Anastasia Ailamaki - Querying and Exploring Big Brain Data

12:00
Thursday
2
May
2013
  • Keynote Speech
Place: 
Organized by: 
L’équipe "Keynotes" du LIG
Speaker: 
Anastasia Ailamaki

Anastasia Ailamaki is a Professor of Computer Sciences at the Ecole Polytechnique Federale de Lausanne (>EPFL) in Switzerland. Her research interests are in database systems and applications, and in particular (a) in strengthening the interaction between the database software and emerging hardware and I/O devices, and (b) in automating database management to support computationally-demanding and demanding data-intensive scientific applications. She has received a Finmeccanica endowed chair from the Computer Science Department at Carnegie Mellon (2007), a European Young Investigator Award from the European Science Foundation (2007), an Alfred P. Sloan Research Fellowship (2005), eight best-paper awards at top conferences (2001-2011), and an NSF CAREER award (2002). She earned her Ph.D. in Computer Science from the University of Wisconsin-Madison in 2000. She is a senior member of the IEEE and a member of the ACM, and has also been a CRA-W mentor.

"Réalisation technique : Djamel Hadji | Tous droits réservés"

Today’s scientific processes heavily depend on fast and accurate analysis of experimental data. Scientists are routinely overwhelmed by the effort needed to manage the volumes of data produced either by observing phenomena or by sophisticated simulations. As database systems have proven inefficient, inadequate, or insufficient to meet the needs of scientific applications, the scientific community typically uses special-purpose legacy software. With the exponential growth of dataset size and complexity, application-specific systems, however, no longer scale to efficiently analyse the relevant parts of their data, thereby slowing down the cycle of analysing, understanding, and preparing new experiments. I will illustrate the problem with a challenging application on brain simulation data and will show how the problems from neuroscience translate into challenges for the data management community. I will also use the example of neuroscience to show how novel data management and, in particular, spatial indexing and navigation have enabled today’s neuroscientists to simulate a meaningful percentage of the human brain. Finally I will describe the challenges of integrating simulation and medical neuroscience data to advance our understanding of the functionality of the brain.