Research Projects
Open Source Software Engagement
Open source software is critically important for both individuals and organizations. This importance raises questions about how we understand the health and sustainability of the open-source projects we rely on. Open source projects that fail can have negative impacts on the community involved in the project as well as organizations that rely on such projects.
CHAOSS helps people who want to know more about the health and sustainability of the open-source projects they are engaged with
Activities & Outcomes
- Funded over $3M from organizations including the Alfred P. Sloan Foundation, the Ford Foundation, Mozilla, and Red Hat
- Produced over 100 open source community health metrics along with software and programs to implement the metrics
- Presented and deployed globally
Key Faculty and Collaborators
- Matt Germonprez, Ph.D.
- Sean Goggins, Ph.D.
- Dawn Foster, Ph.D.
Empowering Human-AI Collaboration through Conversational Explanations
Based on a state-of-the-art explainable social recommender system, this project aims to achieve three objectives. First, I present a user-centric participatory design approach to measuring and improving the user's mental model, elicitation mechanism, and explanation models. Second, I utilize a human-centered design approach to develop a prototype to allow conversational explanations in an AI-powered recommender system. Third, I adopt empirical methods such as interviews and surveys to investigate and theorize the user's mental model of interacting with explainable AI systems.
Activities & Outcomes
- Received NSF HCC CRII Award
- Built system prototypes with large language models
Key Faculty and Collaborators
- Chun-Hua Tsai, Ph.D.
Promoting Community Health Access through Explainable AI-Mediated Communication
This project aims to explore the effects of promoting community health access through Explainable AI mediated Communication (XAI-MC). My approach is to mediate communication between healthcare providers (e.g., OBGYN, healthcare coordinators) and patients (e.g., pregnant women) through a self-explained transparent computational agent.
Specifically, it was proposed to build AI-powered medical assistants (e.g., smart chatbot) to improve health access, awareness, and literacy to the underserved populations. This project would collaborate with an Omaha local nonprofit group that exclusively provides maternal wellness services for underserved black populations.
Activities & Outcomes
- Received two University of Nebraska Collaboration Initiative awards
Key Faculty and Collaborators
- Chun-Hua Tsai, Ph.D.
- Ann Anderson Berry (UNMC)
A Multi-level Collaborative Design Framework for Cross-sovereignty Software Accountability
This project aims to tackle the distinct software accountability challenges arising from the unique governance structures, cultures, and technological conditions of tribal nations, as well as their intricate relations with other sovereign entities. This project develops and implements a multi-level participatory design framework and an AI (artificial intelligence) chatbot for emergency management.
This initiative enhances emergency management in tribal nations, improves crosssovereignty software accountability, and elevates AI system literacy among indigenous communities.
Activities & Outcomes
- Received NSF DASS Award
Key Faculty and Collaborators
- Chun-Hua Tsai, Ph.D.
- Yu-Che Chen
- Edouardo Zendejas
Consortium for Public Health Research Lab
Research foci:
- Usability research to improve the ease of use, usefulness, efficiency, effectiveness, user satisfaction of Health IT applications
- Data visualization to optimize human cognition
- Best practices developing & designing Health IT systems
Key projects:
- Emergency response system for public health labs – STATPack (licensed)
- Public health citizen science GIS application (licensed)
- Dashboard using IoT sensors to monitor health of first responders (DOT & OFD)
- Nebraska Juvenile Justice Case Management System (NDOJ)
- Optimizing the EHR for Cardiac Care (NIHR01)
- PICU Sedation Management Usability Study at Childrens Hospital
- C2SES USSTRATCOM
Activities & Outcomes
- $8.7M+ funding – NIH, NSF, CDC, NHHS, HHS, Childrens Hospital, DOD, DOJ, NDOJ, USSTRATCOM, Northrup Gruman, Youthcare, Beyond, NRI, NASA, HRSA, ARHQ, USACE, NE Crime Commission
- 100+ publications
- 100+ students have gained marketable IT experience and 100% of them are employed
- Licensed two software applications, Developed and deployed State of Nebraska Crime Commission application
Key Faculty and Collaborators
- Ann Fruhling, Ph.D.
DeEmotions: A novel dataset for multilabel classification of Depression-linked Emotions
Social networking platforms have become a focal point of increased interest in recent years. Within the domain of social network analysis, the task of multiple emotions (8) prediction from social media texts has emerged as a particularly intriguing subfield.
In this research, we created a novel dataset, DeEmotion from reddit regarding emotion detection of social media texts and evaluated by F1 macro by multilabel classification of the texts with machine learning (SVM, XGBoost, and Light GBM), and deep learning methods (such as BERT, GAN-BERT, and BART).
Activities & Outcomes
- The ataset was created through a majority vote over inputs by zero-shot classifications from pretrained models
- The quality is validated using annotators and Chat-GPT, exhibiting an acceptable level of inter-rater reliability between annotators
- Offers analysis on DeEmotions to understand the correlation between emotions and their distribution over time
Key Faculty and Collaborators
- Abu Bakar Siddiqur Rahman, Ph.D. candidate (UNO)
- Lotfollah Najjar, Ph.D.
Science of Citizen Science
My research highlights the design, management, and technology supporting diverse stakeholders in citizen science to inform the development of systems and policies for public participation in science. I study the growing role of technologies in citizen science, a type of research collaboration that involves non-professionals in authentic scientific research.
I’m interested in how ordinary people become involved in meaningful realworld research through citizen science projects, and how technologies can help.
Activities & Outcomes
- Funded by NSF & NRI
- Recent keynotes and invited talks
- NASA
- United Nations Statistical Commission
- UNO’s Curious People
- Journal papers on citizen science in 7 different fields
- Advisor to citizen science projects and federal policy initiatives across multiple disciplines
Key Faculty and Collaborators
- Andrea Grover, Ph.D.
Social Network Analysis
The task of link prediction from social network analysis has emerged as a particularly intriguing subfield. This research area involves analyzing a given static image of a social network to forecast potential future connections between currently separate entities.
Broadly speaking, link prediction methodologies can be categorized into three main strategies: feature-based prediction methods, Bayesian statistical models, and probabilistic relational models. A common challenge faced in various link prediction contexts is the issue of class imbalance.
Activities & Outcomes
- To address the challenges associated with imbalanced data classification, there are three primary methods:
- algorithmic techniques
- data preprocessing
- feature selection strategies
- These experiments involve the extraction of topological attributes from social networking graphs and the implementation of data mining practices to bolster the efficiency of classification algorithms
Key Faculty and Collaborators
- Laleh Madahali, Ph.D. (Iowa University)
- Lotfollah Najjar, Ph.D. (UNO)
Understanding the Complex Relationship Between Technology, Parkinson’s Disease, and User Empowerment
Users with chronic neurodegenerative diseases who have limited means to travel to medical facilities due to location, physical disability, or social economic status are particularly vulnerable to being underserved by technology solutions such as mHealth due to difficulties in accessing these populations for adequate participation and user needs elicitation during the design process.
This project focuses on assessing and understanding complex user needs that change over the course of a neurodegenerative disease is through collection of behavioral data using longitudinal methods to enable the design of effective technology solutions that effectively serve latent and dynamic user needs of this unique population.
Key Faculty and Collaborators
- Christine Toh, Ph.D. (UNO College of IS&T)
- Kelly Gonzales, Ph.D. (UNMC College of Nursing)
- Ka-Chun Siu, Ph.D. (UNMC College of Allied Health Professions)
- Crystal Krause, Ph.D. (UNMC College of Medicine)
National Counterterrorism Innovation, Technology and Education (NCITE) Center
This cooperative agreement with DHS S&T establishes UNO as the DHS trusted agent for counterterrorism research. NCITE strives to understand, prevent, and counteract terrorist violence and UNO leads a consortium of academic institutions who study terrorist behavior and the best ways to stop it.
It is an important collaborative center that brings together some of the best minds and cutting-edge ideas around the field of counterterrorism. NCITE provides access to innovation, technology and education for counterterrorism professionals working within the Homeland Security Enterprise. This unique relationship with DHS provides annual funding and flexible contracting vehicles to conduct national research.
Activities & Outcomes
- Received $36.5 Million award from DHS
- Establishes a Basic Ordering Agreement that allows additional $10 Million per year
- 5 new faculty (One in IS&T)
- Funds multiple graduate students (8 in IS&T)
- Raises national profile
Key Faculty and Collaborators
- Gina S. Ligon, Ph.D.
- Douglas C. Derrick, Ph.D.
- Joel S. Elson, Ph.D.