WinDS 2017 : SIAM SDM'17 Workshop Women in Data Science
Call For Papers
CALL FOR PAPERS
First SIAM SDM Workshop Women in Data Science 2017 (WinDS)
April 27th, 2017, Houston, Texas, USA
The WinDS (Women in Data Science) workshop is a full-day event that will be held on April 27th in Houston, Texas in conjunction with SIAM International Conference on Data Mining (SDM 2017). This workshop event brings together female faculty, graduate students, research scientists and industry researchers for an opportunity to connect, exchange ideas and learn from each other in the field of Data Science. Underrepresented minorities, graduates, and undergraduates interested in pursuing data science, machine learning research are encouraged to participate. While most presenters should be women, everybody is invited to attend.
We will strongly encourage female students, post-docs and researchers in all areas of data mining, graph analytics, machine learning and applications in data science related to health, financial, sports, natural resource and so on.
Data science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, machine learning, data mining, and predictive analytics. Data science encompasses several areas such as data analytics, machine learning, statistics, optimization and managing big data.
WinDS will bring together women researchers and practitioners in the field to deal with the emerging challenges in processing both from theoretical and practical works on data science and advanced analytics.
General areas of interest to WinDS include but are not limited to:
- KDD Foundations and Data analytics,
- Machine learning and knowledge discovery
- Storage, retrieval, and search
- Privacy and security
- Applications, practices, tools, and evaluation
The full-day workshop will feature invited talks, contributed talks, and a short session on open problems and directions for future research.
The workshop solicits submissions for talks for both previously published and unpublished work. For unpublished work, authors can submit original work, unpublished ideas in the form of completed work or work-in-progress papers of up to 9 pages in length (excluding references). For previously published work, submitted papers must be no longer than 4 pages in length (excluding references). We particularly encourage papers that propose new research directions as well as interesting applications of data science.
We are using Easychair for submission: https://easychair.org/conferences/?conf=winds2017. All full papers accepted should have a maximum length of 9 pages (single-spaced, 2 columns, 10-point font, and at least 1" margin on each
side) while short papers should have a maximum length 4 pages (same specification than full papers). Authors should use US Letter (8.5" x 11") paper size. Papers must have an abstract with a maximum of 300 words and a keyword list with no more than 6 keywords. Authors are required to submit their papers electronically in PDF format (postscript files can be converted using standard converters).
We would like to encourage you to prepare your paper in LaTeX2e. Papers should be formatted using the SIAM SODA macro, which is available through the SIAM website. You can access it at http://www.siam.org/proceedings/macros.php. The filename is soda2e.all. Make sure you use the macros for SODA and Data Mining Proceedings; papers prepared using other proceedings macros will not be accepted. For Microsoft Word users, please convert your document to the PDF format.
All submissions should clearly present the author information including the names of the authors, the affiliations, and the emails. The main author should be a woman.
- Submission date: January 13th, 2017
- Notification date: January 30th, 2017
- Camera Ready: February 6th, 2017
- Ana Paula Appel - IBM Research - apappel AT br.ibm.com
- Marisa Affonso Vasconcelos - IBM Research - marisaav AT br.ibm.com
- Mirella M. Moro - Federal University of Minas Gerais (UFMG) - mirella AT dcc.ufmg.br
- Yasuko Matsubara - Kumamoto University - yasuko AT cs.kumamoto-u.ac.jp