About
Bilge Acun is a leading research scientist at Meta AI (FAIR) on the Systems for Machine Learning (SysML) team, specializing in sustainable AI, machine learning systems, and distributed computing to address the environmental challenges of scaling massive models amid exploding computational demands. A PhD graduate from the University of Illinois at Urbana-Champaign—where her work on power-efficient large-scale systems earned recognition including a cover feature in IEEE Computer and multiple patent-pending innovations—she has driven high-impact projects at Meta, co-authoring seminal papers like "Sustainable AI: Environmental Implications, Challenges and Opportunities" and "Beyond Efficiency: Scaling AI Sustainably," while contributing to frameworks for carbon-aware datacenter design, multi-modal generative AI systems, and benchmarks like DataPerf. With thousands of citations, keynote speeches at MLSys conferences, and a passion for optimizing AI's carbon footprint through renewable energy integration and efficiency gains, Acun continues to pioneer green computing solutions that enable responsible advancement of frontier AI technologies at global scale.