WinDS 2019 : Women in Web Data Science

Apologies for cross-postings


The Third Women in Web Data Science (WinDS) Workshop
https://sites.google.com/view/winds2019/

https://easychair.org/conferences/?conf=winds2019


Important Dates

Submission date: January 22, 2019
Notification date: February 21, 2019
Camera Ready: March 3, 2019


The Third Women in Web Data Science (WinDS) workshop is a half-day event that will be held on May 13-14, 2019 in San Francisco, US in conjunction with The Web Conference (WWW 2019).


This workshop aims to bring 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, and related research are encouraged to participate. While most presenters should be women, everybody is invited to attend.


We strongly encourage female participants --- students, post-docs, early-career and senior researchers --- in all areas of data mining, machine learning, and applications of data science related to health, finance, natural resources, and so on to participate.


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 big data management.


WinDS will bring together women researchers and practitioners in the field to bring emphasis and discuss the emerging challenges in data science and advanced analytics, including both theoretical and practical perspectives.


General areas of interest to WinDS include but are not limited to:

* KDD foundations
* Big data analytics
* Machine learning and knowledge discovery
* Big data processing, storage, retrieval, and search
* Privacy and security
* Applications, practices, tools, and evaluation


This year we have three different tracks:
Traditional Academic submission: authors can submit original work, unpublished ideas in the form of completed work or work-in-progress papers of at least 4 pages and up to 8 pages in length (including references).
Problems in the Data Science field that women face in your daily job: Work in progress on approaches in data science that analyze and tackle bias and increase data diversity or industry project/cases led by women. For this, authors can submit a two pages in length paper presenting the case and how it was solved to help other women.
Lighting Talk: women that are presenting at the main conference and would like to participate in the workshop by presenting their work can submit a one page abstract. We also accept abstracts of papers presented at other venues.

Papers must be submitted in PDF according to the new ACM format published in ACM guidelines (http://www.acm.org/publications/proceedings-template), selecting the generic ``sigconf'' sample. The PDF files must have all non-standard fonts embedded. Submissions must be self-contained and in English. Submissions that do not follow these guidelines, or do not view or print properly, may be rejected without review.
Microsoft Word users should convert their document to the PDF format for submission.

All submissions should clearly present the author information including the names of the authors, affiliations and the emails. The main author should be a woman.

The proceedings of the workshops will be published jointly with the conference proceedings.

Please use the following Easychair link for submission:

Workshop Chairs:

Ana Paula Appel
IBM Research - [email protected]

Marisa Affonso Vasconcelos
IBM Research - [email protected]

Francesca Spezzano
Boise State University (USA) - [email protected]

Célia Talma Gonçalves
CEOS.PP - ISCAP - Polytechnic Institute of Porto /LIACC - [email protected]