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Data Science Excellence Department Initiative (DS)

A new Data Science initiative at SISSA 

The Department of Physics at SISSA  is opening a new research line and a new path of doctoral training in Data Science.  Since its foundation in 1978, SISSA's main mission has been to train PhD students to the highest international standards. To maintain its competitive level, the School continuously adapts to the evolution of science, which always offers new challenges, often across the borders of traditional disciplines. 

Data Science is nowadays fundamental for the study of many physical systems. Analysis tools developed in this field were fundamental in many recent discoveries within particle physics, astronomy, materials sciences and biophysics. For example, in particle physics and astronomy, scientists grapple with very large data sets, high-dimensional parameter spaces and often low signa-to-noise. Applying machine learning to sift through the data for possible instances of a particle or process allows researchers to pinpoint an interesting event within a stack of trillions. In astronomy, data science and machine learning are often used to identify processes or interesting features (new galaxies, supernovas, possible black holes, dark matter signatures...), and to accelerate manyfolds otherwise untractable computational problems.  

To analyze data sets of such complexity it is necessary to develop new algorithmic strategies aimed at extracting from the data the relevant features that can be later used by the scientists to build models and, above all, to make or test predictions. The development and the understanding of these strategies, which are often inspired by statistical physics, form the core of the "Data Science for Sciences" approach that SISSA is developing. 


We aim to enter this extremely vital and dynamic field with a clear SISSA trademark: top-level theoretical research, motivated by specific problems in materials science, biophysics, cosmology, astrophysics. The new group will include world-leading scientists with expertise in machine learning, neural networks, deep learning, dimensional reduction, and Bayesian inference.

Data Science Modules on offer for 2020-21 


  • 02/11/2020: "Ethics in AI and ML" schedule and reading list available here. The module will be fully remote. Lectures' recordings available here (SISSA VPN required for access).
  • 02/11/2020: "Information Theory and Inference" will be fully remote. Lecture notes from this link. Lectures' recordings available here (SISSA VPN required for access).
  • 18/10/2020: All timetable information moved to Google calendar (see below) and dynamically updated.
  • 16/10/2020: Information for students, "Bayesian Inference I" available from this link.
  • 08/10/2020: Lecture materials for "Introduction to Statistical Inference and Modeling" available from this link. 

This initial curriculum is intended to form the backbone of what will eventually become the PhD in Data Science for AY 2021-22. 

Data Science modules will generally run in the afternoons, so that students from other PhD programmes (which are usually taught in the mornings) will be able to attend. 

We kindly request that students from other PhD programmes who are interested in following our modules register their interest by filling out this form. This is for logistical (especially in view of COVID-19 restrictions to teaching spaces) and pedagogical reasons. Deadline is Fri Oct 2nd 2020.

Our formal learning opportunities will be flanked with a vigorous programme of online seminars (the “SISSA Data Science Seminar Series”, or SISSA DS3), held approximately fortnightly from January 2021, with a focus on showcasing a young and diverse line-up of world-class speakers from all over the world. 

For questions about the Data Science educational offer at SISSA, please contact Prof. Roberto Trotta ( 


  • Students from the Data Science Excellence Department must take all core modules offered in Data Science and take at least 3 optional modules, which can also be chosen from the offering from other PhD programmes.  
  • Postdocs (and SISSA faculty) are welcome to attend if space allows (due to COVID restrictions); in case of limited places, PhD students will be prioritized. Streaming/recording/online delivery will be considered as necessary. 
  • To proceed to year 2, Data Science Excellence Department students must achieve an average of 27/30 in the the core modules.
  • Credit size conversion: 1 credit = 6 hrs of lectures or labs.
  • All of our Data Science modules are open to students from other PhD programmes. Credit can be accrued by such students with the written agreement of their PhD programme coordinator. 
  • Frontal lecturing generally takes place in 2 hrs slots, 2-4pm (but see timetable below for details, with some Labs, Ethics in AI and Journal Club taking place in the mornings due to timetabling constraints), while Labs are 3 hrs slots (2-5pm or 9-12am when necessary). 

Teaching Staff 

  • Guido Sanguinetti (SISSA) 
  • Roberto Trotta (SISSA)
  • Sebastian Goldt (SISSA)
  • Alessandro Laio (SISSA)
  • Andrea de Simone (SISSA/Uni Camerino)
  • Jean Barbier (ICTP)
  • Luca Bortolussi (UniTS) + Guest lecturers 
  • Guest lecturers for “Ethics in AI” module. 

Modules on offer and timetable 


Lectures will also be streamed on Zoom to enable students unable to attend in person to follow remotely. Zoom connection details will be shared with students in due course. 

In case of capacity issues of lecture rooms (i.e. due to excessive number of people due to COVID-19 capacity restrictions), priority for in-person lectures will be given to students taking the lecture for credit. 

Generally, lectures will take place in Aula 128-129, with some exceptions. Please consult the Google calendar below for the most up-to-date information. 

You can subscribe to the Google calendar (to integrate it in your own calendar handler) via this link.

  • Journal Club and Monographic courses: Aula 005
  • Introduction to Statistical Modelling: online (lecturer in remote connection; details will be circulated in due course)
  • Bayesian Inference I on 22/10 and 29/10 in Aula 136  (max 14 attendees)
  • Scientific Programming and Algorithms: in Aula 138



You can download at this link the full syllabus of the courses on offer 


SISSA Faculty Adjoint Faculty
Guido Sanguinetti Andrea de Simone
Roberto Trotta Jean Barbier
Sebastian Goldt  Luca Bortolussi