Research
We have organized our projects into three large domain areas: 1) social work, public health, and medical decision making, 2) conservation, and 3) safety and security. Our recent research on the combating the COVID-19 pandemic is also listed above as a fourth category. Please click on the icons above to view more specific projects.
We focus on fundamental research problems in computational game theory, machine learning, automated planning, intelligent agents and multiagent interactions driven by real world challenges, ensuring a virtuous cycle of research and real-world applications. Examples of research driven by real-world problems include algorithms that enable influence maximization in social networks under dynamism and uncertainty, challenges of learning and handling bounded rationality models of human (adversary) behavior from real-world data, solving massive scale games uncertainty, and others.
Pioneering and foundational research in Security games for public safety
First large-scale applications of machine learning and game theory for wildlife conservation: Based on our green security game model and algorithms, which combine machine learning with security games, we are the first to apply AI models for global anti-poaching project. 
Pioneering research for first large scale applications of social network algorithms for public health: We pioneered practical applications of algorithms on social networks for positive social interventions in low-resource communities. 
