Mission Statement
The AI & Big Data iRDA group is committed to advancing high-impact interdisciplinary research with a focus on Responsible Artificial Intelligence (Responsible AI). We develop and deploy AI systems that are safe, trustworthy, human-centered, and ethical. As AI becomes increasingly integral to decision-making across diverse systems, our mission is to address critical challenges related to reliability, safety, transparency, equity and fairness. We recognize AI & Big Data as essential to modern scientific inquiry and support research and applications across a wide range of domains, including bioinformatics, translational clinical research, transportation, astronomy, public health, and mechanical and civil engineering.
The AI & Big Data group will complement and align with existing research centers and academic departments at ºÚÁÏÍø such as the ºÚÁÏÍø Transportation Research Center, NIPM, ºÚÁÏÍø Medicine, Public Health, Integrated Health Sciences, and Neuroscience. The AI & Big Data group will collaborate with other iRDA groups, including Sustainability in Arid Lands, Racial Equity and Social Justice, Aging and Brain Health, Addictions, and MSI, all of which rely on data analysis and AI systems.
For additional information on the The AI & Big Data iRDA cluster, contact: ai@unlv.edu, ai_irda@unlv.edu, or mingon.kang@unlv.edu.
Members
-
Mingon Kang, Ph.D.
Liaison
Computer Science -
Junggab Son
Computer Science -
Shaikh Arifuzzaman, Ph.D.
Computer Science -
Zuobin Xiong, Ph.D.
Computer Science -
Bryar Shareef, Ph.D.
Computer Science -
Vishal Sharma, Ph.D., MS
School of Public Health
Project Highlight
The AI & Big Data iRDA group leads efforts in Responsible Artificial Intelligence (Responsible AI), an emerging field that prioritizes the safe, trustworthy, human-centered, and ethical deployment of AI systems over solely improving predictive accuracy. Projects include:
- AI-Driven Malware Binary Analysis and Report Generation with Large Language Models
(Project Leaders: Junggab Son and Zuobin Xiong) - Automated Detection of Radicalizing Gaming Content and EEG-Based Neural Assessment
(Project Leaders: Tiffany Howard, Joel Snyder, Shaikh Arifuzzaman) - Trustworthy enzyme number prediction using evidential deep learning
(Project Leaders: Mingon Kang and Jeffery Shen)
Focal Areas
- Fairess AI
- Cybersecurity
- Bioinformatics
- Healthcare Informatics
- High-Performance Computing