406 citations · 429 across the 9 of their papers we have counts for
10 papers
Strategies for Deploying AI Agents in Production at Scientific User Facilities
Ming Du, Xiangyu Yin, Michael Prince +40
Agentic artificial intelligence (AI) is moving beyond research demonstrations toward production use at scientific user facilities, including light sources, neutron sources, nanosci…
Ranking Before Serving: Low-Latency LLM Serving via Pairwise Learning-to-Rank
Yiheng Tao, Yihe Zhang, Matthew Dearing +4
Efficient scheduling of large language model (LLM) inference tasks is critical for achieving low latency and high throughput, a challenge that is becoming increasingly acute with t…
Extracting Practical, Actionable Energy Insights from Supercomputer Telemetry and Logs
Melanie Cornelius, Greg Cross, Shilpika Shilpika +2
As supercomputers grow in size and complexity, power efficiency has become a critical challenge, particularly in understanding GPU power consumption within modern HPC workloads. Th…
Leveraging LLMs to Automate Energy-Aware Refactoring of Parallel Scientific Codes
Matthew T. Dearing, Yiheng Tao, Xingfu Wu +2
Large language models (LLMs) are increasingly used for generating parallel scientific codes, with a primary focus on generating functionally correct code. Recent work has focused o…
Generative AI Uses and Risks for Knowledge Workers in a Science Organization
Kelly B. Wagman, Matthew T. Dearing, Marshini Chetty
Generative AI could enhance scientific discovery by supporting knowledge workers in science organizations. However, the real-world applications and perceived concerns of generative…
Benchmarking large language models for materials synthesis: the case of atomic layer deposition
Angel Yanguas-Gil, Matthew T. Dearing, Jeffrey W. Elam +5
In this work we introduce an open-ended question benchmark, ALDbench, to evaluate the performance of large language models (LLMs) in materials synthesis, and in particular in the f…
LASSI: An LLM-based Automated Self-Correcting Pipeline for Translating Parallel Scientific Codes
Matthew T. Dearing, Yiheng Tao, Xingfu Wu +2
This paper addresses the problem of providing a novel approach to sourcing significant training data for LLMs focused on science and engineering. In particular, a crucial challenge…
Opportunities for Retrieval and Tool Augmented Large Language Models in Scientific Facilities
Michael H. Prince, Henry Chan, Aikaterini Vriza +6
Upgrades to advanced scientific user facilities such as next-generation x-ray light sources, nanoscience centers, and neutron facilities are revolutionizing our understanding of ma…
Analyzing the Performance of Graph Neural Networks with Pipe Parallelism
Matthew T. Dearing, Xiaoyan Wang
Many interesting datasets ubiquitous in machine learning and deep learning can be described via graphs. As the scale and complexity of graph-structured datasets increase, such as i…
Computer-Generated Holographic Optical Tweezer Arrays
Eric R. Dufresne, Gabriel C. Spalding, Matthew T. Dearing +2
Holographic techniques significantly extend the capabilities of laser tweezing, making possible extended trapping patterns for manipulating large numbers of particles and volumes o…