Dr. Hong Lin received his PhD in Computer Science from the University of Science and Technology of China. Before he joined the University of Houston-Downtown (UHD), he was a postdoctoral research associate at Purdue University, and an assistant research officer at the National Research Council, Canada. Dr. Lin is currently a Professor in Computer Science with UHD. His research interests include cognitive intelligence, human-centered computing, parallel/distributed computing, and big data analytics. He is the supervisor of the Grid Computing Lab and a co-founder of the Data Center at UHD. Dr. Lin currently serves as the program director for the Master of Science in Artificial Intelligence program at UHD. Dr. Lin is a senior member of the Association for Computing Machinery (ACM).
What is your background and how did you become a researcher in your field?
I work in human-centred computing, focusing on using machine learning in physiological data analysis and applications in cognitive science and biomedical engineering. Previously, I worked on parallel computing and inductive data analysis. With the rise of machine learning and data mining, it is interesting to see how computing technologies promote effective data analysis and create a new dimension in cognitive science and human behavioral research.
Behavioural Neurology is a peer-reviewed, Open Access journal which publishes original research articles and review articles based on various diseases and syndromes in behavioral neurology. What are some of the key developments in this area that you’ve observed in the past few years?
Behavioral neurology revolutionized traditional psychological study of human behavior by including scientific theory of internal activities of human brain, so that the study of cognitive science can be carried out at the “circuitry” level. This represents a bottom-up approach to understand and realize intelligence. With the development of sensing technology and machine learning, behavioral neurology has made prominent advancements in affective computing and medicinal diagnosis by physiological and imagery analysis.
What is the importance of your chosen Special Issue topic, ‘Early Detection of Stroke-Initiated Behavioral Disorder?
Due to a variety of causes, the incidence rate of stroke (ischemic/hemorrhagic) has increased rapidly. As such automated diagnostic schemes are widely preferred, as digital healthcare supports accurate and quick data acquisition, assessment, and report generation to forecast the disease condition.
This special issue aims to discuss the various advancements employed in the collection and evaluation of the bio-signal (EEG) and bio-image (MRI) to predict the condition of the brain. This special issue assembles the recent research methodologies considered to detect stroke (Ischemic/Hemorrhagic) using single/multichannel EEG and brain MRI of multiple modalities. The publication of this special issue gives readers a birds-eye view over stroke detection, assessment and treatment.
Can you tell us a bit more about your experience editing this special issue, and share what you consider to be the key benefits of being part of the Guest Editorial Team?
The team worked in harmony during the entire process of disseminating the call for papers, inviting authors, handling the reviews, and compiling the special issue for publication. Team members support each other and make sure that there are no loose ends in every phase of the editing process. The editing experience benefited the editors as well as the authors in academic exchange and collaboration, which summarized recent advancements in research in related fields, deepened knowledge, and pointed out the direction for future development.
Read articles in ‘Early Detection of Stroke-Initiated Behavioral Disorder’ >>
This blog post is distributed under the Creative Commons Attribution License (CC-BY). Illustration by David Jury.