Award Abstract # 1735095
NRT: Interdisciplinary Training in Complex Networks and Systems

NSF Org: DGE
Division Of Graduate Education
Recipient: TRUSTEES OF INDIANA UNIVERSITY
Initial Amendment Date: July 6, 2017
Latest Amendment Date: March 15, 2022
Award Number: 1735095
Award Instrument: Standard Grant
Program Manager: Karen
DGE
 Division Of Graduate Education
EDU
 Directorate for STEM Education
Start Date: September 1, 2017
End Date: August 31, 2023 (Estimated)
Total Intended Award Amount: $2,999,845.00
Total Awarded Amount to Date: $3,049,174.00
Funds Obligated to Date: FY 2017 = $2,999,845.00
FY 2019 = $49,329.00
History of Investigator:
  • Selma Sabanovic (Principal Investigator)
    selmas@indiana.edu
  • Bernice Pescosolido (Co-Principal Investigator)
  • Katy Borner (Co-Principal Investigator)
  • Olaf Sporns (Co-Principal Investigator)
  • Luis Rocha (Co-Principal Investigator)
  • Luis Rocha (Former Principal Investigator)
  • Armando Razo (Former Co-Principal Investigator)
  • Selma Sabanovic (Former Co-Principal Investigator)
Recipient Sponsored Research Office: Indiana University
107 S INDIANA AVE
BLOOMINGTON
IN  US  47405-7000
(317)278-3473
Sponsor Congressional District: 09
Primary Place of Performance: Indiana University
919 E 10th Street
Bloomington
IN  US  47408-3912
Primary Place of Performance
Congressional District:
09
Unique Entity Identifier (UEI): YH86RTW2YVJ4
Parent UEI:
NSF Program(s): NSF Research Traineeship (NRT),
Project & Program Evaluation
Primary Program Source: 04001920DB NSF Education & Human Resource
01001718DB NSF RESEARCH & RELATED ACTIVIT

04001718DB NSF Education & Human Resource
Program Reference Code(s): 9179, 1371, 1331, SMET, 7433
Program Element Code(s): 199700, 726100
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.076

ABSTRACT

Understanding complex networked systems is key to solving some of the most vexing problems confronting humankind, from discovering how dynamic brain connections give rise to thoughts and behaviors, to detecting and preventing the spread of misinformation or unhealthy behaviors across a population. Graduate training, however, typically occurs in one of two dimensions: experimental and observational methods in a specific area such as biology and sociology, or in general methodologies such as machine learning and data science. With more and more students seeking to gain sufficient expertise in mathematical and computational methods on top of domain-specific laboratory and social analysis methodologies, a greater demand for more efficient training is emerging. This National Science Foundation Research Traineeship (NRT) award to Indiana University will address this growing need with an integrated dual PhD program that trains students to be "bidisciplinary" in Complex Networks and Systems (CNS) and another discipline of their choosing from the natural and social sciences. It will seamlessly integrate traditional education with interdisciplinary hands-on research in a culture of academic and human diversity. This program will provide unique interdisciplinary training for thirty-four (34) PhD students, including twenty-two (22) funded trainees. The program will provide additional training experience to 40 summer affiliate students and a population of more than 300 participants across the participating PhD programs.

The training program capitalizes on the new Indiana University Network Science Institute (IUNI). The Institute's 165+ faculty members will serve in interdisciplinary PhD program committees to be co-chaired by research mentors from both CNS and the target empirical domain. Project-driven, team-based research at IUNI will seamlessly integrate academic education with interdisciplinary hands-on scientific and industrial research. Trainees will learn to connect the general-purpose, computational expertise of CNS to the deep, domain-specific research methodologies of the natural, behavioral, and social sciences thus bridging the gap between distinct training cultures. They will be a new breed of STEM scientists that escapes the silos of disciplinary training to address the complex problems of the 21st century. Specifically, the four goals of training activity are: 1) provide dual research proficiency; 2) develop collaborative skills via early integration into problem-driven, interdisciplinary research; 3) produce a diverse workforce by recruiting student cohorts from a broad set of disciplines and varied backgrounds to be trained within a team culture; 4) establish a sustainable interdisciplinary training model by enlarging the institutional channels created between informatics and natural and social sciences to other Indiana University departments and institutions. A science-of-science study conducted throughout the NRT project will evaluate the efficacy of interdisciplinary training of the students in this program. This project will develop a flexible dual PhD program and best-practices to allow additional departments at Indiana University to join the program in the future, as well as other institutions to develop similar programs.

The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 33)
Paolillo, John and Harper, Brian and Boothby, Clara and Axelrod, David "YouTube Childrens Videos: Development of a Genre under Algorithm" Proceedings of the Annual Hawaii International Conference on System Sciences , 2020 10.24251/HICSS.2020.336 Citation Details
Bailey, Rachel L and Read, Glenna L and Yan, YaoJun Harry and Liu, Jiawei and Makin, David A and Willits, Dale "Camera Point-of-View Exacerbates Racial Bias in Viewers of Police Use of Force Videos" Journal of Communication , v.71 , 2021 https://doi.org/10.1093/joc/jqab002 Citation Details
Barron, Alexander T. and Huang, Jenny and Spang, Rebecca L. and DeDeo, Simon "Individuals, institutions, and innovation in the debates of the French Revolution" Proceedings of the National Academy of Sciences , 2018 10.1073/pnas.1717729115 Citation Details
Benson, Lauren V and Candadai, Madhavun and Izquierdo, Eduardo J. "Neural reuse in multifunctional neural networks for control tasks" Artificial life , v.32 , 2020 https://doi.org/10.1162/isal_a_00319 Citation Details
Brattig Correia, Rion and de Araújo Kohler, Luciana P. and Mattos, Mauro M. and Rocha, Luis M. "City-wide electronic health records reveal gender and age biases in administration of known drugdrug interactions" npj Digital Medicine , v.2 , 2019 10.1038/s41746-019-0141-x Citation Details
Candadai, Madhavun and Izquierdo, Eduardo J. "Sources of predictive information in dynamical neural networks" Scientific Reports , v.10 , 2020 https://doi.org/10.1038/s41598-020-73380-x Citation Details
Cohen, Aaron M and Dunivin, Zackary O and Smalheiser, Neil R "A probabilistic automated tagger to identify human-related publications" Database , v.2018 , 2018 10.1093/database/bay079 Citation Details
Correia, Rion B. and Gates, Alexander J. and Wang, Xuan and Rocha, Luis M. "CANA: A Python Package for Quantifying Control and Canalization in Boolean Networks" Frontiers in Physiology , v.9 , 2018 10.3389/fphys.2018.01046 Citation Details
Correia, Rion Brattig and Wood, Ian B. and Bollen, Johan and Rocha, Luis M. "Mining Social Media Data for Biomedical Signals and Health-Related Behavior" Annual Review of Biomedical Data Science , v.3 , 2020 10.1146/annurev-biodatasci-030320-040844 Citation Details
Duggan, Ben and Metzcar, John and Macklin, Paul "DAPT: A package enabling distributed automated parameter testing" Gigabyte , v.2021 , 2021 https://doi.org/10.46471/gigabyte.22 Citation Details
Dunivin, Zackary and Zadunayski, Lindsay and Baskota, Ujjwal and Siek, Katie and Mankoff, Jennifer "Gender, Soft Skills, and Patient Experience in Online Physician Reviews: A Large-Scale Text Analysis" Journal of Medical Internet Research , v.22 , 2020 10.2196/14455 Citation Details
(Showing: 1 - 10 of 33)

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

This NSF NRT award supported graduate student participation in a dual PhD program that trains them to be "bidisciplinary" in Complex Networks and Systems (CNS) and another discipline of their choosing from the cognitive, natural, and social sciences. Trainees learned to connect the general-purpose, computational expertise of CNS to the deep, domain-specific research methodologies of the cognitive, natural, and social sciences thus bridging the gap between distinct training cultures. To support the students’ bidisciplinary training, the program aimed to provide students with dual research proficiency and collaborative skill development while preparing them to enter the 21st century STEM workforce. To accomplish these goals, faculty developed specific interdisciplinary training requirements, summer research internships, an annual research showcase, professionalization training in skills such as public presentation and grant writing, a colloquium series with extended opportunity to interact with speakers, and informal social events. 

Over the course of the project’s duration, from Fall 2017 through Summer 2023, 35 doctoral students participated as trainees and over 30 faculty members contributed to their education as advisors and mentors. These students were trained in 15 different disciplines, including cognitive science, intelligent systems engineering, biology, sociology, political science, media studies, folklore, economics and public health, along with complex networks and systems.  When examining student gender over the course of the program, 64.5% of NRT students identify as male, and the remaining 35.5% identify as female. In terms of the racial make-up of NRT doctoral students, 21 students (68%) identify their race as White; eight students (26%) identify as Asian, and one student (3%) identifies as African American; one student did not identify their race. An additional 40 graduate students participated as summer affiliates, and others were able to take part in activities open to the public, including over 30 NSF CNS NRT colloquium talks and Annual Research Showcases. 

Over the course of the award, evaluators assessed the impact of the dual degree requirement of the CNS NRT programs on doctoral students’ time‐to‐milestone achievement, specifically whether NRT students take more time on average than their peers enrolled in other academic plans in the Informatics department at Indiana University. Results indicate that there is no significant difference in the median time taken to achieve doctoral milestones by CNS NRT students when compared to doctoral students enrolled in other Informatics academic plans. As the award closes, 5 trainees have graduated with their doctorates, 1 additional student has defended their dissertation proposal, and 15 additional students have passed their qualifying exams, while 4 have left the program. Of 4 students who left the CNS NRT, 3 continue their training in a single PhD discipline and 1 graduated with a Masters degree.

Between the years 2018 and 2023, research performed by CNS NRT participants produced 77 publications, which include 65 journal articles, 9 conference papers, and 3 books, in over 50 venues.  In terms of interdisciplinary scope of CNS NRT research output, the publications were found to correspond to all 13 broad disciplines in the UCSD journal classification framework, including Medical Specialties, Biotechnology, Health Professionals, and Social Sciences. Example sub‐disciplines associated with NRT research include: Clinical Cancer Research, Molecular & Cellular Neuroscience Molecular Biology, Bioinformatics, Optics & Lasers, Engineering Education, and Communication Research. That is, no major area of science was untouched by the scholarship produced within the CNS NRT.

Participating students and faculty were annually surveyed about their reflections on the NRT performance. We asked all participants to rate whether they agree that the NRT has achieved its stated goals over the last academic year, and their satisfaction with the NRT specific to their role within the NRT (e.g., as a faculty member, as a doctoral or affiliated student). Doctoral students were asked to rate their satisfaction with various aspects of the NRT academic program, their mentorship relationships, as well as the NRTs impact on their research and interdisciplinary skill development. We asked NRT faculty to reflect on their mentorship and advising activities and their doctoral students. In the final year of the award, survey respondents agreed that the CNS NRT has achieved its overall goals, as well as the sub-goals of disseminating research and raising local awareness of CNS NRT programs. 

 


Last Modified: 01/03/2024
Modified by: Selma Sabanovic

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