ICCDA 2021 - 5th International Conference on Compute and Data Analysis (ICCDA 2021)

Website http://iccda.org/ | Edit Freely

Category Compute；Data Analysis

Deadline: December 20, 2020 | Date: February 02, 2021-February 04, 2021

Venue/Country: Sanya, China

Updated: 2020-11-26 16:39:55 (GMT+9)

Call For Papers - CFP

Abbreviation: ICCDA 2021

Website: http://iccda.org/

Date: Feb. 2-4, 2021

Location: Sanya, China

The International Conference on Compute and Data Analysis (ICCDA), is an annual conference hold each year. It is an international forum for academia and industries to exchange visions and ideas in the state of the art and practice of compute and data analysis.

The previous editions of ICCDA were held in Florida Polytechnic University, Lakeland, Northern Illinois University (NIU) DeKalb, University of Hawaii Maui College, Kahului, Silicon Valley, USA. ICCDA 2021 conference will be located in Sanya, China during February 2-4, 2021.

*Proceedings

Accepted and presented papers will be published into the ACM Proceedings (ISBN: 978-1-4503-8911-2), indexed by Ei compendex, scopus, etc.

*Keynote Speakers

Lili Qiu, The University of Texas at Austin, USA (ACM Fellow, IEEE Fellow, and ACM Distinguished Scientist)

Hai Jin, Huazhong University of Science and Technology, China (IEEE Fellow, CCF Fellow)

Zhiguo Gong, The University of Macau

*Invited Speakers

Yucong Duan, Hainan University, China

Lei Li, Hefei University of Technology, China

*Previous ICCDA

Past ICCDA papers were all published in the prestigious ACM proceedings:

ICCDA 2020, ISBN: 978-1-4503-7644-0, EI, Scopus indexing

ICCDA 2019, ISBN: 978-1-4503-6634-2, EI, Scopus indexed

ICCDA 2018, ISBN: 978-1-4503-6359-4, EI, Scopus indexed

ICCDA 2017, ISBN: 978-1-4503-5241-3, EI, Scopus indexed

*Submission Link

http://www.easychair.org/conferences/?conf=iccda2021

*Topics

Mathematical, probabilistic and statistical models and theories

Machine learning theories, models and systems

Knowledge discovery theories, models and systems

Manifold and metric learning

Deep learning

Scalable analysis and learning

Non-iidness learning

Heterogeneous data/information integration

Data pre-processing, sampling and reduction

Dimensionality reduction

Feature selection, transformation and construction

Large scale optimization

High performance computing for data analytics

Architecture, management and process for data science

More topics: http://iccda.org/cfp.html

*Contact

Ms. Maggie Lau

iccda_info163.com

Wechat: iconf-cs