IC Colloquium: Making Sense of Fast Big Data
Event details
| Date | 31.01.2019 |
| Hour | 14:15 › 15:15 |
| Location | |
| Category | Conferences - Seminars |
By: Anne-Marie Kermarrec - Mediego
IC Faculty candidate
Abstract:
Computing systems that make human sense of big data, usually called personalization systems or recommenders, and popularized by Amazon and Netflix, essentially help Internet users extracting information of interest to them. Leveraging machine learning techniques, research on personalization has mainly focused on improving the quality of the information extracted, according to some measure of quality. Yet, building an operational recommender goes far beyond, especially in a world where data is not only big but also changes very fast. This talk will discuss system challenges to scale to a large number of users and a growing volume of fastly changing data to eventually provide real-time personalization.
Bio:
Anne-Marie Kermarrec is a senior researcher at Inria, France where she led a research group on large-scale distributed systems from 2006 to 2015. She is currently the CEO of the Mediego startup that she founded in April 2015. Mediego provides content personalisation services for online publishers, and directly builds on her recent research. She got a PhD thesis from University of Rennes in 1996, and has been with Vrije Universiteit, NL and Microsoft Research Cambridge, UK. Anne-Marie received an ERC grant in 2008 and an ERC proof of Concept in 2013. She received from the French Academy of Science both the Montpetit Award in 2011 and the Innovation Price in 2017. She has been elected to the European Academy in 2013 and named ACM Fellow in 2016. Her research interests are large-scale distributed systems, peer to peer networks and system support for machine learning. She published more than 200 academic papers and received several best papers awards including the Test of time award for her work on gossip-based peer sampling.
IC Faculty candidate
Abstract:
Computing systems that make human sense of big data, usually called personalization systems or recommenders, and popularized by Amazon and Netflix, essentially help Internet users extracting information of interest to them. Leveraging machine learning techniques, research on personalization has mainly focused on improving the quality of the information extracted, according to some measure of quality. Yet, building an operational recommender goes far beyond, especially in a world where data is not only big but also changes very fast. This talk will discuss system challenges to scale to a large number of users and a growing volume of fastly changing data to eventually provide real-time personalization.
Bio:
Anne-Marie Kermarrec is a senior researcher at Inria, France where she led a research group on large-scale distributed systems from 2006 to 2015. She is currently the CEO of the Mediego startup that she founded in April 2015. Mediego provides content personalisation services for online publishers, and directly builds on her recent research. She got a PhD thesis from University of Rennes in 1996, and has been with Vrije Universiteit, NL and Microsoft Research Cambridge, UK. Anne-Marie received an ERC grant in 2008 and an ERC proof of Concept in 2013. She received from the French Academy of Science both the Montpetit Award in 2011 and the Innovation Price in 2017. She has been elected to the European Academy in 2013 and named ACM Fellow in 2016. Her research interests are large-scale distributed systems, peer to peer networks and system support for machine learning. She published more than 200 academic papers and received several best papers awards including the Test of time award for her work on gossip-based peer sampling.
Practical information
- General public
- Free
- This event is internal
Contact
- Host: Anastasia Ailamaki