Research on the performance of machine learning under computational considerations
We are interested in finding answers to the open questions including: a) How to predict the best achievable performance for a given ML task and a given data set, under a limited amount of computation. b) How to predict the performance of the existing methods, as compared to the best achievable performance. c) How to achieve the best performance by modifying training specifications or developing new algorithm. As part of this research, areas of application will be targeted, where the above-mentioned questions are of paramount significance. This includes ML tasks under uncertainty or time variability.
PhD recruitment is in process.
Ashkan Panahi (contact)
Assistant Professor at Chalmers, Computer Science and Engineering (Chalmers), Data Science
Chalmers AI Research Centre (CHAIR)
Funding Chalmers participation during 2020–
Related Areas of Advance and Infrastructure
Information and Communication Technology
Areas of Advance