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.
Media coverage on the distant future of AI and machine learning have painted a scary picture of machines going berserk, rampaging killer robots, and rogue self-driving cars. Those ugly manifestations of machine learning are unlikely to go beyond fiction. But the dangers of machine learning can—and already have—taken different routes. A couple of podcasts featuring El Mahdi El Mhamdi, PhD scholar at EPFL, shed important light on the dark side of AI—poisoned data sets, bad actors, AI-generated fake news, and the Byzantine problem—and his work on technical AI safety and robustness in biological systems.
Every weekday, avid followers of computer science wake up to a new writeup by the inimitable Adrian Colyer on his blog The Morning Paper. His insightful selections help bring practical ideas from the academia to the computing practitioner. In a year, readers are exposed to concepts and ideas from more than 200 papers. In his latest post, Adrian Colyer presents a paper co-authored by Alexandros Daglis, Mark Sutherland, and EcoCloud Founder-Director Babak Falsafi.
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.