Research

I broadly work on designing, developing, deploying, and evaluating Machine Learning, Data Science, and AI to help tackle social and policy problems as well as on Responsible AI efforts for governance, policy, and regulation. My current research is focused on issues that come from the question “How do we design, build, deploy, and evaluate AI-Human collaborative systems for social and policy problems that can reliably help achieve fair and equitable outcomes?” and consists of three pillars:

1. Building AI systems designed to explicitly collaborate with humans to help them make better decisions (for social and policy problems)

How do we integrate AI with HCI and design approaches to build these systems? How do we augment the typical “prediction scores” with additional information that helps human users make better decisions? How do we make AI systems interpretable and explainable to users to help them improve their decision-making? How do we elicit and incorporate human feedback to improve decisions over time?

2. Designing them to deliberately support fair and equitable social outcomes.

My work is focused on embedding fairness and equity in the entire process of scoping, formulating, designing, developing, validating, and deploying AI systems. This includes developing methods for eliciting fairness values from different stakeholders, auditing AI systems for bias and fairness, designing them to be fair, and reducing bias. 

3. Designing and evaluating these systems to be reliable, robust and resilient to changing environments and policies

How do we explicitly build these systems to match their use and deployment settings? What is the appropriate model selection and validation methodology to make them robust to changes over time? How do we make them resilient to gaming? How do we create transparency and trust for different stakeholders including those who will be impacted by the system?

The work in my group is across three types of activities:

1. Collaborative Applied Projects: with governments, non profits, and industry to solve problems in policy and social good
2. 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
3. Research, Tools, and Methodology Development: to develop new methods and tools that are needed across policy areas with special emphasis on increasing fairness, reducing bias, making machine learning/AI algorithms and models more understandable and transparent.

Our areas of interest span health, criminal justice, education, public safety, workforce development, sustainability, transportation, social services, and economic development and we work closely with government agencies (local, federal, international) and NGOs.

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 in a fair and equitable manner. 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, social services, and economic development.