Speaker Sessions

Collection of some freely available talks in which I have been involved as a speaker for conferences, events and podcasts.

Datacast Interview: Building Data Science Projects

Adventures in Machine Learning: Data Paradoxes in Data Sets

Data Driven People Interview (Italian)

Microsoft Reactor of London: Paradoxes in Data Science

Paradoxes are a class of phenomena that arise when, although starting from premises known as true, we derive some sort of logically unreasonable result. As Machine Learning models create knowledge from data, this makes them susceptible to possible cognitive paradoxes between training and testing. In this talk, I walk you through some of the main paradoxes associated with Data Science and how they can be identified. This talk has also been performed for the Data Talks Club and Machine Learning Milan communities.

2020 SAS UK&I Forum

Intro to Viya demo session at the 2020 SAS UK&I Virtual Forum. More information about this event is available at this link.

Global AI On Tour: Causal Reasoning In Machine Learning

Nowadays Machine Learning models, are able to learn from data by identifying patterns in large datasets. Although, humans might be able to perform a same task after just examining a few examples. This is possible thanks to the inherit humans ability to understand causal relationships and use inductive inference in order to assimilate new information about the world. In this demonstration given at the Global AI On Tour conference, we are going to find out more about how to embed Causal Reasoning in Machine Learning.

DataScienceSeed: Causal Reasoning in ML: Spiegare “perché” (Italian)

Al giorno d’oggi le tecnologie di Machine Learning si basano solo sulle correlazioni tra le diverse “features”. Ció nonostante, questo approccio può eventualmente portare a conclusioni errate poiché correlazioni non implicano necessariamente causalità.

Towards Data Science: GPU Accelerated Data Analytics & Machine Learning

GPU acceleration is nowadays becoming more and more important. The main two drivers for this shift are:

  1. The world’s amount of data is doubling every year.
  2. Moore’s law is now coming to an end because of limitations imposed by the quantum realm.

As a demonstration for this shift, an increasing number of online data science platforms is now adding GPU enabled solutions. Some examples are: Kaggle, Google Colaboratory, Microsoft Azure and Amazon Web Services (AWS).

Google Developers Group (Zurich): Paradoxes in Data Science

GCP deployment instructions provided during the presentation are available at this link.



PyCon Italy - Florence 2023

Swiss RE Initiatives

Partnership with Palantir (Foundry), Hack Zurich 2022 and Insurance Data Science Conference 2024.



If you want to keep updated with my latest articles and projects follow me on Medium and subscribe to my mailing list. These are some of my contacts details:

Buy Me A Coffee

Subscribe to my mailing list to be updated about my new blog posts!

* indicates required