9 citations · 15 across the 13 of their papers we have counts for
7 papers · 1 filter
Health-Informed Computing: Estimating and Addressing the Public Health Impact of Data Centers
Yuelin Han, Zhifeng Wu, Pengfei Li +2
The surging demand for artificial intelligence (AI) has led to a rapid expansion of energy-intensive data centers, contributing to criteria air pollutant emissions and raising publ…
A Water Efficiency Dataset for African Data Centers
Noah Shumba, Opelo Tshekiso, Pengfei Li +2
Artificial intelligence (AI) computing and data centers consume large amounts of freshwater, both directly for cooling and indirectly for electricity generation. While most attenti…
Online Budgeted Matching with General Bids
Jianyi Yang, Pengfei Li, Adam Wierman +1
Online Budgeted Matching (OBM) is a classic problem with important applications in online advertising, online service matching, revenue management, and beyond. Traditional online a…
Towards Socially and Environmentally Responsible AI
Pengfei Li, Yejia Liu, Jianyi Yang +1
The sharply increasing sizes of artificial intelligence (AI) models come with significant energy consumption and environmental footprints, which can disproportionately impact certa…
Building Socially-Equitable Public Models
Yejia Liu, Jianyi Yang, Pengfei Li +2
Public models offer predictions to a variety of downstream tasks and have played a crucial role in various AI applications, showcasing their proficiency in accurate predictions. Ho…
A Dataset for Research on Water Sustainability
Pranjol Sen Gupta, Md Rajib Hossen, Pengfei Li +2
Freshwater scarcity is a global problem that requires collective efforts across all industry sectors. Nevertheless, a lack of access to operational water footprint data bars many a…
Online Allocation with Replenishable Budgets: Worst Case and Beyond
Jianyi Yang, Pengfei Li, Mohammad Jaminur Islam +1
This paper studies online resource allocation with replenishable budgets, where budgets can be replenished on top of the initial budget and an agent sequentially chooses online all…