(last update 19/07/2024) 

Academic career

• Currently, Associate Professor of circuit theory (SSD ING-IND/31) at Electrical and Electronic Engineering - Dep. University of Cagliari, Italy.
• December 2010- December 2020, Research Scientist (permanent position) of circuit theory (SSD ING-IND/31) at Electrical and Electronic Engineering Dep. - University of Cagliari, Italy
• March 2007 - December 2010, Postdoc Researcher of circuit theory (SSD ING-IND/31) at Electrical and Electronic Engineering Dep. - University of Cagliari, Italy

Education

• June 2003: “Laurea” degree (magna cum laude) in Electrical Engineering, University of Cagliari, Italy, title of Master Thesis: A virtual tool for studying Power Quality disturbances using Wavelet Transform. 
• March 2007: PhD in Electrical Engineering with “Doctor Europaeus” certificate, University of Padova, Italy. PhD Thesis: A disruption prediction system for ASDEX Upgrade based on Neural Networks.

In charge of the following courses:

• Applied Electromagnetism. University of Cagliari, Cagliari (IT), Master of Science in Electrical Engineering (9 CFU) and Energetic Engineering (6 CFU). Academic years, from 2018-19 to currently.
• Introduction to Controlled Thermonuclear Fusion. University of Cagliari, Cagliari (IT), Industrial engineering PhD school. Academic years, from 2021-2022 to currently.
• Circuit theory. University of Cagliari, Cagliari (IT), Bachelor of Science in Civil Engineering (4 CFU) Academic years, from 2010-11 to 2018-19 and from 2023-24 to currently.

Research Topics

The research activity in the controlled fusion field is focused on the application of artificial intelligence algorithms and the development of data mining, pre and post-processing techniques for classification, prediction, optimization and diagnostics.

• Machine Learning algorithms for disruption prediction and avoidance at ASDEX Upgrade and JET Tokamaks. Application and implementation of data-driven algorithms able to promptly detect disruptions to be handle in real-time with appropriate actions. In this context, Artificial Neural Networks predictors have been developed to activate the disruption protection systems (see projects 13-15) to prevent the machine from damages due to the abrupt loss of plasma energy confinement.  Moreover, innovative Machine Learning algorithms have been applied for the detection of key events in disruptive paths to activate the discharge control system to seek to recover the plasma to the previous stable state (see projects 1, 8-10). The research activities have been developed in collaboration with the researchers from the ASDEX Upgrade team and the JET contributors. Research activities are developed in collaboration with researchers from Culham Centre for Fusion Energy (CCFE), Abingdon (UK), and at IPP Max-Planck-Institut für Plasmaphysik, Garching b. München (DE), where are located the controlled fusion experimental machines JET and ASDEX Upgrade, respectively.
• Artificial Neural Network applications for image diagnostics in controlled fusion. Application of Multi-layer perceptron and Deep Neural Networks for the analysis and the characterization of thermal events (strike-lines) in the W7-X stellarator divertor modules (see projects 11-12); inverse reconstruction of the thermal flux from IR temperature measurements on the instrumental calorimeter tiles for the ITER negative ion beam source (SPIDER); filament detections from 2D fast camera images for the spherical tokamak MAST Upgrade. Research activities are developed in collaboration with researchers from IPP Max-Planck-Institut für Plasmaphysik, Greifswald (DE), where is located W7-X, the Istituto Gas Ionizzati, Padova (IT), where is located the experiment SPIDER, and the Culham Center for Fusion Energy (CCFE), where is located MAST Upgrade.
• Data-mining techniques for the creation of a DEMO relevant off-normal event database.Application of data mining, pre and post-processing techniques for the creation of a multi-machine database of plasma perturbations inducing vertical displacement events (VDEs), impurity accumulation disruption and possible JET flux pumping candidates, in relevant DEMO experiments (see projects 5-7). Research activities are developed in collaboration with researchers from EUROfusion-Programme Management Unit, Garching (DE).
• White noise characterization and modelling of thermo-mechanical stresses of Pick-up coils. Assessment of the systematic error on the inverse plasma position and shape reconstruction due to the white noise effect on the in-vessel pick-up coil measurements. Thermo-mechanical stress analysis of torlon/copper ex-vessel pick-up coils using finite element code (see project 2-4).

Main research projects

• 2022-2027 Chief Scientific Investigator of the project "Construction of a Multi-Machine Database of MFE Experiment”, in the frame work of IAEA Coordinated Research Project F13022: Artificial Intelligence for Accelerating Fusion Research and Development.
• 2021 – DTT (Divertor Tokamak Test) 2021 Task DMA_Diagnostics_Magnetic. Sub task owner for La Tuscia - Univ. di Cagliari, work package ID 4.10.4.1.11_001, white noise characterization and FEM modelling of thermo-mechanical stresses of Pick-up coils.
• 2022 DTT 2022-DIA-DIC/DOC Diagnostics Project.  Task owner for Univ. di Cagliari. Task DIA-TEN-68527, white noise characterization and FEM modelling of thermo-mechanical stresses of Pick-up coils

Deliverable owner for ENEA-Univ. of Cagliari:

• 2023 - EUROfusion program, Work package DIV-IDTT.S.06-T025-D003. Task 2023 pick up coils FEM analysis progress of design activities.
• 2024, 2023, 2022, 2021 - EUROfusion program, Work package DES-FS.PLA.S-T014-D003, D002, D001. Task PLA.S.02-06.2/Disruption Expert 2023, 2022, 2021
• 2020 - EUROfusion program, Work package PMI5.3 Demo Physics design integration. Task PMI-5.3.2-T028/Plasma Perturbation database in DEMO relevant scenarios.
• 2019 - EUROfusion program, Work package PMI5.3 Demo Physics design integration. Task 5.3.2-T022: JET and ASDEX Upgrade plasma perturbation database in DEMO relevant scenarios.

Mentor of EUROfusion Engineering Grant:

• AWP24-EEG-ENEA/Aymerich, Real-time heat flux estimation in fusion devices through machine learning techniques, 01 June 2024- 31 May 2026.

Member of research team:

• EUROfusion program on Tokamak exploitation WPTE: 2021, 2022-24 Topic RT04, 2022-24 RT22-08.
• EUROfusion program on JET experimental campaigns WPJET1: 2021 Task M21-03 and Task; 2020 Task M18-04 T17-03; 2018-2019 Task M18-04; 2017 Task 17-14; 2015-216 Task T15-3; 2014 Task T13-23.
• EUROfusion program on medium-size tokamaks WPMST1: 2018-2020 Topic T06-AUG; 2017 Topic T7-AUG; 2015-16 Task T15-03; 2014 Task AUG14-1.1-1.
• EUROfusion program on W7-X OP1.2b: 2020-21 Task S1.X2.A.T2 and Task S1.X2.A.T2; 2019 S1.X2.A.T2 and Task S1.X2.A.T2; 2018 Task S1.P2.T7; 2018 Task S1.P2.T7 and WP18.S1. B3.T4.
• EUROfusion work Program on W7-X Exploitation. Task: 2023-W7X-3.3.2-ENEA-UC: Preparation and Support for W7-X Experiments in 2023.
• EFDA 2012 and 2013 Work Program Topical Area A7, Disruptions Prediction Avoidance Mitigation and Consequences, founded by EURATOM.
• Development of disruption protection tools for Tokamak reactors, founded by the EURATOM and ENEA agreement (2007-2013).

Awards

Highlight of the year 2015 for the article: Automatic disruption classification in JET with the ITER-Like Wall, 10.1088/0741-3335/57/12/125003. Plasma Physics and Controlled Fusion, International Atomic Energy Agency (IAEA).

Publications

She is author of 83 articles published on international journals, 18 conference papers and 3 book chapters, obtaining a h-index of 27 and 2097 citations (Scopus database, June 2024).