About
Welcome! I teach classes in the Data Science and Analytics program and McCourt School of Public Policy at Georgetown University.
I received my PhD in Political Science from Columbia University in 2022, with a dissertation on using Large Language Models (LLMs) to trace the evolution of political thought. During the PhD I worked as an Adjunct Instructor in NYU's Center for Data Science and Research Assistant in Columbia's School of International and Public Affairs (SIPA).
My research in political science mainly pertains to the history of political thought and what I call "computational political theory" (Natural Language Processing + Network Analysis + Political Theory)
My research in economics focuses on empirical, text-analytic studies of monopsony and collective bargaining in modern and historical labor markets.
Publications
Unsupervised Extraction of Workplace Rights and Duties from Collective Bargaining Agreements
Elliott Ash, Jeff Jacobs, Bentley MacLeod, Suresh Naidu, and Dominik Stammbach
Proceedings of the 20th IEEE International Conference on Data Mining -- International Workshop on Mining and Learning in the Legal Domain (ICDM-MLLD). 2020.
Monopsony in Online Labor Markets
Arindrajit Dube, Jeff Jacobs, Suresh Naidu, and Siddharth Suri
American Economic Review: Insights, (2)1: 33-46. 2020.
How to Do Things with Translations: Cross-Lingual Phrase Embeddings for Translation Analysis
Jeff Jacobs
Ninth Annual Conference on New Directions in Analyzing Text as Data (TADA). 2018.
Information Cartography: Creating Zoomable, Large-Scale Maps of Information
Dafna Shahaf, Jaewon Yang, Caroline Suen, Jeff Jacobs, Heidi Wang, and Jure Leskovec
Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining (KDD), 1097-1105. 2013.
Metaheuristic Approaches to Resource Scheduling in Heterogeneous Wireless Networks
Cristy Duan, Honghao Gao, Jeff Jacobs, and Weixiang Wu
Joint Mathematics Meetings (JMM). 2012.
Evolving Musical Counterpoint: The Chronopoint Genetic Algorithm
Jeff Jacobs and James Reggia
Proceedings of the 2011 IEEE Congress on Evolutionary Computation, 11-16. 2011.
Education
Columbia University
- PhD in Political Science, Sep 2022
- MPhil in Political Science, May 2018
- MA in Political Science, May 2016
Stanford University
- MS in Computer Science, May 2014
University of Maryland
- BS in Computer Science, BS in Mathematics, BA in Economics, May 2012
Teaching
DSAN 5300: Statistical Learning
Mondays, 6:30-9pm, Car Barn 203
DSAN 5450: Data Ethics and Policy
Wednesdays, 3:30-6pm, Walsh 498
DSAN 5500: Data Structures and Algorithms in Python
Thursdays, 6:30-9pm, Healy 104
DSAN 5650: Causal Inference for Computational Social Science
Online Meetings (Zoom)
Previous Courses