A Center for Sustainable Cloud Computing

Analytics & Applications

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.


Adversarial Machine Learning
Efficient distributed learning solutions, taking into account adversarial behavior in both worker-server and peer-to-peer architectures.
AI-Driven Classifier Building Pipeline
Maintain any classifier throughout the lifetime of its usage.
Armasuisse Project
Identify risks associated with new technologies and develop solutions to ward off such threats.
Co-located Deep Learning Training and Inference
duhl-new-strategy-to-render-10x-faster-machine-learning DuHL: New Strategy to Render 10X Faster Machine Learning
A new generic algorithmic building block to accelerate training of machine learning models on heterogeneous computing systems.
Embedded AI for Aerospatial Navigation
Deep learning (DL) algorithms for the computation of the satellite’s relative position and altitude.
Energy-Efficient Acquisition and Embedded Processing of Bio-signals
Novel biosignal sampling strategies that reduce the amount of acquired data by orders of magnitude
Federated Machine Learning
over Fog/Edge/Cloud Architectures
Firmenich Project
Innovation in fragrance and taste creation.
Framing FFs
Framing analysis of online discourse of returning foreign fighters and their families
Identifying the Impact of Propagandistic Social Media Accounts
The methodology will combine state-of-the-art Artificial Intelligence methods with investigative journalistic tactics.
Large-scale Data Analytics Large-scale Data Analytics
Maximize resource utilization and concurrently achieve high parallelism and load balance.
learning-based-dimensionality-reduction Learning-based Dimensionality Reduction
Advanced techniques that will improve the applicability of adaptive sampling.
Media Observatory Initiative
A web platform to capture, process, and visualize the Swiss and global news landscape.
ML-Enabled IoT Devices and Embedded AI
Research will help reduce network bandwidth usage and response latency
Distributed Machine Learning Benchmark
Modularity and Scalability in ML
Algorithms that combine three key aspects: accelerated, non-convex, and distributed training.
Multimodal and Personalized Methods for Health and Wellness Monitoring
Self-awareness for dynamic energy management on mobile and wearable platforms.
Real-time Event Processing Real-time Event Processing
Customer experience optimization is much improved if updates to the system are carried out in real time.
scalable-learning-machines Scalable Learning Machines
Developing learning systems that can be customized according to the skills and abilities of students.
A Platform for Real-Time Evaluation of News Articles
Tamedia Video Concierge
Increase the usage of videos from Tamedia’s archives by enabling advanced search options.
time-data-trade-off Time-data Trade-off
Accelerating the process of mathematical optimization.
training-dnn-using-ssd Training DNN Using SSD
An algorithm termed stochastic spectral descent (SSD) for training deep neural networks.
Training for Recommendation Models on Heterogeneous Servers
Strategies to automatically select the best accelerator to run a specific DNN training.
Training GANs: A Convex Optimization Perspective
Generative adversarial networks (GANs) are a class of deep generative models, extensively used in AI.