Welcome to the RAISE Lab @ Penn State!

The RAISE Lab is a group of Artificial Intelligence (AI) researchers within the College of Information Sciences and Technology at the Pennsylvania State University that make foundational contributions to the field of Responsible AI for Social Emancipation; our goal is to advance the state-of-the-art in AI tools and algorithms to solve critical challenges faced by marginalized communities around the world, while ensuring that our algorithms do not exacerbate existing health/social/economic inequities in society.

Our research is highly interdisciplinary; we closely collaborate with domain experts in public health, social work, agronomy, conservation, and public safety and security (among others) to develop an understanding of key societal issues in these domains; we then develop state-of-the-art AI tools and algorithms which address these societal issues. In particular, we conduct fundamental AI research in the sub-fields of spatiotemporal deep learning, social network analysis, game theory, and FAT-ML (fairness, accountability, and transparency in ML), while using techniques from multi-agent systems and operations research. Aiming to address the most pressing problems in current-day societies, the RAISE Lab intends to bridge the divide between theory and practice in AI research by not only providing strong methodological contributions, but also by building applications that are tested and deployed in the real world. These applications have fundamentally altered practices in the domains that we have worked in. A unique aspect of our research is that we spend a considerable amount of time in the field, whether it is in urban settings in Los Angeles, or in rural settings in Kenya, Ethiopia, and India, to translate theory into practice, and to ensure that our AI models and algorithms actually get deployed in the real-world.

Specifically, we work on advancing AI research motivated by the grand challenges of the American Academy of Social Work and Social Welfare and the UN Sustainable Development agenda. A particular interest of ours is focusing on problems faced by under-served communities around the world, and trying to develop AI-driven tools and techniques to tackle these problems. While developing these solutions, a key focus of our is to ensure that our algorithms do not exacerbate existing health/social/economic inequities in society.