Scaling-up Data Science

In the “good ol’ days” – if they have ever existed – we used to have models aimed at explaining phenomena via a limited number of parameters and we could test them using a small amount of data. When we collected new data, we only had to feed them into the model and compute the outcome.

Nowadays, statistical and machine learning models can have millions of parameters and we can collect billions of heterogeneous datapoints coming from different sources, and no computer in the world is able to process such quantities in a reasonable amount of time. That’s what computational algorithms are for: they are processes that come to around the same results of the original model, but in a simpler and faster way.

There are some issues, though. We don’t always exactly understand why a computational algorithm works and, if it does, we can’t be sure it will work as well with different or considerably larger datasets.

“This lack of understanding results in the routine use of inefficient and largely suboptimal algorithms, and makes the design of efficient algorithms for practically used models something of an art,” said Giacomo Zanella, Assistant Professor at Bocconi Department of Decision Sciences.

Zanella obtained a €1.5 ERC Starting Grant from the European Research Council (ERC) to better understand computational algorithms for large-scale probabilistic models, thus making their design not an art, but a science. The project (PrSc-HDBayLe - Provable scalability for high-dimensional Bayesian Learning) aims to single out the most promising algorithms using rigorous and innovative mathematical techniques, and to produce guidelines to improve them and develop new ones.

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