Author

Nicolas H. Christianson

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Sep 2026

Risk-Sensitive Peak-Aware Energy Scheduling: Competitive and Learning-Augmented Algorithms

Risk is a critical consideration in many energy-related problems, where stakeholders are often sensitive to rare but costly events. However, traditional online algorithms typically optimize expected cost and can perform poorly in such risk-sensitive settings. Motivated by this challenge, we study the design of algorith...

Lukas Himmelreich, Nicolas H. Christianson, A. Wierman · ACM SIGMETRICS Performance E... · 1 citation

#machine learning Preprint Sep 2026

DCEmbed: Scalable Optimization over Neural Surrogates

DCEmbed is proposed, a heuristic for optimization problems with embedded neural surrogates that leverages the difference-of-convex (DC) representation of the network and avoids adding activation binaries, and demonstrates much faster progress toward high-quality feasible solutions than approaches using exact mixed-inte...

Akshay Sreekumar, Nicolas H. Christianson, P. Donti et al. · 0 citations

Preprint Sep 2026

Multi-Scale Datacenter Power Modulation

A hierarchical receding-horizon controller is proposed that separates slow server reconfiguration from fast throttling recourse, and offers significant speedups compared to standard optimization solvers, computing near-optimal plans within a 20 second real-time control interval.

Akshay Sreekumar, Nicolas H. Christianson, Fiodar Kazhamiaka et al. · 1 citation