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.

Efficient distributed learning solutions, taking into account adversarial behavior in both worker-server and peer-to-peer architectures.

Maintain any classifier throughout the lifetime of its usage.

Identify risks associated with new technologies and develop solutions to ward off such threats.

A new generic algorithmic building block to accelerate training of machine learning models on heterogeneous computing systems.

Deep learning (DL) algorithms for the computation of the satellite’s relative position and altitude.

Novel biosignal sampling strategies that reduce the amount of acquired data by orders of magnitude

The methodology will combine state-of-the-art Artificial Intelligence methods with investigative journalistic tactics.

Maximize resource utilization and concurrently achieve high parallelism and load balance.

Advanced techniques that will improve the applicability of adaptive sampling.

A web platform to capture, process, and visualize the Swiss and global news landscape.

Research will help reduce network bandwidth usage and response latency

Algorithms that combine three key aspects: accelerated, non-convex, and distributed training.

Self-awareness for dynamic energy management on mobile and wearable platforms.

Customer experience optimization is much improved if updates to the system are carried out in real time.

Developing learning systems that can be customized according to the skills and abilities of students.

Increase the usage of videos from Tamedia’s archives by enabling advanced search options.

An algorithm termed stochastic spectral descent (SSD) for training deep neural networks.

Strategies to automatically select the best accelerator to run a specific DNN training.

Generative adversarial networks (GANs) are a class of deep generative models, extensively used in AI.