开会时间: 2024-10-06

会议地点: San Diego, CA, United States

网址：https://dsaa.co/

Key DatesKey Dates

DSAA Introduction

The IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA) is fully sponsored by the IEEE through the IEEE Computational Intelligence Society and technically sponsored by ACM through SIGKDD and the American Statistical Association. DSAA was initiated in 2014 as the first IEEE conference dedicated to data science and analytics and received strategic support from the IEEE Big Data Initiative. The IEEE Task Force on Data Science and Advanced Analytics (TF-DSAA) governs the DSAA conferences.

DSAAs have been hosted in Asia, America, and Europe iteratively by experienced high-profile general and program chairs. DSAA provides a premier data science forum that brings together researchers, industry/government practitioners, and end-users of big data for discussion and exchange of original findings, state-of-the-art achievements, and best practices on data-intensive statistics, mathematics, analytics, learning, computing, and informatics and their applications. DSAA sets up a high standard including 10-page submissions in the IEEE double-column format, double-blind review, high-profile general and program chairs, prestigious keynote speeches, a competitive acceptance rate, and the next-generation data scientist award.

DSAA solicits both significant theoretical and practical progress on data science and advanced analytics. DSAA has two main tracks: Research and Application, a series of Special Sessions, and a unique Trends and Controversies session, in addition to a journal track, a student poster track, an industry poster track, tutorials, and a pre-conference industry day with prestigious invited industry talks. DSAA special sessions substantially upgrade conventional workshops to encourage emerging topics in data science while maintaining rigorous paper review and selection criteria.

DSAA is competitively recognized by Google Metrics and conference ranking such as in the category of Theories of Computer Science by the China Computer Federation.

Organizers

General Chairs

Huan Liu    ，Arizona State University

Vipin Kumar，University of Minnesota

DSAA TopicsDSAA Topics

DSAA encourages submissions on original and significant theoretical research achievements and best practices of data-driven advanced analytics, computing, discovery, machine learning, management, optimization, statistics, and their applications. Typical topics include but are not limited to:DSAA encourages submissions on original and significant theoretical research achievements and best practices of data-driven advanced analytics, computing, discovery, machine learning, management, optimization, statistics, and their applications. Typical topics include but are not limited to:

Data science foundations

• Data characteristics and complexities
• Interactions, couplings, relations, and heterogeneities
• Factors, structures, relations and distributions
• Mathematical, probabilistic, and statistical theories and models
• Learning theories, models, and systems
• Information theories for analytics and learning
• Deep analytics and deep learning
• Cognitve, neural and human learning methods
• Intent and insight learning
• Inference, regularization and optimization

Analytics, learning, and optimization

• Heterogeneous and mixed analytics
• Multi-domain/media/modal/source/view/task learning
• NLP, text and document analysis
• Temporal, sequential and geo-spatial analysis
• Graph, tree, group and community analysis
• Web, online and network analysis
• Adaptive, continual, online, stream and real-time analytics
• Distributed, parallel and high-performing analytics
• Large-scale and scalable analytics
• Descriptive, predictive and prescriptive analytics

Infrastructure, management, and processing

• Data pre-processing, sampling and augmentation
• Feature engineering and transformation
• High-performance, parallel and distributed computing
• Analytical system architectures and infrastructure
• Heterogeneous data/information integration, matching and sharing
• Crowdsourcing, cloud and edge computing
• Post-processing and post-mining
• Human-learning interaction and interfaces
• Web, social web and distributed search
• Indexing and query processing
• Information and knowledge retrieval
• Personalized search and recommendation

Evaluation, applications, and tools

• Complexity, efficiency, effectiveness, and scalability
• Quality, fairness, bias, and evaluation
• Social and economic impact and actionability
• Presentation and visualization
• Analytical and visualization languages and toolkit
• Business and government analytics
• Online, mobile, IoT, social, living analysis
• Domain-specific applications
• Anomaly, fraud, exception, change, event and crisis
• Ethics, integrity and regulation
• Security, trust, diversity, and risk
• Privacy-preserving analytics
• Reproducibility, explanation and interpretability