Workshop Proposal of Foundational AI for Pervasive Computing (FairPC)

Sydney, Australia

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Introduction

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Data collected from sensors and embedded devices are becoming pervasive in numerous real-world applications, e.g., IoT devices, healthcare, wearable devices, smart vehicles, environmental sciences, etc. With massive amounts of data collected everywhere, we have entered an era for a much greater understanding of complex and fundamental challenges in diverse applications through Foundational AI for Pervasive Computing.

Through this workshop, we aim to provide a platform for researchers and AI practitioners from both academia and industry to discuss potential research directions, especially in foundational models, key technical issues, and present solutions to tackle related challenges in practical applications. We would like to discuss foundational models and large language models (LLMs) and their potential impacts on pervasive computing applications. We will invite researchers and AI practitioners from the related areas of machine learning, data science, statistics, econometrics, and many others to contribute to this workshop. The proposed workshop is well-aligned with the main conference focusing on data science, data mining, knowledge discovery, and topics in generative AI and LLMs.

Call for Papers

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Important Dates

Events	Dates
Paper Submission Due	February 22, 2025
Acceptance Notification	March 15, 2025
Camera Ready Due	March 29, 2025
Workshop Date	June 10, 2025

Submission Instructions

Paper submissions must be in English and adhere to the Springer LNCS/LNAI formatting guidelines. Submissions are handled electronically through PAKDD's CMT system in PDF format and will undergo double-blind review by the Program Committee based on technical quality, relevance, originality, significance, and presentation quality. Papers violating the Submission Policy or with modified author lists after submission will be rejected without review. Each paper must include an abstract of up to 200 words. Papers for the main or special track on large language models in data science must not exceed 12 pages, including references and appendices. The survey track papers may have up to 18 pages, with 15 for the main text and 3 for references.

Formatting Template: https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines

Contact

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The detailed information about our organizing committee members is below:

(Click on members' names to access their bio)

Beiyu Lin

beiyu.lin@ou.edu

She is an assistant professor in the Department of Computer Science at the University of Oklahoma. She focuses on mining sensor data and studying their diverse applications. Beiyu has received several honors and awards, such as Best Applied Data Science Paper Award at SDM 2021. She has organized several workshops and tutorials on AI, such as Google exploreCSR and SDM Tutorials.

Dongjin Song

dongjin.song@uconn.edu

He is an assistant professor in the Department of Computer Sci- ence and Engineering at the University of Connecticut. He has publications at top-tier data science and AI conferences, such as AAAI, IJCAI, NeurIPS, etc. He won the UConn Research Excellence Research (REP) Award in 2021. He has co-organized a series of Workshops.

Tianlong Chen

tianlong@cs.unc.edu

He is an assistant professor in the Department of Computer Science and Engineering at the University of North Carolina at Chapel Hill. He has publications at top-tier data science and AI conferences, such as EMNLP, ICML, etc. He received AdvML Rising Star in 2023 and other awards.

Jing Ma

jing.ma5@case.edu

She is a Timothy E. and Allison L. Schroeder Assistant Professor in the Department of Computer & Data Sciences at Case Western Reserve Univer- sity. Her research focuses on trustworthy AI, causal machine learning with publications in top venues such as NeurIPS, KDD, etc. She has received the SIGKDD Best Paper Award (2022), AAAI New Faculty Highlights (2024), etc.

Pan He

pan.he@auburn.edu

He is an Assistant Professor in the Department of Computer Science and Software Engineering at Auburn University. His research centers on machine learning, smart infrastructure, etc.

Sihong He

sihong.he@uta.edu

She is an Assistant Professor in the Department of Computer Science and Engineering at University of Texas, Arlington. Her research has focused on robust and data-driven interconnected CPS with publications at ICML, NeurIPS, etc.