Stephanie Eckman

Stephanie Eckman

Researcher & Data Scientist

I have a Ph.D. in Statistics & Methodology. I collect high quality data for social science and model training

Skills

R coding
Statistics, Data Science, Machine Learning
International Data Collection

Experience

 
 
 
 
 
Principal Research Scientist
Amazon
February 2023 – Present Arlington, VA
 
 
 
 
 
Researcher and Data Scientist
Social Data Science Center, University of Maryland
February 2023 – Present College Park, MD
 
 
 
 
 
Fellow
RTI International
August 2015 – December 2022 Washington, DC
  • Conducting and publishing research into data quality
  • Mentoring junior researchers
  • Providing scientific leadership to the institute
 
 
 
 
 
Senior Researcher
Institute for Employment Research
July 2010 – July 2015 Nuremberg, Germany
  • Conducting and publishing research
  • Advising on design of IAB surveys
 
 
 
 
 
Chair of Sociology (Interim)
University of Mannheim
January 2013 – January 2014 Mannheim, Germany
  • Conducting and publishing research
  • Teaching courses in data analysis & research methods
  • Mentoring students
 
 
 
 
 
Methodologist
NORC at the University of Chicago
April 2001 – January 2010 Chicago, IL

Recent & Upcoming Talks

Use of Synthetic Data in Social Science
May 6, 2025 9:00 AM New York City, NY
Stephanie Eckman
Project Slides
Use of Synthetic Data in Social Science
Keynote: High Quality Training Data for AI Models: Lessons from 20 Years in Surveys
Whether future AI models are fair, trustworthy, and aligned with the public’s interests rests in part on our ability to collect …
Apr 1, 2025 12:00 AM Berlin, Germany
Stephanie Eckman
Project Slides
Keynote: High Quality Training Data for AI Models: Lessons from 20 Years in Surveys
The Science of Data Collection: Insights from Surveys can Improve Machine Learning Models
Whether future AI models make the world safer or less safe for humans rests in part on our ability to efficiently collect accurate data …
May 30, 2024 12:00 PM
Stephanie Eckman
Project Slides
The Science of Data Collection: Insights from Surveys can Improve Machine Learning Models
Annotation Sensitivity: Training Data Collection Methods Affect Model Performance
5 minute lightening talk on methods to collect annotations for NLP models
Aug 22, 2023 6:30 PM Arlington, VA
Stephanie Eckman
Project Slides Video
Annotation Sensitivity: Training Data Collection Methods Affect Model Performance
Bringing Survey Methodology to Machine Learning: Effects of Data Collection Methods on Model Performance
The instruments used to collect training data for machine learning models have many similarities to web surveys, such as the provision …
Jun 8, 2023 1:00 PM
Stephanie Eckman
Slides stephnie
Bringing Survey Methodology to Machine Learning: Effects of Data Collection Methods on Model Performance
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Research Projects

Feb 13, 2023
Data Quality, Data Centric AI

Helping data scientists collect more accurate training data, decreasing the cost and time needed to train models

Feb 14, 2023
Coverage & Selection Bias

My research has explored undercoverage bias in surveys around the world

Feb 13, 2023
Innovative Sampling Methods

Alternative sampling approaches which do not depend on up-to-date census data or interviewer involvement

Feb 13, 2023
Role of Incentives in Data Construction

The incentives of those producing data impact the quality of the data

Feb 13, 2023
Passive Data Collection

Data collected without the involvement of the respondent can improve quality

Additional Publications

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Stephanie Eckman, Barbara Plank, Frauke Kreuter (2024). Position: Insights from Survey Methodology can Improve Training Data. ICML.

PDF Project DOI

Florian Keusch, Sebastian Bähr, Georg-Christoph Haas, Frauke Kreuter, Mark Trappmann, Stephanie Eckman (2022). Non-participation in smartphone data collection using research apps. JRSSA.

PDF DOI

Stephanie Eckman, Jennifer Unangst, Jill Dever, Christopher Antoun (2022). The Precision of Estimates of Nonresponse Bias in Means. JSSAM.

DOI

Tobias Konitzer, Jennifer Allen, Stephanie Eckman, Baird Howland, Markus Mobius, David Rothschild, Duncan Watts (2021). Comparing Estimates of News Consumption from Survey and Passively Collected Behavioral Data. POQ.

PDF Project DOI

Christoph Kern, Stephanie Eckman, Jacob Beck, Rob Chew, Bolei Ma, Frauke Kreuter (2021). Annotation Sensitivity: Training Data Collection Methods Affect Model Performance. EMNLP.

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Contact

  • steph@umd.edu