Martin Jaggi, Tenure Track Assistant Professor at EPFL’s School of Computer and Communication Sciences, has won a Google Focused Research Award for 2018 in the area of Machine Learning. The award-winning proposal was on “Large-Scale Optimization: Beyond Convexity,” jointly with Alexandre d’Aspremont and Francis Bach.

The project proposes convergence acceleration techniques for solving generic optimization problems, including deep learning. This is of immense relevance today because of the sheer number and complexity of deep learning applications. In their study, Martin Jaggi and his coauthors propose an approach that tackles non-convex problems and deep neural networks with reduced implementation cost. This is because the complexity overhead is much less than original training algorithms and the proposed scheme allows reusability of existing methods, such as neural network training software. Their approach toward acceleration performance and distributed training could become a core component of modern deep neural network training pipelines.

The research is of crucial interest to Google’s continuous commitment to back innovative research in computer science and engineering. To further that commitment, Google instituted the Focused Research Awards program in 2010. Since then, the program has supported collaborations in more than twenty key research areas that are of interest to both the academic community and Google. They include Machine Learning, Artificial Intelligence, Algorithms, Cloud Computing, Geomapping, and Networking.

Martin Jaggi’s core areas of expertise are machine learning, optimization algorithms for learning systems, and text understanding. Before joining EPFL, he completed his Ph.D. on Learning and Optimization from ETH Zurich and worked as a post-doctoral researcher at ETH Zurich, at the Simons Institute in Berkeley, US, and at Ecole Polytechnique in Paris, France.