activity
20152025
most citedFlow-Bench: A Dataset for Computational Workflow Anomaly Detection

3 citations · 14 across the 15 of their papers we have counts for

collaborators

15 papers

cs.AI2025

GridMind: LLMs-Powered Agents for Power System Analysis and Operations

Hongwei Jin, Kibaek Kim, Jonghwan Kwon

The complexity of traditional power system analysis workflows presents significant barriers to efficient decision-making in modern electric grids. This paper presents GridMind, a m…

cs.DC2025

Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling

Prachi Jadhav, Hongwei Jin, Ewa Deelman +1

High-Performance Computing (HPC) job scheduling involves balancing conflicting objectives such as minimizing makespan, reducing wait times, optimizing resource use, and ensuring fa…

math.NA2025

A study on the 1- inverse of tensors via the M-Product

Siran Chen, Hongwei Jin, Shaowu Huang +1

In this paper, we will study the issue about the 1- inverse, where , via the M-product. The aim of the current study is threefold. Firstly, the definition and c…

math.NA2024

On the extensions of the GD inverse of tensors via the M-Product

Hongwei Jin, Siran Chen, Shaowu Huang +1

We study extensions of the GD tensor inverse using the M-product. The aim of current research is threefold. In the first place, the tensor GD inverse under the M-product is introdu…

cs.LG2024★ 2 cited

ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain

Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna +70

This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learnin…

cs.SE2024★ 1 cited

Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning

Hongwei Jin, George Papadimitriou, Krishnan Raghavan +5

Anomaly detection in computational workflows is critical for ensuring system reliability and security. However, traditional rule-based methods struggle to detect novel anomalies. T…

eess.SY2024

Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement

Hongwei Jin, Prasanna Balaprakash, Allen Zou +5

The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts.…

cs.LG2023★ 1 cited

Self-supervised Learning for Anomaly Detection in Computational Workflows

Hongwei Jin, Krishnan Raghavan, George Papadimitriou +4

Anomaly detection is the task of identifying abnormal behavior of a system. Anomaly detection in computational workflows is of special interest because of its wide implications in…

cs.DC2023★ 3 cited

Flow-Bench: A Dataset for Computational Workflow Anomaly Detection

George Papadimitriou, Hongwei Jin, Cong Wang +5

A computational workflow, also known as workflow, consists of tasks that must be executed in a specific order to attain a specific goal. Often, in fields such as biology, chemistry…

math.RA2022★ 2 cited

The generalized inverses of the quaternion tensor via the T-product

Hongwei Jin, Peifeng Zhou, Hongjie Jiang +1

In this article, specific definitions of the Moore-Penrose inverse, Drazin inverse of the quaternion tensor and the inverse along two quaternion tensors are introduced under the T-…

math.RA2022★ 1 cited

The generalized inverses of tensors via the C-Product

Hongwei Jin, Shumin Xu, Hongjie Jiang +1

This paper studies the issues about the generalized inverses of tensors under the C-Product. The aim of this paper is threefold. Firstly, this paper present the definition of the M…

cs.LG2022

Orthogonal Gromov-Wasserstein Discrepancy with Efficient Lower Bound

Hongwei Jin, Zishun Yu, Xinhua Zhang

Comparing structured data from possibly different metric-measure spaces is a fundamental task in machine learning, with applications in, e.g., graph classification. The Gromov-Wass…

cs.LG2022★ 2 cited

Gromov-Wasserstein Discrepancy with Local Differential Privacy for Distributed Structural Graphs

Hongwei Jin, Xun Chen

Learning the similarity between structured data, especially the graphs, is one of the essential problems. Besides the approach like graph kernels, Gromov-Wasserstein (GW) distance…

math.RA2017★ 2 cited

On one-sided (B,C)-inverses of arbitrary matrices

Julio Benitez, Enrico Boasso, Hongwei Jin

In this article one-sided (b, c)-inverses of arbitrary matrices as well as one-sided inverses along a (not necessarily square) matrix, will be studied. In adddition, the (b, c)-inv…

math.SP2015

Lower and upper bounds for -eigenvalues of even order real symmetric tensors

Hongwei Jin, M. Rajesh Kannan, Minru Bai

In this article, we define new classes of tensors called double -tensors, quasi-double -tensors and establish some of their properties. Using these prop…