Anastasia Ailamaki

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Anastasia Ailamaki is a Professor of Computer Sciences at the Ecole Polytechnique Federale de Lausanne (EPFL) in Switzerland and the co-founder of RAW Labs SA, a swiss company developing real-time analytics infrastructures for heterogeneous big data. Her research interests are in data-intensive 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 data management to support computationally- demanding, data-intensive scientific applications. 

She has received an ERC Consolidator Award (2013), 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), an NSF CAREER award (2002), and ten best-paper awards in database, storage, and computer architecture conferences. She holds a Ph.D. in Computer Science from the University of Wisconsin-Madison in 2000. She is an ACM fellow, an IEEE fellow, the Laureate for the 2018 Nemitsas Prize in Computer Science, and an elected member of the Swiss National Research Council. She has served as a CRA-W mentor, and is a member of the Expert Network of the World Economic Forum. 

EPFL IC IINFCOM DIAS
BC 226 (Bâtiment BC)
Station 14
1015 Lausanne

Web site:  Web site:  https://dias.epfl.ch/

Web site:  Web site:  https://sin.epfl.ch

Web site:  Web site:  https://ssc.epfl.ch

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Administrative data

Fields of expertise

Database management systems, scientific applications, computer architecture 

Publications

Infoscience publications

Teaching & PhD

Teaching

Computer Science

Communication Systems

Courses

Data-intensive systems

This course covers the data management system design concepts using a hands-on approach.

Systems for data management and data science

This course is intended for students who want to understand modern large-scale data analysis systems and database systems. The course covers fundamental principles for understanding and building systems for managing and analyzing large amounts of data. It covers a wide range of topics and technologi