Topics of Interest

A more specific list of topics of interest for speakers and attendees to consider:

• Incorporating realtime and ad-hoc data analytics into applications and deployment on supercomputing and cluster platforms
• Scientific data set creation, ingest, curation, and analysis with stochastic approaches
• Computational steering through machine learning models
• Meta-data and data metrics collection and generation for large data collections and output data sets
• Multi-precision inference methods and their use on modern hardware for simulation data
• Novel use of discriminative and generative machine learning approaches for scientific data sets including reinforcement learning
• Modern HPC storage issues when dealing with integration of computational simulation outputs with data analytics software
• Synchronous and asynchronous learning approaches for methods related neural network training, stochastic gradient descent, loss-function engineering, etc.
• Model derivation and training for scalable simulations and data sets
• Deployment of statistical models and their implementations such as TensorFlow, (Py)Torch, Caffe 1/2, Keras, combined with their integration with large scale simulations through containers (Kubernetes, Docker, Singularity, OpenShift), virtualizaiton, colocation, etc.