The Swiss National Supercomputing Centre (CSCS), the Institute of Computational Sciences (ICS) at USI (Università della Svizzera italiana), and the Data Analytics and Data-Driven Simulations program (DADSi, part of FoMICS) organized the Summer School on “Accelerating Data Science with HPC”, which took place at CSCS in Lugano from September 4 to 6, 2017.

Inquisitive minds want to know what causes the universe to expand, how M-theory binds the smallest of the small particles or how social dynamics can lead to revolutions. In recent centuries, developments in science and technology brought us closer to explore the expanding universe, discover unknown particles like bosons or find out how and why a society interacts and reacts. To explain the fascinating phenomena of nature, Natural scientists develop complex 'mechanistic models' of deterministic or stochastic nature. But the hard question is how to choose the best model for our data or how to calibrate the model given the data.

The way that statisticians answer these questions is with Approximate Bayesian Computation (ABC), which we learnt on the first day of the summer school and which we combined with High Performance Computing (HPC). The second day focused on a popular machine learning approach 'Deep-learning' which mimics the deep neural network structure in our brain, in order to predict complex phenomena of nature. The summer school taked a route of open discussion and brainstorming sessions where we explored two cornerstones of today's data-science, ABC and Deep Learning being accelerated by HPC with hands on examples and exercises.

Playlist with all the videos »

Advanced Approximate Bayesi Computations I, Carlo Albert (EAWAG) »

Deep Learning and Automatic Differentiation, Baydin (University of Oxford) »

Inference Compilation, Tuan Anh Le (University of Oxford) »

Likelihood free inference: Quantify parameter and model uncertainty in complex systems, Antonietta mira & Ritabrata Dutta (USI) »

Probabilistic Programming, Deep Learning and Inference Compilation, Frank Wood (University of Oxford) »

Probabilistic Programming, Frank Wood (University of Oxford) »

Choice of Discrepancy and Summary: Classification as a cure, Ritabrata Dutta (USI) »