Abstract
Alongside high blood pressure and blood sugar, ambient air pollution such as fine particulate matter is a leading public health risk factor worldwide, costing more than 6% of global GDP. Despite years of progress in air quality management, the energy sector remains a key source of harmful pollutants, which can travel hundreds of miles, penetrate human lungs, and contribute to serious health outcomes, including asthma and cancer. Addressing these health impacts requires optimal resource management on both the supply and demand sides, presenting complex algorithmic challenges.
In this talk, I will focus on demand-side solutions for energy-intensive data centers—a rapidly growing industry projected to impose a $10-20 billion public health burden in the United States by 2028—and introduce AI-augmented decision-making to optimize data center resource management and mitigate public health risks. Specifically, I will present an algorithmic framework that incorporates domain expertise as a guardrail to empower AI-augmented decisions with reliability guarantees. Finally, I will discuss broader applications of our AI-augmented decision-making approach, including its use in optimizing healthcare resource allocation.
Bio
Shaolei Ren is an Associate Professor of Electrical and Computer Engineering at the University of California, Riverside. His research broadly focuses on AI for Good, addressing societal challenges such as public health and sustainability. His work has influenced AI governance frameworks adopted by international organizations such as the United Nations, UNESCO, and WHO, while also driving industry innovations, including the first real-time water footprint reporting tool for computing. He is a recipient of the NSF CAREER Award (2015) and several paper awards, including at ACM e-Energy (2024, 2016) and IEEE ICC (2016). He earned his Ph.D. from the University of California, Los Angeles.
