Anastasia Ailamaki, professor of Computer and Communication Sciences at EPFL and co-founder of RAW Labs SA, has been honored with the SIGMOD E.F. Codd Innovation Award. The award recognizes her “pioneering work on the architecture of database systems, its interaction with computer architecture, and scientific data management.” She joins a distinguished group of past awardees, all of whom are influential scientists in the field of database management.
The good old roll of the dice is the archetype of randomness. And then there are lottery drawings and competitions where the outcome depends on generating random numbers. However, verifiable randomness, or the lack of predictability, continues to be a deep-rooted problem in cryptography. The newly constituted League of Entropy, with EPFL as a founding member, has decided to tackle the problem head on.
EPFL’s home-grown programming language Scala has won this year’s Programming Languages Software Award. The honor is awarded by ACM SIGPLAN each year to an individual or an institution to recognize the development of a software system that has had a significant impact on programming language research, implementations, and tools. Scala was originally developed by Professor Martin Odersky in 2004 at the School of Computer and Communication Sciences (IC). Professor Odersky now heads the Scala Center, an open-source foundation for the software based at EPFL.
The 2019 Spring Simulation Conference (SpringSim’19) concluded on May 2 at Tucson, Arizona. During the four-day event, many original papers were presented on the theory and practice of modeling and simulation in the scientific and engineering fields. The conference was especially significant for EPFL and EcoCloud because a paper co-authored by PhD scholar Yasir Mahmood Qureshi was selected for the Runner-up Paper Award.
We are investigating new stochastic theories and analytics including statistical learning techniques, machine training and inference systems and their applications to IoT, social media and big data platforms.
We are investigating technologies to maximize efficiency with in-memory data services, in-situ query processing on streamed data, and on-demand query engine customization using multi-objective compiler optimization.
We are investigating a three-pronged approach to holistic data platforms and datacenter efficiency: vertical integration to minimize data-movement; specialization to optimize work per service; and approximation to tailor work for output quality.
We are investigating technologies spanning from decentralized trust and cryptography to robustness and resiliency of natural processes to strengthen security, privacy and trust in data platforms and systems.