Learning Collider
Jasmin Dial is a data scientist currently at Learning Collider since April 2022, with previous experience as a data analyst at Mission Asset Fund from April 2021 to April 2022 and as a research analyst at the NYC Department of Education from August 2018 to April 2021. Earlier roles include graduate teaching assistant at the Harris School of Public Policy at the University of Chicago, policy research intern at the NYC Department of Education, and research assistant at the University of Chicago Poverty Lab. Jasmin's career also features instructional and outreach roles at Game Theory Academy and tutoring experience with the Aspire Education Project. Educational credentials include a Master of Science in Computational Analysis and Public Policy from the Harris School of Public Policy and a Bachelor's Degree in Economics and Sociology from the University of California, Berkeley.
Learning Collider
Learning Collider is a social science research lab accelerating social impact through untapped data, technology platforms, and collaborative research. Our growing research portfolio spans sectors, applications, and geographies but common threads tie each research partnership and project together. Those threads include real-world research using real-time data intertwined with the opportunity to scale *real* social and economic mobility. In short, we’re researchers grounded in the real world, not the ivory tower. From Silicon Valley startups to remote learning in Botswana to hiring AI used by Fortune 500 companies, Learning Collider: - Connects to and analyzes high-dimensional, real-time data sourced from technology platforms owned and operated by public or private partners - Pairs, supports, and trains applied researchers to work in novel, evolving data environments - Builds research infrastructure within technology platforms to embed mechanisms for continuous testing, experimentation, and monitoring - Conducts rigorous studies focused on improving lives and livelihoods, i.e. scaling economic mobility interventions, designing fair algorithms, and building responsible technology Unlike many research labs, we operate largely outside of university systems, enabling flexibility in working with partners and research affiliates. This also positions us to connect researchers across institutions and geographic boundaries with high-value datasets and partners. Sharing our how-what-why is core to our work. Generally, technology and unintentional harm driven by technology are opaque. Here on LinkedIn, we share relevant and timely insights to support responsible and actionable tech decision-making, startup tech design, and policy-making.