Are you passionate about helping governments and non-profits be more equitable and more effective with your AI/ML/Data Science skills? We currently have Post-Doc positions for recent PhDs in these areas and a passion for social impact. Post-Docs will support educational and training programs, lead applied research projects with government agencies and non-profits in education, public health, criminal justice, environment, economic development and international development, and conduct research in areas such as interpretability, bias and fairness, and other machine learning methods focused on problems in social sciences and public policy with a strong emphasis on releasing the work through open source tools, shared curriculum, and publications.
We are a team at Carnegie Mellon University (in Pittsburgh) across the School of Computer Science and the Heinz College of Public Policy focused on using AI, machine learning, and data science to have a positive and equitable impact on society. Our interdisciplinary group works at the intersection of research, practice, and education to improve society and public policy.
We typically tailor a set of activities based on the interests and future goals of post-doctoral fellows. Everyone in the group will participate in applied projects to get grounded in real problems and then depending on their interest and future career goals, focus on a research area, educational efforts, or outreach efforts.
Current Applied Projects:
- Improving public health in Mexico by identifying children at risk of missing important vaccinations for targeted outreach campaigns, in partnership with Fundación Carlos Slim;
- Working with the American Civil Liberties Union (ACLU) to support their efforts to promote legislative outcomes that improve individual liberties by developing tools that help them efficiently surface bills to take action on issue areas they care about;
- Identifying and connecting people in Allegheny County who are eligible but not enrolled in the Supplemental Nutrition Assistance Program (SNAP);
Current Research Focus:
- Research investigating the elicitation, definition, measurement, detection, and mitigation of bias and disparities in ML-assisted decision making systems in public policy contexts, including empirical studies expanding on our project work as well as the development of new tools. This work includes collaborators from across CMU.
- Explorations of ML “interpretability” methods to help improve the overall performance of human-ML systems, such as decisions about whether to implement or override specific model recommendations, identifying appropriate interventions for people in need, or debugging and refining models themselves. The focus here is in designing, conducting, and analyzing results from user studies in collaboration with government, nonprofit, and industry collaborators and designing and evaluating methods focused to improve outcomes for specific real-world use cases.
Responsibilities:
The specific focus and responsibilities for each postdoctoral fellow in the group may vary with the person’s preferences and the group’s needs, but will generally include:
- Playing a key technical role in at least one of the applied projects described above (or similar projects as they arise)
- Coordinating with the government and non-profit partners for these projects, including scoping, ongoing updates, field trials & evaluation, and eventual implementation & deployment
- Participating in research focused on bias and fairness in machine learning, model explainability, or methods that are tailored to the nuances of problems in public policy
- Contributing to open source tools for building, evaluating, and mitigating disparities in machine learning models built for social good applications
- Participating in educational programs, including courses at CMU and/or our summer Data Science for Social Good Fellowship
- Mentoring students and research associates who work with the group
Qualifications:
- PhD (or equivalent) degree required, preferably in computer science, statistics, or quantitative social sciences
- Strong Python experience
- Experienced in using databases
- Expertise in data analysis and machine learning using python is a plus, especially using modules such as statsmodels, scikit-learn, pandas, sqlalchemy, etc.
- Experience working on real-world problems and passion for making a social impact
Carnegie Mellon University offers full-time employees a wide range of benefits, including health, dental, vision, transit, and retirement, as well as parental leave and tuition benefits.
