FAIR-CS: Framework for Interdisciplinary Research Collaborations in Online Computing Programs
- URL: http://arxiv.org/abs/2507.11802v1
- Date: Tue, 15 Jul 2025 23:51:17 GMT
- Title: FAIR-CS: Framework for Interdisciplinary Research Collaborations in Online Computing Programs
- Authors: Breanna Shi, Thomas Deatherage, Jeanette Schofield, Charles R. Clark, Thomas Orth, Nicholas Lytle,
- Abstract summary: This paper presents the Framework for Accelerating Interdisciplinary Research in Computer Science (FAIR-CS)<n>FAIR-CS is a method for achieving research goals, developing research communities, and supporting high quality mentorship in an online research environment.<n>We discuss the implementation of FAIR-CS in the Human-Augmented Analytics Group (HAAG) with researchers from the Georgia Tech's Online Master of Computer Science program.
- Score: 0.4712282770819684
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Research experience is crucial for computing master's students pursuing academic and scientific careers, yet online students have traditionally been excluded from these opportunities due to the physical constraints of traditional research environments. This paper presents the Framework for Accelerating Interdisciplinary Research in Computer Science (FAIR-CS), a method for achieving research goals, developing research communities, and supporting high quality mentorship in an online research environment. This method advances virtual research operations by orchestrating dynamic partnerships between master's level researchers and academic mentors, resulting in interdisciplinary publications. We then discuss the implementation of FAIR-CS in the Human-Augmented Analytics Group (HAAG), with researchers from the Georgia Tech's Online Master of Computer Science program. Through documented project records and experiences with 72 active users, we present our lessons learned and evaluate the evolution of FAIR-CS in HAAG. This paper serves as a comprehensive resource for other institutions seeking to establish similar virtual research initiatives, demonstrating how the traditional research lab environment can be effectively replicated in the virtual space while maintaining robust collaborative relationships and supporting knowledge transfer.
Related papers
- Dynamic Knowledge Exchange and Dual-diversity Review: Concisely Unleashing the Potential of a Multi-Agent Research Team [53.38438460574943]
IDVSCI is a multi-agent framework built on large language models (LLMs)<n>It incorporates two key innovations: a Dynamic Knowledge Exchange mechanism and a Dual-Diversity Review paradigm.<n>Results show that IDVSCI consistently achieves the best performance across two datasets.
arXiv Detail & Related papers (2025-06-23T07:12:08Z) - ScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflows [82.07367406991678]
Large Language Models (LLMs) have extended their impact beyond Natural Language Processing.<n>Among these, computer-using agents are capable of interacting with operating systems as humans do.<n>We introduce ScienceBoard, which encompasses a realistic, multi-domain environment featuring dynamic and visually rich scientific software.
arXiv Detail & Related papers (2025-05-26T12:27:27Z) - Not real or too soft? On the challenges of publishing interdisciplinary software engineering research [4.597329752530121]
Discipline of software engineering combines social and technological dimensions.<n>Interdisciplinary research submitted to software engineering venues may not receive the same level of recognition as more traditional or technical topics.
arXiv Detail & Related papers (2025-01-11T12:18:46Z) - Teaching Research Design in Software Engineering [1.9659095632676098]
Empirical Software Engineering (ESE) has emerged as a contending force aiming to critically evaluate and provide knowledge that informs practice in adopting new technologies.
This chapter teaches foundational skills in research design, essential for educating software engineers and researchers in ESE.
arXiv Detail & Related papers (2024-07-06T21:06:13Z) - ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
ResearchAgent is an AI-based system for ideation and operationalization of novel work.<n>ResearchAgent automatically defines novel problems, proposes methods and designs experiments, while iteratively refining them.<n>We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
arXiv Detail & Related papers (2024-04-11T13:36:29Z) - SurveyAgent: A Conversational System for Personalized and Efficient Research Survey [50.04283471107001]
This paper introduces SurveyAgent, a novel conversational system designed to provide personalized and efficient research survey assistance to researchers.
SurveyAgent integrates three key modules: Knowledge Management for organizing papers, Recommendation for discovering relevant literature, and Query Answering for engaging with content on a deeper level.
Our evaluation demonstrates SurveyAgent's effectiveness in streamlining research activities, showcasing its capability to facilitate how researchers interact with scientific literature.
arXiv Detail & Related papers (2024-04-09T15:01:51Z) - An Undergraduate Consortium for Addressing the Leaky Pipeline to Computing Research [1.9336815376402718]
This experience report describes a first-of-its-kind Undergraduate Consortium (UC)
The UC aims to broaden participation in the AI research community by recruiting students, particularly those from historically marginalized groups.
This paper presents our program design, inspired by a rich set of evidence-based practices, and a preliminary evaluation of the first years that points to the UC achieving many of its desired outcomes.
arXiv Detail & Related papers (2024-03-25T21:43:43Z) - SciOps: Achieving Productivity and Reliability in Data-Intensive Research [0.8414742293641504]
Scientists are increasingly leveraging advances in instruments, automation, and collaborative tools to scale up their experiments and research goals.
Various scientific disciplines, including neuroscience, have adopted key technologies to enhance collaboration, inspiration and automation.
We introduce a five-level Capability Maturity Model describing the principles of rigorous scientific operations.
arXiv Detail & Related papers (2023-12-29T21:37:22Z) - Industry-Academia Research Collaboration in Software Engineering: The
Certus Model [13.021014899410684]
Building scalable and effective research collaborations in software engineering is known to be challenging.
This paper aims to understand what are the elements of a successful industry-academia collaboration that enable the culture of participative knowledge creation.
arXiv Detail & Related papers (2022-04-23T10:16:23Z) - Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations.
Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
arXiv Detail & Related papers (2021-01-15T10:43:10Z) - Learnings from Frontier Development Lab and SpaceML -- AI Accelerators
for NASA and ESA [57.06643156253045]
Research with AI and ML technologies lives in a variety of settings with often asynchronous goals and timelines.
We perform a case study of the Frontier Development Lab (FDL), an AI accelerator under a public-private partnership from NASA and ESA.
FDL research follows principled practices that are grounded in responsible development, conduct, and dissemination of AI research.
arXiv Detail & Related papers (2020-11-09T21:23:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.