Top machine learning and data analytics experts lead discussions in healthcare and security

Boeing [NYSE:BA] and Qatar Computing Research Institute (QCRI), a member of Qatar Foundation for Education, Science and Community Development, have announced the speaker lineup for the Second Annual Machine Learning And Data Analytics Symposium (MLDAS) that will take place this week.

The symposium, on 9 and 10 March, is open to students, researchers and industry experts, and will feature top global experts discussing applications, recent advances, and new solutions in the fields of machine learning and data analytics. The two-day event will be held at the Qatar National Convention Centre.

The symposium demonstrates QCRI’s endeavours to support Qatar Foundation in its mission to build the country’s innovation and technology capacity in line with the Qatar National Vision 2030.

The first day will focus on applying machine learning to healthcare and medicine. There will be several talks, panels and discussions on health records and genomic data led by David Page and Mark Craven from the University of Wisconsin. Jenna Wiens, from the University of Michigan and a Forbes ‘30 Under 30’ honouree, will lead an overview of how to use spatiotemporal patient data to improve patient outcomes. Rich Caruana, from Microsoft Research, will present his work on intelligible models for healthcare.

The second day will focus on machine learning for graph data, security and surveillance. Christos Faloutsos from Carnegie Mellon University will discuss how large graphs can be mined for fraud detection. Polo Chau of Georgia Institute of Technology will lead a discussion of how graph mining and visualisation can help untangle the complicated data trails left by cyber criminals to cover their tracks. 

Other expert speakers on the second day contributing to the discussions on security and social media include Mubarak Shah from the University of Central Florida; Noshir Contractor of Northwestern University; Gary Delany of CSIRO (Commonwealth Scientific and Industrial Research Organisation); and Jaideep Srivastava and Divy Agrawal of QCRI. 

Supplementing the two-day symposium will be a hands-on machine learning workshop for students, which will take place on 12 March at Carnegie Mellon University in Qatar. The workshop aims to introduce the theory and basic concepts of machine learning, and the application of machine learning to practical data mining tasks. Registration information for the workshop can be found at www.mldas.org.

“Data mining and machine learning are revolutionising the use of data analytics in all facets of human endeavour, from recognising and decoding images, to learning customer preferences, identifying and predicting disease causing genes, understanding social networks, and more,” said Dr Mohammed J. Zaki, Principal Scientist, QCRI and co-chair of the symposium.

“The overwhelming response to the second QCRI-Boeing MLDAS symposium, with submissions from 21 countries and over 250 registrations from various constituents in Qatar, the Middle East, and indeed, from all over the world, firmly attests to the potential of data analytics in solving real-world problems in various fields. The symposium cements the growing research partnership between Boeing and QCRI, which is rapidly establishing itself as a world-class institute in the science and art of data mining and learning, among other areas.”

Dr Dragos Margineantu from Boeing Research & Technology (BR&T) and co-chair of the symposium added: “By focusing on areas of high interest for the machine learning and data analytics communities, our speakers and participants will be able to present and exchange detailed ideas that will drive research on these important topics.

“This symposium is becoming a premier venue for sharing the most recent advances in applied machine learning and data analytics, two key areas of growth for BR&T. We are very excited about the interest and response that we got from the potential attendees.” 

The full agenda and further information about the symposium can be found by visiting www.mldas.org.