IEEE BigData 2026 Undergraduate and REU Consortium
Introduction
We are delighted to announce the Undergraduate and REU Consortium, to be held as part of the IEEE BigData 2026 Conference, December 14-17, 2026, Phoenix, Arizona, USA. This Consortium provides a platform for undergraduate researchers, including participants of NSF Research Experiences for Undergraduates (REU) Sites and similar programs, to showcase their innovative work in the field of big data and related disciplines.
Scope of Topics
We invite submissions of original research papers from undergraduate and REU students on topics related to big data, including but not limited to:
- Theoretical Foundations and Scaling Laws for Big Data and Large Models
- Statistical Learning and Optimization at Scale
- Data-Centric AI and Data Valuation
- Systems and Infrastructure for Training and Serving Large Models
- Distributed and Accelerator-based Computing for Big Data
- Stream and Real-time Processing Systems for Big Data
- Systems for Vector, Embedding, and Retrieval Workloads
- Energy-efficient and Carbon-aware Computing for Big Data
- Data Integration and Cleaning for Machine Learning
- Data Labeling, Annotation, and Weak Supervision
- Management of Unstructured, Multimodal, and Streaming Data
- Large-scale Recommendation and Personalization Systems
- Data Versioning, Lineage, and Observability
- Social Network Analysis and Community Detection
- Graph Mining and Graph Representation Learning
- Learned and Neural Retrieval for Big Data
- Graph- and Knowledge-Graph-based Retrieval
- Anomaly Detection and Spatio-temporal Pattern Mining
- Multimodal and Sensor/IoT Data Mining
- Self-supervised and Representation Learning for Big Data
- Generative AI for Data Synthesis and Augmentation
- Parameter-efficient Fine-tuning and Adaptation of Large Models
- Reasoning and In-context Learning for Data Analysis
- Active and Data-efficient Learning
- Uncertainty Quantification and Out-of-distribution Generalization
- Visualization and Interactive Analytics for Big Data
- Privacy-preserving Analytics, Federated Learning, and Differential Privacy
- Fairness, Accountability, Transparency, and Responsible AI
- Data Governance, Provenance, Watermarking, and Licensing for AI
- Security and Robustness of Big Data and ML Systems
- Data Exchange, Monetization, and Pricing
- Data Management for Pre-training and Fine-tuning of Foundation Models
- Data Deduplication, Filtering, and Curation for Pre-training
- Retrieval-Augmented Generation for Large Models
- Long-context Modeling, Memory, and Continual Learning
- LLM Agents and Agentic Workflows over Big Data
- Multimodal Foundation Models and Cross-modal Data
- Evaluation, Benchmarking, and Data-centric Testing of Foundation Models
- Model Compression, Distillation, and Quantization
- Complex Big Data Applications in Engineering, Healthcare, Finance, Education, Transportation, and Telecommunication
- Big Data Analytics in Government, the Public Sector, and Society
- Real-world Case Studies of Value Creation through Big Data Analytics
- Big Data for Climate, Energy, and Sustainability
- LLM- and Agent-powered Big Data Applications
- Big Data and AI for Scientific Discovery
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.
Eligibility
- Undergraduate students pursuing an academic degree at the time of submission are eligible to submit papers as first authors. Participants of NSF REU Sites/Supplements and similar programs, such as LSAMP, are particularly encouraged.
- Each submission must have at least one student author, who should be the presenter if the paper is accepted.
- Co-authorship with faculty members, mentors, or researchers is allowed, but the undergraduate student must be the primary contributor to the work.
Submission Format Requirements
- Submissions must adhere to the IEEE Computer Society Proceedings Manuscript Formatting Guidelines.
- Undergraduate/REU student research papers should not exceed 6 pages, including all figures, tables, and references.
- Please indicate the first author's status, undergraduate or REU participant, in the author affiliation of your submitted paper.
Important Dates
- Paper Submission Deadline: September 20, 2026, 11:59 PM AoE
- Notification of Acceptance: October 9, 2026
- Camera-Ready Paper Submission: October 24, 2026
- Symposium Date: TBD (some day between Dec 14-17, 2026)
How to Submit
Please submit your papers electronically through the Consortium's submission portal (IEEE BigData 2026 submission system, Undergraduate and REU Consortium track): https://wi-lab.com/cyberchair/2026/bigdata26/index.php. The review process is single-blind, meaning that reviewers remain anonymous, but authors are not. All papers accepted by this Consortium will be included in the Workshop Proceedings published by the IEEE Computer Society Press, made available at the Conference.
Camera-Ready Instructions
Camera-ready instructions and the camera-ready template will be provided here after acceptance notifications.
Program Agenda
Join us in shaping the future of big data research by sharing your insights and discoveries at the Undergraduate and REU Consortium. We look forward to receiving your submissions and to an engaging and enriching event at the IEEE BigData 2026 Conference.
For further information or any questions regarding submissions, please contact the Undergraduate and REU Consortium Co-Chairs.
The detailed program agenda will be announced closer to the symposium date.
Website built by the IEEE BigData Undergraduate and REU Consortium organization group.