Jake Ryland Williams is an Associate Professor in the Department of Information Science at Drexel University's College of Computing and Informatics. With a background in mathematical sciences and physics, he joined Drexel in fall 2016 after serving as a postdoctoral researcher and faculty instructor at UC Berkeley. His academic journey spans multiple disciplines, bridging theoretical mathematics with practical data science applications. Education: PhD in Mathematical Science, University of Vermont (2015) MS in Applied Mathematics, University of Vermont (2011) BA in Physics, University of Vermont (2007) Williams' research spans data science, computational social science, natural language processing, and machine learning. His foundational work includes developing a scalable framework for extracting generalized lexical units that opened the field of phrase-based text analysis and challenging established statistical theories of language production. His work increasingly focuses on leveraging quantitative linguistics to improve foundation models while addressing societal impacts of generative AI and information warfare. Current research examines the intersection of social media, political events, and collective action through computational methods. His publication record demonstrates consistent contributions across natural language processing, social media analysis, and machine learning. The articles reveal a trajectory from foundational linguistic research toward increasingly applied work addressing societal challenges through computational methods, particularly focusing on social media analysis, language model optimization, and health informatics applications. His recent work reflects growing concerns about AI ethics and the societal impacts of information technologies. Teaching and Mentorship: Williams teaches across Drexel's data science curriculum, including doctoral course Foundations of Data Science (INFO 825), graduate courses Data Acquisition and Pre-Processing (DSCI 511), Data Analysis and Interpretation (DSCI 521), and Natural Language Processing with Deep Learning (DSCI 691), plus undergraduate Introduction to Data Science (INFO 103). His teaching philosophy emphasizes establishing core curricula while developing specialized electives focused on social computing applications and bias in data. Research Groups: Williams leads the CODED Lab, which focuses on social information and language processing with the perspective that technological systems facilitating public communication can be designed with open data for meta-purposes benefiting participants and organizations. He also founded the Text Processing Working Group (TPWG), which provides interactive demos, projects, and tutorials about text processing for students starting in Natural Language Processing.
