Christopher M. Graziul

Christopher M. Graziul

Research Assistant Professor, Comparative Human Development

University of Chicago

Biography

My research examines the promise and limits of AI/ML-based tools to study policing, specifically how two-way radio use relates to officer coordination and behavior. This work seeks to clarify what happens before and during encounters with the public by making sense of these radio transmissions as an understudied sociotechnical system critical to the practice of policing. Since little work has explored how officers make use of this technology in practice, much of my work has involved navigating the lack of ethical guidelines around publicly available data which, nonetheless, contains sensitive information about members of the public.

By developing the tools needed to analyze police radio communications, my goal is to create opportunities for others to study radio policing and thus demystify routine police activity as well as identify trends and patterns relevant to public safety, public health, and related policymaking.

My aspirations include (a) development of participatory data governance policies for a national repository of policing data; (b) deployment of explainable models for processing unstructured policing data, specifically radio transmissions; (c) engagement with community members to both determine appropriate standards for responsible access to policing data and identify research questions addressing the needs and concerns of local residents.

I am currently on the tenure-track faculty job market, but I also love to do time-bounded projects! (Reach out if you need a team player with experience in management consulting, numerical optimization, and social science research!)

Interests
  • Computational Social Science
  • Critical Policing Studies
  • Urban Sociology
  • Social Networks
  • Spatial Analysis
Education

  • PhD in Sociology, 2016

    University of Chicago

  • MA in Social Science, 2005

    University of Chicago

  • BS in Mathematics, 2004

    Virginia Tech

  • BS in Physics, 2004

    Virginia Tech

Experience

 
 
 
 
 
University of Chicago
Research Assisant Professor
University of Chicago
August 2022 – Present Illinois

Responsibilities include:

  • Direct and oversee development of voice activity detection (VAD), automatic speech recognition (ASR), speech emotion recognition (SER), and natural language processing (NLP) strategies to determine the viability of using publicly available broadcast police communications to understand police behavior via Spencer’s phenomenological variant of ecological systems theory (PVEST).
  • Direct all aspects of manuscript development, from ideation to submission, across relevant fields and publication venues.
  • Mentor 15-20 (graduate) research assistants engaged in qualitative, quantitative, and computational tasks supporting theory-based data science
 
 
 
 
 
University of Chicago
Data Scientist
University of Chicago
July 2018 – July 2022 Illinois

Responsibilities included:

All aspects of quantitative and mixed methods research projects, including but not limited to production of IRB protocols, secondary data analysis, and experiment design, in service of the Urban Resiliency Initiative and in collaboration with on-site and off-site research teams.

  • Supported development of PVEST-informed mixed methods based research designs
  • Developed a high-level PVEST-based research strategy for the NSF-funded STEM-US Center
  • Assessed efficacy of an arts-augmented STEM curriculum on academic performance of 7th-8th graders
  • Evaluated assessment tool to identify the needs of HBCU STEM students in order to improve success of HBCU students in STEM careers
  • Contributed to multiple successfully funded grants to conduct transdisciplinary research between education researchers, social scientists, computer scientists, and physical scientists
 
 
 
 
 
Brown University
Postdoctoral Research Fellow
Brown University
July 2016 – June 2018 Rhode Island

Objective: Geocode full count census data from 1900 to 1940 for 69 cities

  • Calculate residential segregation measures via point, line, and polygon data
  • Assess state-of-the-art segregation measures given newly availabile point data
  • Design and implement ETL workflow using Python
    • Integrate legacy Python and R code into workflow
    • Develop metrics to evaluate data integrity/quality
    • Optimize process for scaling to 69 cities in 1930 (>30M records)
  • Use Python, Stata, and Matlab to…
    • Validate accuracy of digitized street names using additional data
    • Perform fuzzy string matching to correct street names using additional data
    • Extract Census block numbers from historic maps using machine vision
    • Report relevant metrics to assess process performance
  • Analyze residential segregation patterns in 1930 St. Louis for 10+ racial/ethnic groups