Martin Jaggi, Tenure Track Assistant Professor at EPFL’s School of Computer and Communication Sciences, has won the Google Focused Research Award for 2018 in the area of Machine Learning. The award-winning investigation was on “Large-Scale Optimization: Beyond Convexity,” completed jointly with Alexandre d’Aspremont and Francis Bach.
In about two months’ time, participants will assemble in Seattle for the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2018). Apart from the academic discourses that will take place at the four-day conference (November 6-9), the event is also of particular interest for EPFL because two of its outstanding researchers will be awarded the Best Paper Award for their contribution to the previous edition of the annual event.
The Dimitris N. Chorafas Foundation recognizes outstanding scientific work in selected fields in engineering sciences, medicine, and natural sciences. The winners are chosen each year from among the select list of graduating doctorate students submitted by the Foundation’s partner universities in Europe, North America, and Asia. One of this year’s awardees is Manos Karpathiotakis, who completed his PhD at EPFL’s Data-Intensive Applications and Systems (DIAS) Laboratory in 2017 and is currently a scientist at the laboratory.
In a press release last month, the Takis & Louki Nemitsas Foundation announced the selection of Anastasia Ailamaki, Professor and Director at EPFL’s Data-Intensive Applications and Systems Laboratory, as the Laureate of the NEMITSAS Prize 2018 in Computer Science.
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.