Machine learning (ML) is becoming an increasingly essential tool to advance research in fields as diverse as Astronomy, Biophysics, Particle Physics, Environmental science, and Material Science. By extracting patterns and information from large and complex data sets, ML helps in understanding complex multi-scale phenomena that would otherwise be intractable.  The development of efficient, robust, interpretable and reproducible ML pipelines is thus crucial to advance scientific discovery in computational sciences.

 

We are excited to announce an the Advanced School in Applied ML at ICTP,  which will take place in Trieste (Italy) from May 27th to May 31st, 2024, dedicated to PhD students and junior researchers. The school aims to bridge the gap between theoretical notions in data science/ML and application of ML to cutting edge scientific problems, with a focus on scalability and advanced HPC methods for ML, and in particular for deep learning (DL). 

 

Lecturers from top-ranked research centers/universities and big tech companies (e.g. Intel, Nvidia, IBM) will cover the following topics: symbolic regression, Gaussian processes for time-series data analysis, transfer learning and domain adaptation, Graph Neural Networks and Transformers for multi-modal analysis, AI explainability, Neuromorphic computing, for energy-efficient DL training. Each topic will be complemented by the necessary tools to create efficient, scalable, and portable ML pipelines that exploit the capabilities of modern HPC infrastructures.

 

30 participants will be selected with attention to creating a cohort with balanced representation of countries (particularly, least developed countries), gender and under-represented groups.  

 

This school will be the first activity in the field of advanced applied Machine Learning organised by the brand-new ICTP International Consortium for Scientific Computing (ICSC).

Speakers:
Cristiano De Nobili, PI School
​Gabriel Fonseca-Guerra, INTEL
Caroline Heneka, University of Heidelberg
Nabeel Seedat, Cambridge
​Savannha Thais, Columbia University
Paolo Viviani, LINKS foundation/B-CRATOS


Poster abstract: Applicants are encouraged to submit an abstract for a poster session. Please use the ICTP templates that are available below for download.

We would also suggest to apply to the following activity that may also be of interest to you: "Youth in High Dimensions: Recent Progress in Machine Learning, High-Dimensional Statistics and Inference".

Grants: A limited number of grants are available to support the attendance of selected participants, with priority given to participants from developing countries. There is no registration fee.