DART Monthly Webinar // Socially Aware Data Analytics

Title: Socially Aware Data Analytics
Presenter: Chenyi Hu, Xiuzhen Huang, Toby Klein, Qinghua Li, Zhenghui Sha, Ningning Wu, Xintao Wu, Anna Zajicek, and Lu Zhang
Date presented: February 24, 2021

Description: There are increasing concerns from the public on privacy, fairness, safety, and robustness issues of data analytics, data collection, data sharing, and decision making. The social awareness thrust team will present their cutting-edge research on socially aware data analytics that can address social concerns and enable big data analytics to promote social good and prevent social harm.

Presenter's Bios:
Dr. Chenyi Hu, Professor of Computer Science Department at University of Central Arkansas. He has received multiple grant awards from NSF and other federal funding agencies in supporting his research work. His publications are on interval computing and applications in data analysis. His recent focus is is on socially aware crowdsourcing. Prior to chairing Computer Science Department at UCA from 2002-2013, he was a tenured professor at the University of Houston-Downtown.  He also served as a commissioner for the ABET Computing Accreditation Commission from 2016-2019.


Dr. Xiuzhen Huang (Co-Lead), Ph.D., Professor of Computer Science, Founding Director, Arkansas Artificial Intelligence (AI) Campus; Founding Director, Joint Translational Research Lab of Arkansas State University and St. Bernards Medical Center Internal Medicine Residency Program; Principal Investigator, Arkansas State University Bioinformatics Lab. Dr. Xiuzhen Huang’s many contributions to science include: bioinformatics and biomedical informatics; machine learning, artificial intelligence, and deep learning; and algorithm design and development, parameterized computation and complexity, and the theory of computation. 


Toby Klein, a first year PhD student in the Public Policy program at the University of Arkansas, specializing in Social Justice. She is a mixed methodologist, with research backgrounds in neuroscience and public health. Her current research interests explore the impact of anti-hate legislation in the Southern United States, and she is excited to assist on this important project.


Dr. Qinghua Li, Associate Professor of Computer Science and Computer Engineering Department at University of Arkansas Fayetteville. He has extensive experience in cybersecurity, privacy-aware computing, mobile computing and networking, and cyber-physical systems (especially smart grid). He received the NSF CAREER Award in 2018.


Dr. Zhenghui Sha, Assistant Professor of Mechanical Engineering Department at University of Arkansas Fayetteville. He has extensive experience in data-driven design research and complex systems engineering, particularly the design of market systems. 


Dr. Ningning Wu, Professor of Information Science Department at University of Arkansas Little Rock. She has strong expertise in data analytics, text mining, information security, and data quality and governance.


Dr. Xintao Wu (Co-Lead), Professor and Charles D. Morgan/Acxiom Endowed Graduate Research Chair of Computer Science and Computer Engineering Department at University of Arkansas Fayetteville. His research interests include privacy preserving data mining, fairness aware machine learning, fraud detection, and causal inference.  


Dr. Anna Zajicek, Professor of Sociology and Associate Dean in the J. William Fulbright College of Arts and Science at the University of Arkansas, Fayetteville. Her scholarship has been devoted to the intersectionality of social inequalities, discrimination, and public policy. She has been involved in interdisciplinary research focusing social awareness and big data and on successful strategies to institutionalize programs and policies aimed at the advancement of historically underrepresented groups in STEM disciplines.


Dr. Lu Zhang, Assistant Professor of Computer Science and Computer Engineering Department at University of Arkansas Fayetteville. He has rich research experience on structural causal models and their applications on fairness-aware machine learning.