Summary
Alina Arseniev-koehler is a computational social scientist and incoming Assistant Professor of Sociology at Purdue University who specializes in extracting signal from large, messy text corpora to study social bias in human and machine language. With nine years of experience spanning doctoral work at UCLA, postdoctoral training in biomedical informatics at UC San Diego, and applied research at Michigan, she combines NLP, machine learning, and social theory to measure stigma and social categories at scale. Her projects include curating a 4+ million–article health news corpus and developing validated methods for text data mining and visualization. She has taught and co-organized multiple computational social science summer institutes and led hands-on training in R, network analysis, and reproducible workflows. Uncommonly for a sociologist, she pairs deep domain expertise with production-oriented skills (e.g., TensorFlow LSTM models and large-scale corpus curation) that bridge academic research and biomedical informatics. Based in West Lafayette, she continues to pursue translational work that illuminates how language both reflects and shapes social inequality.
9 years of coding experience
8 years of employment as a software developer
Bachelor's degree, Sociology; Minor in Statistics, Bachelor's degree, Sociology; Minor in Statistics at University of Washington
Doctor of Philosophy - PhD, Sociology, Doctor of Philosophy - PhD, Sociology at University of California, Los Angeles