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GPU sharing between workloads is an e!ective approach to increase GPU utilization and reduce idle power waste. To minimize resource contention under GPU sharing, current architectures allow users to allocate core GPU compute resources exclusively to workloads. However, identifying the most e''cient GPU compute resource allocation for colocated workloads is challenging, as it requires balancing potential performance degradation and power savings. This paper presents a framework for finding the most energy-e''cient compute allocation for colocated workload pairs under NVIDIA MPS using lightweight prediction models. Experimental results, using a range of training, inference, and general CUDA workloads, demonstrate that our solution outperforms the equal sharing strategy by 35%, on average, and is within 1.5% of the o#ine optimal strategy. 

Coaches are vital for effective collaboration, but cost and resource constraints often limit their availability during real-world tasks. This limitation poses serious challenges in life-critical domains that rely on effective teamwork, such as healthcare and disaster response. To address this gap, we propose and realize an innovative application of AI: task-time team coaching. Specifically, we introduce Socratic, a novel AI system that complements human coaches by providing real-time guidance during task execution. Socratic monitors team behavior, detects misalignments in team members' shared understanding, and delivers automated interventions to improve team performance. We validated Socratic through two human subject experiments involving dyadic collaboration. The results demonstrate that the system significantly enhances team performance with minimal interventions. Participants also perceived Socratic as helpful and trustworthy, supporting its potential for adoption. Our findings also suggest promising directions both for AI research and its practical applications to enhance human teamwork. 

A systematic methodology for the quantification of lithium inventory is developed and the degradation mechanisms of high-voltage lithium batteries are revealed. 

Effective human-human and human-autonomy teamwork is critical but often challenging to perfect. The challenge is particularly relevant in time-critical domains, such as healthcare and disaster response, where the time pressures can make coordination increasingly difficult to achieve and the consequences of imperfect coordination can be severe. To improve teamwork in these and other domains, we present TIC: an automated intervention approach for improving coordination between team members. Using BTIL, a multi-agent imitation learning algorithm, our approach first learns a generative model of team behavior from past task execution data. Next, it utilizes the learned generative model and team's task objective (shared reward) to algorithmically generate execution-time interventions. We evaluate our approach in synthetic multi-agent teaming scenarios, where team members make decentralized decisions without full observability of the environment. The experiments demonstrate that the automated interventions can successfully improve team performance and shed light on the design of autonomous agents for improving teamwork.