Research Expertise: Analytics, Data Mining, Machine Learning, Social Media Analytics, Text Analytics, Natural Language Processing, Social Networks.
Recently (and reluctantly) added buzzwords: Big Data, Data Science, Artificial Intelligence
Older buzzwords that are trendy now: Machine Learning
Not so old buzzwords that are not trendy now: Data Mining
I’m interested in using computation, data and analytics for solving high impact social good problems in areas such as criminal justice, education, healthcare, energy, transportation, economic development, and public safety.
I am the Director of the Center for Data Science and Public Policy, Research Director and Senior Fellow at the Computation Institute and a Senior Fellow at the Harris School of Public Policy at the University of Chicago.
What I used to do: Chief Scientist at Obama for America 2012 campaign focusing on analytics, technology, and data. Senior Research Scientist and Director of Analytics research at Accenture Labs where I led a technology research team focused on applied R&D in analytics, machine learning, and data mining for large-scale & emerging business problems in various industries including healthcare, retail & CPG, manufacturing, intelligence, and financial services.
In my ample free time, I advise several analytics start-ups and non-profits, speak at, organize and participate in academic and industry analytics conferences, and publish in machine learning and data mining conferences and journals.
Historically, my research has spanned from general machine learning and data science to privacy preserving data analysis, text analysis, semi-supervised learning, active learning, information retrieval, Natural Language Processing, and knowledge management. Most of my work has focused on developing and using machine learning & data mining approaches to solve large-scale problems in corporate, political, and non-profit areas.
My current interests lie at the intersection of Machine Learning, Public Policy, and Social Sciences. I’m interested in solving large-scale and high impact social problems using data driven and evidence based methods. A lot of government, civic, and non-profit organizations are realizing the value of better data and have been focusing on improving data collection and data standardization. My goal is to build on these efforts, and work with these organizations to use this data to help improve outcomes. My work involves developing and using machine learning and social science methods that can be operationalized to solve policy and social challenges across health, criminal justice, education, public safety, criminal justice, social services, and economic development. My work has three areas of focus:
- Training: students and professionals in governments and non profits in the use of data driven methods (Machine Learning, AI, Data Science) for policy and social good
- Collaborative Projects: with governments and non profits to solve problems in policy and social good
- R&D: to develop new methods that are needed across policy areas with special emphasis on increasing fairness, reducing bias, making machine learning and AI algorithms and models more understandable and transparent.
My current projects include:
- working with police departments to build Early Intervention Systems that can help identify officers (and dispatches) at risk of adverse interactions with the public
- working with public health departments to do preventative lead inspections and reduce lead poisoning in children
- working with cities to improve home inspection processes to for early blight detection (and prevention) and/or to improve code compliance and health and safety violations in rental properties.
- working with jurisdictions to combine criminal justice, homelessness, mental health, and medical data to identify people in need of social services and public assistance programs and prevent future incarceration
- working with school districts to identify students in need of extra support to achieve different educational outcomes (high school graduation for example)
- comparing existing and developing new methods to make machine learning ad artificial intelligence methods and models more understandable and interpretable for use in policy problems.
- developing bias and fairness audit tools for machine learning models (See Aequitas, our new Bias and Fairness Auidit Tool)
- conducting a bias and fairness analysis across problems in education, health, criminal justice, and social services.