In order to contain a pandemic like COVID-19, we first need to understand it. We need to analyze relevant data and develop an authentic and informative mental model. This can be carried out through “visualization” and “exploration”. There are many ways in which visualizations could lead to misleading conclusions; therefore, good visualization is challenging.
In this lecture, experts from Qatar Computing Research Institute (QCRI) will study several aspects of COVID-19 by reviewing the different types of visualization techniques and challenges associated with them.
The second half of this lecture will cover exploratory data analysis (EDA) where experts will interpret visualizations, develop hypotheses, analyze them and compare the advantages and disadvantages of different visualizations. The goal is to review best practices in analyzing the statistics of COVID-19.
Nan Tang is a senior scientist at QCRI. Prior to joining QCRI in December 2011, he was a Research Fellow at the Laboratory for Foundations of Computer Science at the University of Edinburgh. Tang obtained his PhD degree from the Chinese University of Hong Kong. His research interests focus on data preparation, data visualization, and collaborative data science.
Mohammad Amin Sadeghi is a scientist at QCRI, where he studies machine learning applications. Prior to this, he was an Assistant Professor in the Department of ECE at the University of Tehran. He graduated from the University of Illinois Urbana-Champaign. Dr. Sadeghi has previously worked at Google, Amazon and Adobe.
His research interests include machine learning, computer vision and their applications.
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