[AISWorld] CFP for 2023 INFORMS Workshop on Data Science

[Mochen Yang]

Dear AIS World,

My name is Mochen Yang and I'm an organizer for the 2023 INFORMS Workshop
on Data Science. Could you please help send the following message to
AISWorld subscribers? I'm also attaching the PDF version of the CFP just in
case it's easier to send that. Much appreciated!

-----Beginning of message-----

The 2023 INFORMS Workshop on Data Science

Hosted by The INFORMS College on Artificial Intelligence

Conference date: Saturday, October 14, 2023

Location: Phoenix, Arizona

Conference website: https://sites.google.com/view/data-science-2023/



The INFORMS Workshop on Data Science is a premier research conference
dedicated to developing data science theories, methods, and algorithms to
solve challenging problems and benefit businesses and society at large. The
workshop invites innovative data science research contributions that
address business and societal challenges from the lens of statistical
learning, data mining, machine learning, deep learning, reinforcement
learning, network science, and artificial intelligence. The workshop
welcomes original research addressing non-trivial data analytical
challenges and problems in marketing, finance, supply chain, healthcare,
energy, cybersecurity, social network services, privacy, credibility, etc.
Contributions to novel methods may be motivated by insightful observations
on the limitations of existing data science methods to address practical
challenges or by studying entirely new data science problems. Similarly,
novel techniques may be inspired based on the unique characteristics of a
particular application environment. Research contributions on theoretical
and methodological foundations of data science, such as optimization for
machine learning and new algorithms for data mining, are also welcome.

Research Contributions May Include:

   -

   Models for data science and predictive analytics
   -

   Performance measures in data science with important practical
   implications
   -

   Computational methods for big data, text mining, and natural language
   processing
   -

   Innovative methods for social network analytics on individuals and firms
   -

   Data acquisition, cleaning, integration, and best practices
   -

   Data-driven methods for cybersecurity and data privacy problems
   -

   Prediction of rare events, anomaly detection, and fraud detection
   -

   Methods for induction and inference with missing values
   -

   Data-driven methods for effective risk management
   -

   Data science for healthcare: chronic disease management, preventative
   care, etc.
   -

   Data science for industrial applications: energy, education, finance,
   supply chain, etc.
   -

   Large-scale recommendation systems and social media systems
   -

   Visual analytics for business data in image and video formats
   -

   Mobile analytics and spatial-temporal data mining
   -

   Experiences with big data project deployments
   -

   Machine learning, reinforcement learning, and AI for business
   applications
   -

   Adaptation of emerging deep learning techniques, e.g., transformers,
   graph embedding approaches for targeted business applications
   -

   Generative AI and its various impacts on individuals, organizations, and
   societies.


Important Dates:

Paper Submission Open: May 19, 2023

Paper Submission Deadline June 30, 2023

Notification of Paper Acceptance: August 11, 2023

Early Registration Deadline (INFORMS early registration): August 31, 2023

Workshop Date: October 14, 2023

Information for Authors:

   -

   Conference submission website:
   https://cmt3.research.microsoft.com/WDS2023
   -

   Submissions in the form of complete papers or short papers are welcome.


   -

   Complete paper submissions should be a maximum of 10 pages, including
   tables and figures.
   -

   Short paper submissions (which could be extended abstracts or
   work-in-progress papers) should be a maximum of 5 pages, including tables
   and figures.
   -

   References (irrespective of complete paper or short paper submissions)
   do not count towards the page limit.


   -

   Use single-spaced text with 12-point font and one-inch margins on four
   sides, printable on 8.5 x 11-inch paper.
   -

   Submissions must be blinded. No author information should appear
   anywhere in the document.
   -

   INFORMS or Workshop on Data Science does not take ownership of paper
   copyrights.
   -

   When uploading papers to the submission portal, the authors can indicate
   whether or not the paper’s main contributor is a student (so as to be
   considered for the best student paper award).

Student Scholarship:

We will provide a scholarship to some student authors or student co-authors
of accepted workshop papers. This scholarship will cover the registration
fees for the INFORMS meeting and the INFORMS Data Science Workshop. More
details will be provided upon paper acceptance notifications.

Best Paper Awards:

The INFORMS College on Artificial Intelligence, which hosts the Workshop on
Data Science, will be sponsoring three categories of awards: the best
complete paper, the best short paper, and the best student paper.

Opportunity for Invited Journal Submission:

Selected full papers on relevant topics will also be invited to submit the
extended abstract to the Special Issue of Responsible AI and Data Science
for Social Good at INFORMS Journal on Computing. Details of the special
issue call for papers can be found at INFORMS Journal on Computing
<https://pubsonline.informs.org/page/ijoc/calls-for-papers> website.

Organizing Committee:

Honorary Chairs

Olivia Sheng, University of Utah

Alexander S. Tuzhilin, New York University

Conference Chairs

Jingjing Li, University of Virginia, jl9rf at virginia.edu

Xiao Liu, Arizona State University, xiao.liu.10 at asu.edu

Sagar Samtani, Indiana University, ssamtani at iu.edu

Program Chairs

Victor Benjamin, Arizona State University, Victor.Benjamin at asu.edu

Yingfei Wang, University of Washington, yingfei at uw.edu

Mochen Yang, University of Minnesota, yang3653 at umn.edu

Publicity Chairs

Konstantin Bauman, Temple University, tuh42084 at temple.edu

Dokyun (DK) Lee, Boston University, dokyun at bu.edu

Konstantina Valogianni, IE Business School, konstantina.valogianni at ie.edu

Junjie Wu, Beihang University, wujj at buaa.edu.cn

Local Chairs

Reihane Boghrati, Arizona State University, reihane.boghrati at asu.edu

Katsiaryna (Katja) Siamionava, Arizona State University,
katsiaryna.siamionava at asu.edu

Finance Chair

Brent Kitchens, University of Virginia, bmk2a at comm.virginia.edu

Webmaster

Liben Chen, University of Minnesota, chen7954 at umn.edu

------------End Message-------------

-- 
Mochen Yang
Assistant Professor
Department of Information and Decision Sciences
Carlson School of Management
University of Minnesota
Webpage: mochenyang.github.io
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