars in damage. How were people affected by the storm? What were the responses of individuals, governments, corporates and NGOs? How was it covered by local, national and international media, as opposed to individuals' accounts? Who has created the dominant narratives of Cyclone Amphan; and whose voices go unheard? We aim to use online data -- such as Twitter posts, news headlines and research publications -- to analyze people's experiences of Cyclone Amphan. Project scoping done_all Completed
Amphan: analyzing experiences of extreme weather events using online data International Water Management Institute Education Cyclone Amphan made landfall in South Asia on May 20, 2020. It was the most damaging storm in the history of the Indian Ocean, rendering hundreds of thousands of people homeless, ravaging agricultural lands and causing billions of dollars in damage. How were people affected by the storm? What were the responses of individuals, governments, corporates and NGOs? How was it covered by local, national and international media, as opposed to individuals' accounts? Who has created the dominant narratives of Cyclone Amphan; and whose voices go unheard? We aim to use online data -- such as Twitter posts, news headlines and research publications -- to analyze people's experiences of Cyclone Amphan. Project management done_all Completed