The prestigious MICRO Test of Time (ToT) Award is an annual feature at the IEEE/ACM International Symposium on Microarchitecture. This year was the 51st edition of the conference, held between October 20 and 24 in Fukuoka City, Japan. In the course of the conference, the Awards Committee named Thomas Ball and James R. Larus as the winners of the fifth MICRO Test of Time Award. That is an honor for EPFL as well; Professor Larus is Dean of the School of Computer and Communication Sciences (IC).
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?
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.
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.