Title: Graph Neural Network in High Energy Physics with the ATLAS experiment

Abstract
Machine Learning is a rapidly expanding field with a wide range of applications in science. The Large Hadron Collider, the world’s largest particle accelerator, utilizes Neural Networks for various tasks, including flavor tagging, i.e. the process of identifying the flavor of the hadron that initiates a jet in a collision event. Graph Neural Networks are currently the primary machine-learning tool used for flavor tagging.

Biography
I obtained my Bachelor's Degree in Physics at the University of Parma, I moved to Trieste where I took my Master's Degree in 'Nuclear and Subnuclear Physics'. Now, I am a PhD student at the University of Trento and I collaborate with Fondazione Bruno Kessler (FBK).

Title: On whitening techniques for gravitational waves bursts analysis

Abstract:

Data conditioning is a fundamental step in analyzing gravitational wave data to obtain reliable results. Whitening filtering techniques are especially crucial as they bring hidden signals to the forefront. Despite their necessity, these techniques require a challenging a priori characterization of noise, which is not always easy to achieve. In this discussion, I will delve into the implementation of a new whitening technique within the framework of earth-based gravitational wave detectors.

Biography: 

I am originally from Sardinia, Cagliari, where I started my  Bachelor in physics. I kept my studies in high energy theoretical physics at the univeristy of Pisa. After working in a company for a while, I came back to Academia and I now am a PhD student in gravitational waves physics. I focus on data analysis of earth based detectors data withing the Lirgo-Virgo-Kagra collaboration 

Title: Sowing sustainability: training teachers and analyzing student mental models

Abstract
Exploring the impact of a multidisciplinary sustainability training course for teachers, focusing on behavioral activation and the quantitative analysis of students' mental models regarding the greenhouse effect through their drawings.

Biography
Currently a PhD student at the University of Trento, focusing on physics education, sustainability, and teacher training. Former Mathematics teacher at 'Immaginazione e Lavoro' in Turin (2022), and didactic tutor at 'Xké, The Curiosity Lab' (2019-2022). Holds a Master's in Physics and specialized credits in teaching methodologies from the University of Turin.

Title: First-principles modeling of photoexcited systems: how collisions shape the evolution of light-driven systems

Abstract
The dynamic field of ultrafast spectroscopy has experienced remarkable growth, driven by advancements in both theoretical and experimental techniques. Yet, the intricate details governing system relaxation post-illumination remain elusive. In this presentation, I will present a novel computational scheme, leveraging density functional theory and many-body perturbation. This approach addresses carrier relaxation and lattice thermalization, paving the way to the exploration of real-time dynamics of photoexcited systems

Biography
I earned both my Bachelor's and Master's degrees from the University of Trento. Currently, I am a Ph.D. student in the 'Materials Theory Group', where my research focuses on the first-principles modeling of materials properties within the realm of ultrafast spectroscopy. My work involves the theoretical analysis of photoexcited systems, and I am actively contributing to the development of innovative computational schemes for realistic large-scale simulations

Title: Vibrational dynamics of ultra-stable and conventional glasses

Abstract
I will briefly introduce how thermodynamic properties of conventional glasses and crystals are remarkably different at low temperatures, and how ultra-stable glasses apparently do not exhibit the typical glassy anomalies. I will then show the first measure of the vibrational density of states of an ultra-stable glass and of the corresponding conventional glass, and discuss the conclusions we reached, regarding thermodynamic properties as well.

Biography
I am a PhD student in the SDSC group at the University of Trento, in Cotutelle with the University of Grenoble. My research activity is focused on the study of relaxation dynamics of chalcogenide glasses and vibrational dynamics of ultra-stable glasses, exploiting a combination of experimental methods, which include laboratory-based spectroscopic techniques and advanced X-ray spectroscopies at large-scale facilities.

Title: Loss is More: Exploring the weight space of a perceptron via enhanced sampling techniques.

Abstract
The success of machine learning revealed the potential of neural networks but also raised fundamental questions: how does learning occur? Our approach utilizes enhanced sampling methods to explore the loss profile and reconstruct the density of states of networks, addressing optimization in rugged landscapes. Effective in real datasets, these methods enable exploring data dimensionality and structure impact, providing insights into the interplay between input data properties and network learning, and advancing our understanding of how artificial networks adapt to different information contexts.

Biography
I am Margherita, a PhD student in the “Statistical and Biological Physics” group and one of the representatives of the Physics PhD. Originally from Matera, I am in Trento because I completed both my Bachelor's and Master's degrees here. My research deals with modeling emergent phenomena in complex systems, revealing insights through concise and informative representations in the field of statistical mechanics.

Title: Sound propagation in dipolar supersolids

Abstract
Recently discovered supersolid phase of dipolar Bose-Einstein Condensates combines characteristics of two phases: superfluid and solid. This peculiar state of matter exhibits simultaneous spontaneous breaking of two symmetries leading to two Goldstone modes. In this talk, I will illustrate the most important features of supersolids and present how the measurement of the speed of sounds reveals the fundamental properties of these systems.

Biography
I obtained my Master's degree in applied physics at Warsaw University of Technology, where I was studying fermionic gases. I decided to change the spin to an integer and joined the Pitaevskii Center for Bose-Einstein Condensation. My research focuses on the supersolid phase of dipolar BECs. 

Title: A photonic neural network for error compensation in optical telecom links

Abstract
Chromatic dispersion is one of the main error sources that affect data transmission in optical fiber links. We experimentally demonstrate channel equalization via a photonic neural network featuring reconfigurability, versatility, negligible latency, and reduced power consumption compared to other currently available devices. 

Biography
Emiliano Staffoli spent all of his undergraduate studies at the University of Trento, where he got his master's degree in March 2022. He is currently a Ph.D. student at the Nanoscience Laboratory, where he proceeds in his research in Silicon-integrated photonics neural networks applied to telecommunications. Since 2022, he is also president of the local SPIE chapter.

Title: Time-resolved spectroscopic investigation of H2/N2 low-temperature plasmas

Abstract
The Ph.D. project aims to investigate the temporal evolution of relevant parameters, such as temperature and species densities, in pulsed H2/N2 plasmas. One of the main goals is to monitor and understand the production of atomic species and the processes leading to the formation of stable molecular products (e.g., ammonia - NH3).

Biography
I’m currently working in the Atomic and Molecular Physics group, here in Trento, for the first part of my Ph.D. project, while I’m going to spend the second half at ASML Netherlands N.V., a world leader company in the semiconductor industry.  My research is mainly focused on the characterization of H2/N2 low-temperature plasmas using spectroscopic techniques. In my free time, I enjoy defeating my colleagues in football matches.