The SOLAR (Sustainability, Optimization, Learning, and Algorithms Research) Lab develops the algorithmic foundations for sustainable, reliable, and adaptive computing systems. As AI reshapes digital infrastructure, future systems must make intelligent decisions under uncertainty, adapt to changing conditions, and operate within real-world resource constraints. Our research develops tools for learning, optimization, and coordination that connect rigorous theoretical guarantees to practical systems across data centers, edge/cloud platforms, smart energy infrastructure, and adaptive networks. By studying the growing interaction between AI models, computing systems, and energy infrastructure, we aim to help build next-generation digital infrastructure that is efficient, resilient, and environmentally responsible.
Recent News
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January 19, 2026We’ve launched Research in the AI Era, a new seminar series on how AI is reshaping the research lifecycle across computer science—see details and the schedule at here.
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July 31, 2025The SOLAR Lab has received a new NSF grant to advance the theory and applications of corruption-robust online optimization.
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July 30, 2025The SOLAR Lab has received a new NSF grant on cooperative learning in heterogeneous edge networks.
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June 25, 2025Our vision paper on Toward Environmentally Equitable AI, is published in the July 2025 issue of Communications of the ACM.
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June 10, 2025Congratulations to Xutong Liu for having his paper selected as a best paper runner-up at ACM Sigmetrics 2025!
Open Positions
We are actively looking for well-motivated and talented students and postdocs to join our research group. If interested, please see our recent publications and active research projects, and if still interested please apply to our graduate program and mention our lab name.
Funding support
Our research is supported by a Google Research Faculty Award, an NSF CAREER Award, and other grants from NSF (SaTC-2512128, CNS-2533814, Expeditions-2325956, CNS-2102963, CNS-2106299, CPS-2136199, NGSDI-2105494, CNS-1908298), Department of Energy, Amazon, VMWare, and Adobe.
