This IEEE Task Force on Data Sciences and Advanced Analytics (TF-DSAA) creates a prestigious community to focus on research, education/training, development, engagement, business and applications of big data, data science, and advanced analytics. The TF-DSAA is affiliated with the the Data Mining Technical Committee, IEEE Computational Intelligence Society. The TF-DSAA is technically maintained by the The International Institute of Data and Analytics.


  • IEEE DSAA'2018 welcomes to you to Turin, Italy.
  • IEEE DSAA'2017 calls for papers, special sessions, tutorials, and next-generation data scientist award applications.
  • IEEE DSAA'2016 acceptance rate is lower ever, 20.2%!
  • Listen to the panel discussion video 1, video 2, video 3, and video 4 at DSAA'2016.
  • Listen to Prof David Donoho's DSAA'2016 keynote speech on 50 years of data science: past and future, video 1, video 2, video 3 and video 4.
This IEEE Task Force on Data Science and Advanced Analytics (TF-DSAA) fosters an inter- and trans-disciplinary research and practice network concentrating on the research and innovation, education and training, development and applications, economy and business, and profession and communities. TF-DSAA enables to build and develop the deep synergy between the related aspects and areas in statistics, mathematics, informatics, computing, big data, data science, and advanced analytics. The synergy will not happen naturally, and a technical committee will enable the inter-disciplinary and cross-domain interactions between relevant areas, domains and interested audience, and hence the creation of a big interdisciplinary community to address this important new scientific, social and economic agenda with unlimited potential. This task force aims to glue the relevant pieces into an integrated theme while address critical theoretical, technical and practical issues emerging in broad-based data science and big data disciplines, domains and applications. It also develops the communities of data science, big data and advanced analytics in a holistic and systematic way to address the trends, challenges, and opportunities in research, education, development and applications.
TF-DSAA Objectives
The TF-DSAA will take the following major roles and goals into consideration:
  • Forming the community infrastructure and communication platforms for data sciences and big data analytics
  • Producing a roadmap for research, education and development of data sciences and big data analytics
  • Fostering capabilities for creating technical standards and services in data science and big data analytics
  • Promoting research, education and development of data science and big data analytics, and
  • Establishing broad-based connections with relevant communities including ACM, IEEE, and ASA.
In particular, the TF-DSAA will aim to support the creation of unique research excellence and next-generation data scientists in the following areas:
Data-driven scientific research on significant and complex real-life problems: While there have been studies on performing machine learning and knowledge discovery from data, the TF-DSAA encourages original and high-impact research and development on significant real-life problems from scientific, nature-inspired, socio-economic and philosophical perspectives. This will support the communications and collaborations to produce new theories and solutions in complex mathematical and statistical, scientific, and natural problem-solving systems and methodologies that have not been addressed in the existing sciences, and lead to significant impact on economy, society, decision and future;
Complex big data understanding and analytics: While infrastructures such as map-reduce have been used for handling large scale data via parallelizable algorithms, there are still significant gaps in meeting the requirements of building computational intelligence from terabytes or even exabytes of data across media, domains and modals in aspects including representing, learning, analyzing, managing, processing, and presenting complex data with heterogeneity, coupling relationships, invisibility, high frequency and dimensionality. The TF-DSAA encourages significant research foci on such matters toward real-time, personalized, transformative and actionable problem-solving;
Reproducible data science applications and practices: Cutting edge data analytics algorithms have been continuously proposed and extensively explored in the academia, but many of them have not been developed in alignment with real-life challenges, few of them successfully applied in the real world to benefit our business, living, society and future. The TF-DSAA encourages the transformation of research and practice culture of inventing and applying the state-of-the-art data analytics methods into the business, government, economy, life, health and many other domains, to advance policy-making, decision-making, productivity, industry and economy transformation by extracting real values from data.
TF-DSAA Activities
The TF-DSAA supports the following events:
  • The annual IEEE International Conference on Data Science and Advanced Analytics (DSAA). More about DSAA.
  • The annual International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC), see TF-BESC
  • Tutorials, special sessions, and lecture series on big data, data science and advanced analytics in DSAA conferences;
  • Seminars and talks given by senior researchers and professionals in the areas of big data, data science, and advanced analytics hosted by respective DSAA members;
  • The annual Big Data Summit in Sydney.
  • Relevant conferences, workshops and forums in big data, data science, analytics, statistics, and computing.
TF-DSAA Organizers


Professor Longbing Cao University of Technology Sydney, Australia

Vice Chairs:

Professor Eric Gaussier University Joseph Fourier, France
Professor Vincent S. Tseng National Cheng Kung University, Taiwan
Professor George Karypis University of Minnesota, USA
Professor Bart Goethals University of Antwerp, Belgium
TF-DSAA Membership
If you are interested in
  • joining TF-DSAA as a task force member,
  • joining IEEE International Conference on Data Science and Advanced Analytics as a program committee member,
  • hosting IEEE DSAA conference in your country,
  • joining IEEE DSAA organizing committee,
  • or
  • having any suggestions and inquiries about TF-DSAA or IEEE DSAA conference,
please contact us by contacts(a)
TF-DSAA Contacts
Contact dsaatf(a) for
  • general inquiries,
  • membership,
  • conferences and activities, and
  • comments and suggestions
about the DSAA Task Force.