KDD 2025 Panel on AI for Science

Vipin Kumar; Yan Liu; Aidong Zhang

doi:10.1145/3711896.3742464

KDD 2025 Panel on AI for Science

Vipin Kumar
, Yan Liu
, Aidong Zhang

Department of Computer Science and Engineering

University of Minnesota Twin Cities
University of Southern California

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Artificial Intelligence (AI) is rapidly reshaping the landscape of scientific discovery by enabling the development of novel models that tackle complex, data- and computation-intensive problems. Scientific challenges, in turn, provide rich, use-inspired settings that push the boundaries of AI research. This virtuous cycle is increasingly driven by cross-disciplinary collaboration, where advances in AI and domain sciences co-evolve to accelerate innovation. In this plenary panel, we will examine the opportunities and challenges in designing cutting-edge AI models for scientific discovery, and high- light the transformative potential of cross-disciplinary partnerships in shaping the future of both AI and science.

Original language	English
Title of host publication	KDD 2025 - Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	5988
Number of pages	1
ISBN (Electronic)	9798400714542
DOIs	https://doi.org/10.1145/3711896.3742464
State	Published - Aug 3 2025
Event	31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2025 - Toronto, Canada Duration: Aug 3 2025 → Aug 7 2025

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume	2
ISSN (Print)	2154-817X

Conference

Conference	31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2025
Country/Territory	Canada
City	Toronto
Period	08/3/25 → 08/7/25

Keywords

ai for science
artificial intelligence
machine learning

Access to Document

10.1145/3711896.3742464

Cite this

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title = "KDD 2025 Panel on AI for Science",

abstract = "Artificial Intelligence (AI) is rapidly reshaping the landscape of scientific discovery by enabling the development of novel models that tackle complex, data- and computation-intensive problems. Scientific challenges, in turn, provide rich, use-inspired settings that push the boundaries of AI research. This virtuous cycle is increasingly driven by cross-disciplinary collaboration, where advances in AI and domain sciences co-evolve to accelerate innovation. In this plenary panel, we will examine the opportunities and challenges in designing cutting-edge AI models for scientific discovery, and high- light the transformative potential of cross-disciplinary partnerships in shaping the future of both AI and science.",

keywords = "ai for science, artificial intelligence, machine learning",

author = "Vipin Kumar and Yan Liu and Aidong Zhang",

note = "Publisher Copyright: {\textcopyright} 2025 Owner/Author.; 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2025 ; Conference date: 03-08-2025 Through 07-08-2025",

year = "2025",

month = aug,

day = "3",

doi = "10.1145/3711896.3742464",

language = "English",

series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

publisher = "Association for Computing Machinery ",

pages = "5988",

booktitle = "KDD 2025 - Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining",

address = "United States",

}

Kumar, V, Liu, Y & Zhang, A 2025, KDD 2025 Panel on AI for Science. in KDD 2025 - Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 2, Association for Computing Machinery , pp. 5988, 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2025, Toronto, Canada, 08/3/25. https://doi.org/10.1145/3711896.3742464

KDD 2025 Panel on AI for Science. / Kumar, Vipin; Liu, Yan; Zhang, Aidong.
KDD 2025 - Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery , 2025. p. 5988 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - KDD 2025 Panel on AI for Science

AU - Kumar, Vipin

AU - Liu, Yan

AU - Zhang, Aidong

PY - 2025/8/3

Y1 - 2025/8/3

N2 - Artificial Intelligence (AI) is rapidly reshaping the landscape of scientific discovery by enabling the development of novel models that tackle complex, data- and computation-intensive problems. Scientific challenges, in turn, provide rich, use-inspired settings that push the boundaries of AI research. This virtuous cycle is increasingly driven by cross-disciplinary collaboration, where advances in AI and domain sciences co-evolve to accelerate innovation. In this plenary panel, we will examine the opportunities and challenges in designing cutting-edge AI models for scientific discovery, and high- light the transformative potential of cross-disciplinary partnerships in shaping the future of both AI and science.

AB - Artificial Intelligence (AI) is rapidly reshaping the landscape of scientific discovery by enabling the development of novel models that tackle complex, data- and computation-intensive problems. Scientific challenges, in turn, provide rich, use-inspired settings that push the boundaries of AI research. This virtuous cycle is increasingly driven by cross-disciplinary collaboration, where advances in AI and domain sciences co-evolve to accelerate innovation. In this plenary panel, we will examine the opportunities and challenges in designing cutting-edge AI models for scientific discovery, and high- light the transformative potential of cross-disciplinary partnerships in shaping the future of both AI and science.

KW - ai for science

KW - artificial intelligence

KW - machine learning

UR - https://www.scopus.com/pages/publications/105014327778

U2 - 10.1145/3711896.3742464

DO - 10.1145/3711896.3742464

M3 - Conference contribution

AN - SCOPUS:105014327778

T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

SP - 5988

BT - KDD 2025 - Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

PB - Association for Computing Machinery

T2 - 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2025

Y2 - 3 August 2025 through 7 August 2025

ER -

KDD 2025 Panel on AI for Science

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Keywords

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