Chris Marokov

About Chris Marokov

Building on a decade of “data work” across all three branches of the Federal government, I currently serve as an advisor to Trademarks leadership at USPTO. In addition to directly supporting executives with performance, fees, and macro-economic models, I co-lead an informal research center where we focus on data integration and statistical modeling, including ML, across the organization: from operational processes to strategic planning to data-driven policies. The data and statistical work I lead also tracks external economic dynamics underlined by innovation, intellectual property, and e-commerce. I recently partnered with the Pacific Northwest National Laboratory to focus on graphs and Natural Language Processing, with the goal of building a Knowledge Graph as a one-stop knowledge/risk management tool. Prior to my current role, I served as a Statistician in the office of the U.S. Chief Statistician at OMB with primary focus on Big Data innovation across Federal statistical data collections. I helped improve data and research studies across the government by introducing innovative approaches like Social Listening and Computer Simulation. I also partnered with NASA and NSF-NCSES to transfer NLP and Knowledge Graph expertise in line with Evidence Act requirements for administrative data use and Learning Agendas. Before joining OMB, I was a lead Social Science Analyst for research, guiding the modeling work of the Federal Judiciary’s Federally Funded Research and Development Center at MITRE as well as performing statistical research on behalf of the U.S. Judicial Conference. In 2016, I worked with private companies to leverage Social Listening and Remote Sensing to inform the Presidential initiative on “Promise Zones”. Of note is the “cars-in-driveways” Remote Sensing technique I envisioned to analyze daily economic activity in neighborhoods. Similarly, at the UCLA Luskin Center for Innovation, I implemented a GIS buffer as a ratio of miles gained per minute of charging to visualize output capacity of existing EV charging infrastructure and inform future installation plans for cities and municipalities. In 2015, following an extensive training at the Open Source Center, I introduced OSINT and NLP at the Congressional Research Service. Outside of my immediate work responsibilities, I evaluate private sector proposals on AI/ML for the Joint Venture Partnership opportunity at NTIS, U.S. Department of Commerce. I am affiliated with the Center for Social Complexity at the GMU, College of Science and the Santa Fe Institute through coursework and research.

Experience

1. Program Advisor

   Dhs Science And Technology Directorate

   Apr 2023 — Present

Education

• UCLA Luskin School of Public Affairs

  Master of Public Policy, Econometrics and Quantitative Economics

  2010 — 2012

• George Mason University

  Doctor of Philosophy - PhD, Computational Social Science

  2019 — 2024

• Georgetown University School of Continuing Studies

  Professional Certifcate in Data Science, Data Science

  2017