Professor Reda Alhajj

The role of Data Science in Healthcare and Bioinformatics: From Patient Monitoring to Utilizing Multiple Data Sources for Knowledge Discovery and Recommendation

This talk will focus on techniques and structures which could maximize the benefit from various sources and types of interrelated data to better investigate a given problem, e.g., chronic diseases and patient monitoring. Clinical data analysis could reveal interesting discoveries. Healthcare forums may be good medium for communication between patients and medical doctors. Historical data analysis may help in better planning, prediction and treatment. For diseases like cancer, better knowledge discovery and recommendations are possible by integrating discoveries from the analysis of various sources and types of data, including images of infected and normal samples, corresponding gene expression data, surveys completed by patients and normal persons of diverse characteristics, and sentiment analysis of published research (e.g., PubMed abstracts/full articles) related to the investigated type of cancer. Combining these sources will help and better guide pathologists in their effort to microscopically examine more potential cancerous images with high accuracy and confidence. We describe some of our accomplishments, ongoing research and future research plans. The notion of big data will be addressed to show how it is possible to process incrementally available big data using limited computing resources. The benefit of various data mining and network modeling mechanisms for data analysis and prediction will be addressed.

Reda Alhajj is a professor in the Department of Computer Science at the University of Calgary. He published over 500 papers in refereed international journals, conferences and edited books. He served on the program committee of several international conferences. He is founding editor in chief of the Springer premier journal "Social Networks Analysis and Mining", founding editor-in-chief of Springer Series "Lecture Notes on Social Networks", founding editor-in-chief of Springer journal "Network Modeling Analysis in Health Informatics and Bioinformatics", founding co-editor-in-chief of Springer "Encyclopedia on Social Networks Analysis and Mining", founding steering chair of the flagship conference "IEEE/ACM International Conference on Advances in Social Network Analysis and Mining", and three accompanying symposiums FAB, FOSINT-SI and HI-BI-BI. He is member of the editorial board of the Journal of Information Assurance and Security, Journal of Data Mining and Bioinformatics, Journal of Data Mining, Modeling and Management; he has been guest editor of a number of special issues and edited a number of conference proceedings. Dr. Alhajj's primary work and research interests focus on various aspects of data science and big data with emphasis on areas like: (1) scalable techniques and structures for data management and mining, (2) social network analysis with applications in computational biology and bioinformatics, homeland security, etc., (3) sequence analysis with emphasis on domains like financial, weather, traffic, energy, etc., (4) XML, schema integration and re-engineering. He currently leads a large research group of PhD and MSc candidates. He received best graduate supervision award and community service award at the University of Calgary. He recently mentored a number of successful teams, including SANO who ranked first in the Microsoft Imagine Cup Competition in Canada and received KFC Innovation Award in the World Finals held in Russia, TRAK who ranked in the top 15 teams in the open data analysis competition in Canada, Go2There who ranked first in the Imagine Camp competition organized by Microsoft Canada, Funiverse who ranked first in Microsoft Imagine Cup Competition in Canada.