Special Symposium 1:
Undergraduate and High School Symposium
1. Introduction
We are delighted to announce the inaugural Undergraduate and High School Symposium, to be held as part of the IEEE BigData 2024 Conference. This symposium aims to provide a platform for young researchers to showcase their innovative work in the field of big data and related disciplines.
2. Scope of Topics
We invite submissions of original research papers from undergraduate and high school students on topics related to big data, including but not limited to:
Big Data Science and Foundations
- Novel Theoretical Models for Big Data
- New Computational Models for Big Data
- Data and Information Quality for Big Data
- New Data Standards
Big Data Infrastructure
- Cloud/Grid/Stream Computing for Big Data
- High Performance/Parallel Computing Platforms for Big Data
- Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment
- Energy-efficient Computing for Big Data
- Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data
- Software Techniques and Architectures in Cloud/Grid/Stream Computing
- Big Data Open Platforms
- New Programming Models for Big Data beyond Hadoop/MapReduce, STORM
- Software Systems to Support Big Data Computing
Big Data Management
- Data Acquisition, Integration, Cleaning, and Best Practices
- Computational Modeling and Data Integration
- Large-scale Recommendation Systems and Social Media Systems
- Cloud/Grid/Stream Data Mining- Big Velocity Data
- Mobility and Big Data
- Multimedia and Multi-structured Data- Big Variety Data
- Compliance and Governance for Big Data
Big Data Search and Mining
- Social Web Search and Mining
- Web Search
- Algorithms and Systems for Big Data Search
- Distributed, and Peer-to-peer Search
- Big Data Search Architectures, Scalability and Efficiency
- Link and Graph Mining
- Semantic-based Data Mining and Data Pre-processing
- Search and Mining of variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data
Big Data Learning and Analytics
- Predictive analytics on Big Data
- Machine learning algorithms for Big Data
- Deep learning for Big Data
- Feature representation learning for Big Data
- Dimension reduction for Big Data
- Physics informed Big Data learning
- Visualization Analytics for Big Data
Data Ecosystem
- Data ecosystem concepts, theory, structure, and process
- Ecosystem services and management
- Methods for data exchange, monetization, and pricing
- Trust, resilience, privacy, and security issues
- Privacy preserving Big Data collection/analytics
- Trust management in Big Data systems
- Ecosystem assessment, valuation, and sustainability
- Experimental studies of fairness, diversity, accountability, and transparency
Foundation Models for Big Data
- Big data management for pre-training
- Big data management for fine-tuning
- Big data management for prompt-tuning
- Prompt Engineering and its Management
- Foundation Model Operationalization for multiple users
Big Data Applications
- Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication
- Big Data Analytics in Small Business Enterprises (SMEs)
- Big Data Analytics in Government, Public Sector and Society in General
- Real-life Case Studies of Value Creation through Big Data Analytics
- Big Data as a Service
- Big Data Industry Standards
- Experiences with Big Data Project Deployments
Submitted papers should present novel ideas, methodologies, algorithms, or applications in the realm of big data. Papers will be evaluated based on their technical quality, novelty, relevance, and clarity of presentation.
3. Eligibility
- Undergraduate students and high school students pursuing an academic degree at the time of submission are eligible to submit papers as first authors.
- Each submission must have at least one student author, who should be the presenter if the paper is accepted.
- Co-authorship with faculty members or researchers is allowed, but the student must be the primary contributor to the work.
4. Submission Format Requirements
- Submissions must adhere to the IEEE Computer Society Proceedings Manuscript Formatting Guidelines.
- Undergraduate student research papers should not exceed 6 pages, including all figures, tables, and references.
- High school student research papers should not exceed 5 pages, including all figures, tables, and references.
- Please highlight whether the first author is a high school student or an undergraduate student in the author affiliation of your submitted paper.
5. Important dates
- Paper Submission Deadline: Oct 1, 2024 11:59 PM AoE
- Notification of Acceptance: Nov 4, 2024
- Camera-Ready Paper Submission: Nov 20, 2024
- Symposium Date: TBD
6. How to Submit
Please submit your papers electronically through the symposium's submission portal. The review process is single-blind, meaning that reviewers remain anonymous, but authors are not. All papers accepted by this symposium will be included in the Workshop Proceedings published by the IEEE Computer Society Press, made available at the Conference.
7. Program Committee Members (TBD)
8. Presentation Agenda (TBD)
Join us in shaping the future of big data research by sharing your insights and discoveries at the Undergraduate and High School Symposium. We look forward to receiving your submissions and to an engaging and enriching event at IEEE BigData 2024 Conference. For further information or any questions regarding submissions, please contact the Undergraduate and High School Symposium Co-Chairs, Dr. Xuan Wang and Dr. Yanjie Fu, at ieee-bigdata-2024-undergraduate-high-school-symposium-g@vt.edu.
Special Symposium 2:
2024 National Symposium for NSF REU Research in Data Science, Systems, and Security (REU 2024 Symposium)
Collocated at the https://www3.cs.stonybrook.edu/~ieeebigdata2024/
December 15-18, 2024, Washington DC, USA
Description
Undergraduate research plays an important role in attracting our best undergraduates to
continue towards graduate education in the science and engineering fields. Publishing
research in a professional venue is part of the training for future researchers. The
National Science Foundation (NSF) provides support for undergraduate research within
the Research Experience for Undergraduates (REU) program. The goal of this
Symposium is to provide a venue for students to publish their research done as part of
the REU program. The symposium seeks original submissions in research areas that
are currently funded by the NSF's directorate for Computer and Information Science
and Engineering (CISE) or other related directorates. The research topics of this
symposium focus on Data Science, Systems, and Security. Research done by
undergraduate researchers without explicit funding sources or via funding from similar
programs such as Louis Stokes Alliances for Minority Participation (LSAMP) are also
eligible to submit.
The key requirement for this Symposium is that the submission's lead author must be
an undergraduate student or high school student. To promote research experiences in
K-12, high school students are also eligible to submit although it is highly recommended
such submissions are in collaboration with undergraduate students and/or faculty
mentors.
The same REU Symposium has been held in 2021, 2022 and 2023. More information can be found at
REU Symposium 2021,
REU Symposium 2022,
REU Symposium 2023.
The same REU Symposium has been held in Big Data 2019
(https://bigdataieee.org/BigData2019/SpecialSymposium.html),
Big Data 2018
(https://bigdataieee.org/BigData2018/SpecialSymposium.html),
and Big Data 2021
(https://bigdataieee.org/BigData2021/SpecialSymposium.html).
- Paper submission due date: Tuesday, November 1, 2024
- Decision notification: November 11, 2024
- Camera-ready due date: November 17, 2024
- Pre-registration: November 23, 2024
- Symposium: One day in December 15-18, 2024
Paper Submission
Authors are invited to submit full papers (maximal 10 pages) or short papers (maximal 6
pages) as per IEEE 8.5 x 11 manuscript guidelines (templates for LaTex, Word and PDF can be found at
IEEE Templates for Conference Proceedings).
All papers must be submitted via
the conference submission system for the symposium.
At least one author of each accepted paper is required to attend the symposium and
present the paper. All the accepted papers by the symposium will be included in the
Proceedings of the IEEE Big Data 2024 Conference (IEEE BigData 2024) which will be
published by IEEE Computer Society.
Program Chairs
- Dr. Xuechen Zhang, Washington State University, Organizer of the NSF REU Site on Advancing Data-Driven Deep Coupling of Computational Simulations and Experiments
- Dr. Xiaokun Yang, University of Houston-Clear Lake, Co-Organizer of the NSF REU Site on Advancing Data-Driven Deep Coupling of Computational Simulations and Experiments
- Dr. Xinghui Zhao, Washington State University, Co-Organizer of the NSF REU Site on Advancing Data-Driven Deep Coupling of Computational Simulations and Experiments
- Dr. Matthias K. Gobbert, University of Maryland, Baltimore County, Organizer of the NSF REU Site on Online Interdisciplinary Big Data Analytics in Science and Engineering
- Dr. Jianwu Wang, University of Maryland, Baltimore County, Co-Organizer of the NSF REU Site on Online Interdisciplinary Big Data Analytics in Science and Engineering
We will invite PIs of related REU Sites PIs to be our PC members to advertise the opportunity and review submissions.
| Name | Last Name | Institution |
|---|---|---|
| Prasad | Calyam | University of Missouri-Columbia |
| Chen | Cao | Pennsylvania State University Behrend |
| Arielle | Carr | Lehigh University |
| Tuan | Le | New Mexico State University |
| Enyue | Lu | Salisbury University |
| Kewei | Sha | University of North Texas |
| Xu | Shuai | Case Western Reserve University |
| Boyang | Wang | University of Cincinnati |
| Nansong | Wu | Sonoma State University |
| Hailu | Xu | California State University, Long Beach |
| Dongfang | Zhao | University of Washington |
| Jun | Zheng | New Mexico Tech University |
| Weiwei | Zhou | University of California Davis |