School of Computer Sciences,
Universiti Sains Malaysia (USM), Malaysia
Rosni Abdullah is a Professor in Parallel Computing at the School of Computer Sciences, Universiti Sains Malaysia (USM) and is one of the national pioneers in this field. She obtained her PhD in April 1997 from Loughborough University, United Kingdom specializing in the area of Parallel Algorithms. Both her Bachelors and Masters degree in Computer Science were obtained from Western Michigan University, Kalamazoo, Michigan, U.S.A.
Rosni was the first lady Dean for the School of Computer Sciences, Universiti Sains Malaysia (USM) from 2004 to 2012. She is also the Head of the Parallel and Distributed Processing Research Group at the School since its inception in 1994. Her research areas include Parallel and Distributed Computing, Parallel Numerical Algorithms and Computational Biology. She has graduated 20 PhD students and led 13 research grants including two European Union grants and three Intel grants. She currently drives the Makers movement at USM.
She is currently back as the Dean of the School of Computer Sciences at USM, as well as the Director of the National Advanced IPv6 Center (Nav6), a center of research excellence in USM that focus on Cybersecurity and Internet of Things (IoT).
Keynote Title: Convergence of Big Data and Artificial Intelligence for Big Biology
We are witnessing an exciting era of data explosion in biology where biological data from various data sources is growing at an exponential rate. This is 26 years after Donald Knuth’s statement, “Biology easily has 500 years of exciting problems to work on”, in an interview with Computer Literacy Bookshops (CLB) on 7th December 1993. There is now demand for new approaches and techniques to organize, manage, and analyze this massive amount of biological data. At the same time, Artificial Intelligence is emerging as a vital method for data analytics. In this talk, we present the convergence of big data and artificial intelligence, and how both technologies are poised to solve the challenges in Big Biology.