Institution profile

Argonne National Laboratory

Academic institutionnorthamerica · us
Official website
Research library414linked papers
Opportunities0open roles
Selected work

Representative Papers

Computational Grids

Oct 01, 1998International Conference on High Performance Computing for Computational Science

This paper addresses the challenge of coordinating geographically distributed, heterogeneous, and autonomous computing resources. Method: It systematically introduces the “computational grid” concept and architecture for building a scalable, secure, and transparent virtual supercomputer. Key innovations include resource virtualization, cross-domain trust mechanisms, a unified naming and scheduling model, a distributed middleware framework, resource discovery and scheduling algorithms, a prototype security authentication protocol based on the Grid Security Infrastructure (GSI), and a cross-platform communication standard. Contribution/Results: The work establishes the foundational paradigm of grid computing, providing both theoretical grounding and practical implementation blueprints. It directly enabled the development of core infrastructure—including the Globus Toolkit—and catalyzed the advancement of e-Science. Moreover, it served as a seminal intellectual precursor to modern cloud and edge computing paradigms.

363 citations9 influentialRead paper

Improving Flow Matching by Aligning Flow Divergence

Jan 31, 2026International Conference on Machine Learning

This work addresses the challenge that conditional flow matching (CFM) struggles to accurately recover the true data distribution dynamics when modeling probability paths. To overcome this limitation, the authors propose a novel approach that introduces a partial differential equation characterizing the discrepancy between learned and ground-truth probability paths. They formulate a joint objective that simultaneously optimizes both the flow field and its divergence, and for the first time establish a theoretical upper bound linking the total variation error of the probability path to the CFM loss and the divergence loss. This method enables concurrent matching of the flow field and its divergence, achieving significant performance improvements over standard CFM on tasks including dynamical systems modeling, DNA sequence generation, and video synthesis, while preserving computational efficiency during generation.

2 citationsRead paper

A practical guide to machine learning interatomic potentials – Status and future

Mar 01, 2025Current opinion in solid state & materials science

To address the practical challenge that non-expert researchers face in applying machine learning interatomic potentials (MLIPs), this work introduces the first industrial-grade, end-to-end MLIP practice framework. Methodologically, it systematically integrates state-of-the-art models—including GAP, M3GNet, NequIP, and Allegro—and unifies active learning, uncertainty quantification, and physics-informed constraint embedding, while establishing standardized protocols for data generation, model selection, interpretability validation, and cross-platform deployment. Its key contributions are: (i) the first formal definition of a practical MLIP construction paradigm and evaluation benchmark; (ii) the open release of a fully reproducible toolchain and implementation guidelines; and (iii) substantial improvements in generalizability and computational efficiency of MLIPs within molecular dynamics simulations—demonstrated successfully in alloy design and catalytic modeling. This framework effectively bridges the gap between ML research and applied computational materials science.

2 citationsRead paper

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

Jan 07, 2026arXiv.org

This work addresses the challenges of data movement and performance tuning in OpenMP GPU offloading by proposing a Codex-based autonomous coding agent. The agent employs a domain-knowledge-guided workflow that integrates hotspot analysis, explicit data layout planning, correctness gating, and performance-profile-driven iterative optimization to automatically translate sequential CPU code into efficient and reliable OpenMP GPU offload code. Evaluated on 31 kernels, the approach successfully generates runnable code for all cases, with 25 outperforming reference implementations. It achieves geometric mean speedups of 3× and 5× on the HeCBench and Rodinia benchmarks, respectively, and demonstrates high compilation and validation success rates in CUDA-to-OpenMP migration tasks.

1 citationsRead paper

AERO: An autonomous platform for continuous research

May 23, 2025

The COVID-19 pandemic revealed critical deficiencies in conventional public health data platforms—particularly regarding automation, continuity, and cross-sector collaboration. To address these gaps, we propose and implement an autonomous platform designed for continuous research, featuring the first end-to-end automated closed-loop architecture that integrates dynamic data governance and multi-stakeholder collaborative governance. The platform leverages Globus for secure, trusted data transfer and identity management, and GitHub for workflow versioning and CI/CD-driven automated execution. It supports fully automated acquisition, validation, transformation, analysis, and policy-governed sharing of surveillance data. Deployed in two real-world public health monitoring scenarios, the system demonstrates operational efficacy; scalability is validated via synthetic workload benchmarking. All system designs, source code, and experimental resources are openly released to ensure full reproducibility.

1 citationsRead paper
Recent publications

Latest Papers