Abstract: The time at which renewable (e.g., solar or wind) energy resources produce electricity cannot generally be controlled. In many settings, however, consumers have some flexibility in their energy consumption needs, and there is growing interest in demand-response programs that leverage this flexibility to shift energy consumption to better match renewable production — thus enabling more efficient utilization of these resources. We study optimal demand response in a setting where consumers use home energy management systems (HEMS) to autonomously adjust…

Abstract: Large language models (LLMs) often concentrate their attention on a small set of tokens—referred to as attention sinks. Common examples include the first token, a prompt-independent sink, and punctuation tokens, which are prompt-dependent. Although these tokens often lack inherent semantic meaning, their presence is critical for model performance, particularly under model compression and KV-caching. Yet, the function, semantic role, and origin of attention sinks—especially those beyond the first token—remain poorly understood. In this talk, I’ll present a comprehensive investigation…

Abstract: Compute scaling has dominated the conversation with modern language models, leading to an impressive array of algorithms that optimize performance for a given training (and sometimes inference) compute budget. But as compute has grown cheaper and more abundant, data is starting to become a bottleneck, and our ability to exchange computing for data efficiency may be crucial to future model scaling. In this talk, I will discuss some of our recent work on synthetic data and algorithmic approaches to…

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