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Arkansas Researchers Developing Prediction Models for Coronavirus

Fayetteville, Ark. - Data Science professor Justin Zhan is collaborating with University of Arkansas for Medical Sciences professors David Ussery and Xuming Zhang to develp accurate predictions of genomic variation trends of coronavirus.

Their work will help public health officials monitor the outbreak and adapt to changes.  It could also provide valuable information for the deisgn of the vaccine.

"To control and prevent COVID-19, public officials need highly robust models for predicting how and where the virus will spread," Zhan said.  "This project will assist that effort and lead to better detection and prevention strategies.  We think it could have colassal social and economic impacts."

Zhan's research focuses on big data, blockchain technologies, information assurance, and biomedical informatics.  For this project, he will use a novel, blockchain-based artificial intelligence system, which integrates information on the relationships of biological systems to predict trends and changes as the coronavirus spreads.  The system will be evaluated and tested through various coronavirus benchmark datasets.

Ussery and Zhang will provide expertise in the areas of bioinformatics, microbiology, and immunology.

A blockchain is a growing list of records, called blocks, that are linked using cryptography.  Each block contains a cryptographic of the previous block, and timestamp, and transaction data generally represented as a Merkle tree.  By design, a blockchain is resistant to modification of the data.  It is "an open, distributed ledger that can record transactions efficiently and in a verifiable and permanent way."  As the fundamental component and functional element of blockchains, Merle trees allow for efficient and secure verification of large data structures and potentially boundless data sets.

With a large grant from the Army Research Office, Zhan in building and graphics processing unit at the U of A.  The unit is a computer cluster of big data research and eduction.

Justin Zhan and David Ussery are both Arkansas Research Alliance scholars.

CSCE Assistant Professor Yarui Peng Receives the Prestigious NSF CAREER Award to Study Design Automation Tools for Heterogeneous Multi-Chiplet Systems

Yarui Peng, assistant professor of computer science and computer engineering, has received the NSF Faculty Early Career Development Award in 2021, known as a CAREER award. NSF describes the CAREER Award as its most prestigious award in support of early-career faculty who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department and organization. Read More

Di Appointed to Lead Computer Science and Computer Engineering

Jia Di (left) will become head of the Department of Computer Science and Computer Engineering Jan. 1. Dale Thompson (right) has served as interim department head since July.

Dec. 14, 2020 - Professor Jia Di has been chosen to lead the Department of Computer Science and Computer Engineering.

The department is the largest in the College of Engineering, with more than 600 undergraduate students enrolled.

Di succeeds Xiaoqing "Frank" Liu, who departed in July to become dean of engineering at Southern Illinois University.

Di has been a faculty member in the Department of Computer Science and Computer Engineering since 2004 and holds the Twenty-First Century Research Leadership Chair. He is internationally recognized for his research in asynchronous integrated circuit design and hardware security. Di holds six patents and is a senior member of the Institute of Electrical and Electronics Engineers, an elected member of the National Academy of Inventors, and an eminent member of Tau Beta Pi.  Read More

Data Science Professor Receives $1.25 Million from Department of Defense

Oct. 08, 2020

FAYETTEVILLE, Ark. - A $1.25 million grant from the Department of Defense will enable data science researcher Justin Zhan to develop novel algorithms to enhance the speed and efficiency of computational software that uses large amounts of streaming data. 

By harnessing big data analytics faster and more efficiently, the algorithms will significantly enhance computational performance of many applications and programs that require massive amounts of streaming data.  This so-called machine-learning approach to big data analytics will improve operational robustness, in addition computational speed and efficiency.  Read More