I contributed to the Global Governance, Regulation, Innovation, Digital Economy (GRID) unit at CEPS, working on EU digital governance, especially the AI Act and the Digital Services Act, as well as transatlantic EU-US issues, including the CEPS Trade and Technology Dialogue. This work followed, following over two years of Brookings-CEPS collaboration, and being hosted by CEPS during my Fulbright-Schuman Innovation Scholarship.See my CEPS reports here:https://www.ceps.eu/ceps-publications/reconciling-the-ai-value-chain-with-the-eus-artificial-intelligence-act/https://www.ceps.eu/ceps-publications/the-ai-act-and-emerging-eu-digital-acquis/https://www.brookings.edu/research/the-eu-ai-act-will-have-global-impact-but-a-limited-brussels-effect/

A proud McCourt alumnus, I taught courses on data science and visualization for policy analysis, as well as a course on the governance of AI. I also support the MS in Data Science and Public Policy, which I proposed and designed for Georgetown in 2016. I previously taught at Georgetown from 2015 to 2017.

I'm studying the implications of artificial intelligence and emerging data technologies on society and governance. This includes how government should respond to AI harms, the need for transparency into technology companies, and how to build international consensus around AI policy issues. My research and writing can be found here: https://www.brookings.edu/experts/alex-engler/

While on my Fulbright-Schuman Innovation Award, I studied the EU digital governance ecosystem and worked on use of data science by the German federal government. In Brussels, I studied the global impact of the EU's Artificial Intelligence Act, and worked with the Center for European Policy Studies on analyses requested by the European Parliament.In Germany, I directly advised senior officials on German federal data policy, and performed a comprehensive analysis of the German government's use of data science: https://www.brookings.edu/research/institutionalizing-data-analysis-in-german-federal-governance/

I helped guide the emerging role of MDRC's Center for Data Insights.

I ran and taught in the M.S. in Computational Analysis and Public Policy program. This innovative and field-leading degree combines quantitative policy analysis curriculum with a series of applied computer science courses. The explicit goal of the program is to create a pipeline of graduates who are highly technical competent, yet also oriented towards public service and social good. The set of skills, as well as the depth of material, provided by this program is unique. It allows students to develop into top-tier policy analysts, data scientists, and civic technologists.In addition to running the program, taught several courses including Data Visualization for Policy Analysis and Large Scale Data Methods.

--Won a $600,000 grant from the Alfred P. Sloan Foundation, jointly with the Tax Policy Center (TPC), to use the cloud and modern data technology to scale the TPC microsimulation model. This project will enable many new applications of microsimulations and account for uncertainty in modeling estimates. --Won a $260,000 grant from the Alfred P. Sloan Foundation, jointly with the Housing Policy Center, to continue development and dissemination of big data framework, Spark Social Science;--Established the Urban Institute's big data infrastructure that allows for affordable, elastic, and accessible big data analysis for Urban researchers. The platform, using infrastructure in AWS EMR, spins up a spark cluster in 12 minutes that can be accessed through RStudio or Python notebooks;--Acted as statistical and programming lead for an Arnold Foundation grant to create synthetic version of IRS administrative data usable for public policy research;--Founded R Users Group, delivering trainings every other month on R and data science to Urban staff.

I was teaching in the Master of Science for Government Analytics program at Johns Hopkins University (JHU), and Georgetown University's McCourt School of Public Policy (GU). In addition, I proposed and designed the M.S. in Data Science and Public Policy at Georgetown University. I have also developed two new courses at Johns Hopkins University: Advanced Data Visualization - Interactive Web Graphics; and Data Science for Public Policy.Courses Taught: Advanced Data Visualization - Interactive Web Graphics in D3: Summer 2016 (JHU)Data Visualization for Policy Analysis: Summer 2014 (JHU), Fall 2014 (JHU), Spring 2015 (GU), Fall 2015 (GU), Spring 2016 (GU & JHU), Fall 2016 (GU), Spring 2017 (GU).Data Science for Public Policy: Spring 2015 (JHU), Fall 2015 (JHU)Data Visualization for Policy Analysis - Online: Summer 2015 (JHU).

I was a general purpose education data analyst, responsible for a wide range of analyses tied to school funding, enrollment, student performance, teacher efficacy, special education, adequate yearly progress and federal compliance.I was the lead analyst on DC CAS and NAEP, performing the data cleaning, verification, and analysis surrounding the state and nationwide tests.I ran OSSE's enrollment projections for the 2014-2015 school year, dramatically improving the quantitative process involved by incorporating school feeder patterns (with iterative multinomial logistic models), accounting for school enrollment caps (with Monte Carlo simulation), and adjusting for within year movement of students between schools.I was originally the second and later became the sole data analyst in charge of the storage and transformation (using custom ETL scripts built in R) of the data that powers LearnDC.org - OSSE's public facing education website.In addition, I managed the data side of the Mathematica contract for D.C.’s teacher value added model as well as acted as lead analyst for school and student growth metrics, including implementation of the Colorado Growth Model (Quartile Regression).In addition, I have built and utilized data infrastructure to power advanced analytical modeling, including determining expected attendance rates for schools based on their student population (weighted robust OLS) and defining clusters of schools (K-means clustering). This also necessitated expanding the available data used to such areas as new socioeconomic metrics and student commute data.

While at CRS, I performed quantitative data analysis in STATA and R on medical loss ratios and other health care industry finance issues.In addition, I contributed to reports and letters on the implementation of the Affordable Care Act, Electronic Health Records incentive programs, and other health care issues for distribution to Members of Congress and their staff.I also contributed data visualization to reports, including some that led to a report being given an award from the CRS Associate Directors.CRS Courses: Federal Budget Process; Budget Resolutions & Appropriations; Legislative Process.

I ran the online component of the Georgetown Public Policy Review (GPPR), overseeing 13 writers and editors. During my tenure, GPPR online has published research and writing that has been cited in prominent publications including the New York Times, Foreign Policy, Reuters and the Associated Press.My articles have been reposted and cited in such publications as Reuters, the Associated Press, the Huffington Post, Mother Jones, The Monkey Cage, Rick Hasen's Election Law Blog, Tax Prof Blog and others. You can see my writing here: http://gppreview.com/authors/gppr-writers/alex-engler/

I performed research and policy analysis on campaign finance, lobbying and political influence of the federal government, often using the statistical computing programs R and STATA, as well as a range of tools for data visualization. My research has been reported on in the Associated Press, the New Yorker and Politico Influence. In addition, I help to build (using Python) and improve online tools that make government more accessible, like Sunlight Foundation’s Scout - which tracks all legislative and regulatory action of the federal government and provides user-specific alerts. https://scout.sunlightfoundation.com/