activity
20162025
most citedParsl: Pervasive Parallel Programming in Python

259 citations · 1.2k across the 51 of their papers we have counts for

collaborators

58 papers

q-bio.QM2025

Scalable Agentic Reasoning for Designing Biologics Targeting Intrinsically Disordered Proteins

Matthew Sinclair, Moeen Meigooni, Archit Vasan +14

Intrinsically disordered proteins (IDPs) represent crucial therapeutic targets due to their significant role in disease -- approximately 80\% of cancer-related proteins contain lon…

cs.DC2024★ 1 cited

Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows

Rafael Ferreira da Silva, Deborah Bard, Kyle Chard +108

The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensit…

cs.SE2024

ExaWorks Software Development Kit: A Robust and Scalable Collection of Interoperable Workflow Technologies

Matteo Turilli, Mihael Hategan-Marandiuc, Mikhail Titov +13

Scientific discovery increasingly requires executing heterogeneous scientific workflows on high-performance computing (HPC) platforms. Heterogeneous workflows contain different typ…

cs.DC2024★ 1 cited

Automated, Reliable, and Efficient Continental-Scale Replication of 7.3 Petabytes of Climate Simulation Data: A Case Study

Lukasz Lacinski, Lee Liming, Steven Turoscy +7

We report on our experiences replicating 7.3 petabytes (PB) of Earth System Grid Federation (ESGF) climate simulation data from Lawrence Livermore National Laboratory (LLNL) in Cal…

cs.DC2024

Steering a Fleet: Adaptation for Large-Scale, Workflow-Based Experiments

Jim Pruyne, Valerie Hayot-Sasson, Weijian Zheng +5

Experimental science is increasingly driven by instruments that produce vast volumes of data and thus a need to manage, compute, describe, and index this data. High performance and…

cs.CL2023★ 3 cited

Attention Lens: A Tool for Mechanistically Interpreting the Attention Head Information Retrieval Mechanism

Mansi Sakarvadia, Arham Khan, Aswathy Ajith +5

Transformer-based Large Language Models (LLMs) are the state-of-the-art for natural language tasks. Recent work has attempted to decode, by reverse engineering the role of linear l…