After extensive research, Professor Rachid Guerraoui and colleagues at EPFL’s School of Computer and Communication Sciences (IC) have proposed a nearly zero-energy alternative to the bitcoin. The system, dubbed Byzantine Reliable Broadcast, represents a paradigm shift in the approach toward cryptocurrencies.
Today, we have a slew of media houses and streaming services that inundate consumers with audio, video, online, and print news. This has raised the specter of rampant news misinformation and disinformation. The threat is amplified by broadcasters who use the same source to disseminate news to consumers. Any bias in the original news source is perpetrated by all secondary services, thus delivering a limited view of news to consumers. However, researchers at EPFL’s Distributed Information Systems Laboratory (LSIR) in the School of Computer and Communication Sciences have developed an algorithm that can detect such biases and external influences in the news industry.
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.
Machine learning has become ubiquitous today with applications ranging from accurate diagnosis of skin cancers and cardiac arrhythmia to recommendations on streaming channels and gaming. However, in the distributed machine learning scheme, what if one ‘worker’ or ‘peer’ is compromised? How can the aggregation system be resilient to the presence of such an adversary?
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.