SEMINAR UNF SERIES
|Speaker:||Dr Anisha Keshavan|
|Title:||Leveraging Web Technology to address challenges with Big Data in Neuroscience.|
|Where:||CRIUGM Room E1910 (http://www.criugm.qc.ca/en/contact.html)|
|When:||Thursday May 31st, 13h-14h|
*The seminar will be presented in English
Dr Anisha Keshavan works with Jason Yeatman in Speech and Hearing Sciences and Ariel Rokem at the eScience Institute. Her research focuses on big data methods in neuroimaging. Advances in MRI technology and image segmentation algorithms have enabled researchers to begin to understand the mechanisms of healthy brain development, psychiatric and neurological disorders. However, accurately measuring the brain at a scale large enough to accommodate genetic association and precision medicine studies is challenging; expert neuroanatomist tracings can take a long time, while automated algorithms are not accurate enough. Dr Keshavan aims to develop methods to combine the accuracy of an experts with the speed of computers by incorporating crowdsourced image segmentation with deep learning algorithms. She received a doctoral degree in Bioengineering from the UC Berkeley – UCSF Joint Graduate Program, and a Bachelor’s degree in Aerospace Engineering and Applied Mathematics from the University of California, Los Angeles.
Advances in technology have enabled neuroscientists to collect massive amounts of data to answer important scientific questions. But the drawback is that we are experiencing a “data deluge”, which has brought about new challenges that we must overcome in order to truly reap the benefits that Big Data promises. In this talk, I propose that web technology can help us overcome big data challenges, and present examples of how this is done in the field of neuroimaging. First, how web-based data visualization can address the challenges of high data dimensionality. Second, how web-based collaborative meta analyses can address the challenge of integrating the never-ending stream of new results in the research literature. Finally, how web-based citizen science platforms can address the problem that decisions made by neuroimaging experts cannot be reliably scaled to large datasets. Web technology has completely transformed our everyday lives, but we are only just beginning to unleash its full potential to accelerate scientific discovery.