[AISWorld] The 2018 NSF Sponsored Conference on Practice and Science of Public Participation in STEM Research on Data Enabled Science and Engineering

[Yuan Hong]

The Rutgers Institute for Data Science, Learning, and Applications 
(I-DSLA) is organizing an NSF-sponsored conference that fosters public 
participation within the context of data enabled science and 
engineering. The conference aims to bring together all of the 
stakeholders in data enabled science, technology and engineering: 
researchers, educators, students, and representatives from industry, 
government, and non-profit organizations. The conference will serve as a 
forum to enable connections and collaborations among all of these 
stakeholders so that they can identify challenges in terms of research 
and education, lay out a vision for the next generation of data enabled 
science and technology systems, and brainstorm solutions for some of the 
immediate challenges and issues.

As such, the conference will: highlight the existing research 
opportunities and challenges within this scope of data enabled science 
and engineering to the broader community; focus on new and emerging 
applications that can benefit from data enabled science and engineering; 
examine obstacles to enabling students, particularly ones from 
underrepresented and minority backgrounds, to have successful careers in 
data enabled science and engineering. In this context, one of our major 
goals will be to identify potential solutions we can implement by 
pioneering and implementing programs to make it a reality. We aim to 
have a special focus on envisioning the smart city of the future, which 
is one of the core priorities of the Newark city government.

Towards this, we are issuing a general call for participation, wherein 
participation goes beyond academic papers, and is rather characterized 
by a more open, interactive visionary forum that encourages aspirational 
concepts from stakeholders, industry perspectives, and citizen 
participation from within and outside of Newark. Short position papers 
and visionary opinion papers that discuss novel aspects of the solution 
from a technological or scientific perspective are both welcomed. 
Furthermore, discussion panels focusing on some of the sticking points 
that hamper progress in this area are also welcome.

Conference Structure and Format. Keynote Speakers, Panels, Position 
statements, and Breakout sessions.

Fundamental Science Questions. We aim to understand the barriers to 
public participation in data intensive scientific research and to 
explore the mechanisms in which public participation can drive 
scientific research in data science. The fundamental science questions 
that we expect to have addressed include the following:


 From a science perspective:

·What are the fundamental challenges in public participation in STEM 
research in general, and in particular, when dealing with a data 
intensive environment?

·How can citizens participate in identifying relevant data science 
problems that directly impact the local community?

·How can problem formulation and formalization be done in an iterative 
interactive setting with participation from citizens, domain experts, 
and academics?

·What are the cyber-physical system aspects of data enabled science, 
technology and engineering in an urban environment?

·How do you enable crowdsensing to empower public participation in an 
urban environment in a scalable, effective, and secure way?

·What are the citizen concerns with employing crowdsensing applications?

·How do citizens engage with local data sources, develop specific 
solutions, and generalize these to solve problems beyond the local 
scale; i.e., How can the citizen data scientist democratize big data?


 From an applications perspective:

·What are the key applications that can benefit from a data science 
perspective, especially from an urban metropolitan environment?

·What are the unique challenges posed by applications?

·What are the constraints in terms of solution strategies?

·How well do different solution strategies fit the environment and how 
can they be implemented and deployed in the community?

·What can be learned from previous successes and/or failures?


 From an education perspective:

·What are the basic skills needed by citizen data scientists to 
successfully democratize big data science?

·How can we develop a self-supporting learning community for both 
traditional STEM students and non-traditional STEM participants?

·How can universities provide a conduit between the public and 
government/business and serve as a fertile seeding place for new 
innovative solutions?

·How can we identify community opportunities for research in STEM and 
connect students to these activities?

·What are the barriers to successfully transitioning STEM students from 
2-year community (city/county) colleges to a traditional 4-year research 
university?



Abstracts/Position Papers.
We invite abstracts and/or position papers (up to 4 pages) addressing 
any or a combination of the above science questions. Authors of selected 
papers will make a 15-min presentation at the conference. Papers not 
selected for presentations at the conference will be considered for a 
poster session.

Submission Site.
https://easychair.org/conferences/?conf=ppsrdese18

Panels.
If you would like to propose a panel, please contact the organizers Dr. 
Nabil Adam (adam at newark.rutgers.edu) and Dr. Jaideep Vaidya 
(jsvaidya at business.rutgers.edu).


Important Dates.

·December 15, 2017 – Deadline for submitting abstract/position papers.

·January 10, 2018 – Author notification

·February 8-9, 2018 – Conference


Travel and Lodging Support.

Requests for travel and lodging support will be considered upon request.


Program Committee (partial, tentative)

·Nabil Adam, I-DSLA, Rutgers (Chair)

·Robert Wieder, (Co-Chair), Rutgers

·Jaideep Vaidya, (Co-Chair), Rutgers

·Thomas Wisniewski, (Co-Chair), NVP

·Francisco Artigas, Rutgers

·Marcia Brown, Rutgers

·Sherri-Ann Butterfield, Rutgers

·Rada Chirkova, NCSU

·Soon Chun, CUNY

·Margaret Cozzens, Rutgers

·Todd Clear, Rutgers

·Anne Englot, Rutgers

·Chantal Fischzang, Rutgers

·Nurgül Fitzgerald, Rutgers

·Alec Gates, Rutgers

·Mark Gluck, Rutgers

·Milt Halem, UMBC

·Yuan Hong, IIT

·Fredrick Jakle, Rutgers

·Karuna Joshi, UMBC

·Barry Komisaruk, Rutgers

·Kevin Lyons, Rutgers

·Mira Marcus, Tel Aviv University

·Michele Pavanello, Rutgers

·Piotr Piotrowiak, Rutgers

·Arthur Powell, Rutgers

·Charles Santeio, Rutgers

·Basit Shafiq, Rutgers

·Patrick Shafto, Rutgers

·Lee Slater, Rutgers

·Marc Steinberg, Rutgers

·Micha Szostak, Rutgers

·David Trout, Rutgers

·Bonita Veysey, Rutgers

·Jessica Ware, Rutgers

·Jerome D. Williams, Rutgers

·John Worobey, Rutgers

·Yelena Yesha, UMBC