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Jake Williams

Jake Williams

Assistant Professor

Information Science

Dr. Williams is an assistant professor of information science at Drexel University. Prior to joining Drexel in the fall of 2016, he was a postdoctoral researcher and faculty instructor at the University of California, Berkeley. He holds a PhD in mathematical science, in addition to an MS in applied mathematics and a BA in physics from the University of Vermont. His research and teaching interests center around data science, computational social science, natural language processing, mathematics, machine learning, scientific programming, and algorithms design. His published contributions to date have yielded a scalable framework for the extraction of generalized lexical units (phrases), which has opened the field of phrase-based text analysis, and led to the development of a current state-of-the-art algorithm for multiword expressions segmentation. His work has also successfully challenged the current major statistical theory of language production, dating back over 15 years. In addition to extending his work in linguistics and natural language processing, a large component of his current research is applied to social science, and is focused on understanding collective action and political events through social media. He has taught a number of courses in undergraduate mathematics, and graduate coursework in scalable machine learning. His current teaching focus is on the development of curricula in undergraduate data science and scientific programming.


  • PhD, Mathematical Science, University of Vermont (2015)
  • MS, Applied Mathematics, University of Vermont (2011)
  • BA, Physics, University of Vermont (2007)

Research/Teaching Interests

Data science, scientific programming, computational social science, computational linguistics and natural language processing, mathematics, machine learning, algorithms, and scalability.

Select Publications

  • Williams, J. R., Bagrow, J. P., Danforth, C. M., and Dodds, P.S. Text mixing shapes the anatomy of rank-frequency distributions. Physical Review E (2015)
  • Williams, J. R., Lessard, P. R., Desu, S., Clark, E. M., Bagrow, J. P., Danforth, C. M., and Dodds, P.S. Zipf's law holds for phrases, not words. Scientific Reports (2015)
  • Williams, J. R., Bagrow, J. P., Danforth, C. M., and Dodds, P.S. Identifying missing dictionary entries with frequency-conserving context models. Physical Review E (2015)
  • Clark, E. M., Williams, J. R., Jones, C. A., Galbraith, R. A., Danforth, C. M., and Dodds, P. S. Sifting Robotic from Organic Text: A Natural Language Approach for Detecting Automation on Twitter. Journal of Computational Science (2016)
  • Dodds, P. S., Clark, E. M., Desu, S., Frank, M. R., Reagan, A. J., Williams, J. R., Mitchell, L., Harris, K., D., Kloumann, I. M., Bagrow, J. P., Megerdoomian, K., McMahon, M. T., Tivnan, B. F., and Danforth, C. M. Human language reveals a universal positivity bias. Proceedings of the National Academy of Sciences (2015)

Awards & Recognition

  • Graduate research fellow (2009–2015)
  • John F. Kenney Award for Excellence in the Study of Graduate Mathematics (2014)
  • Graduate teaching fellow (2009–2013)
  • Graduate Teaching Assistant of the Year (nominee, 2011)