University of Padova, Italy
Ph.D. Physics, February 2015

University of Pisa, Italy
S.M. Physics, April 2011

University of Bologna, Italy
S.B. Physics, October 2008

IAEA Consultant (Vienna, Austria), Nuclear Plasma Fusion Specialist

December 2021 – June 2022

Special Service Agreement to 1) contribute to, review and edit the ‘AI for Atoms’ report from the 2021 Technical Meeting on Artificial Intelligence for Nuclear Technology and Applications; 2) contribute to, review and edit the ‘World Survey of Fusion Devices’ based onthe IAEA Fusion Device Information System.

MIT Plasma Science and Fusion Center (Cambridge, MA), 2016 – present

Research Scientist (2019 – present)

Postdoctoral Associate (2016- January 2019)

• Research on disruptions and disruption warning algorithms through Machine Learning techniques across many different devices, from Alcator C-Mod and DIII-D, to EAST and KSTAR in Asia.
• Realization of real time algorithms, eventually integrated in the Plasma Control System.
• Leader of MIT-PSFC Machine Learning Working Group.

Synergistic activities

• Leader of the MIT-PSFC Machine Learning Working Group: its goal is to foster the application of Machine Learning techniques to boost research in different PSFC Plasma Physics topical areas, by closely working with and mentoring graduate students and postdocs. Monthly seminars are organized with guest lecturers from the international fusion community. https://www.dropbox.com/sh/k4s6f9xoncxzts2/AABR-Zl713LjaTkHP0AUiUSfa?dl=0
• Organizer (PI) of the recurrent DOE-sponsored and PSFC-hosted Computational Physics School for Fusion Research (2019, 2021, 2022): https://sites.google.com/psfc.mit.edu/cps-fr-2022/home
• Organizer of the APS-DPP Mini-conferences on Machine Learning, Data Science, and Artificial Intelligence in Plasma Research (APS-DPP 2018, 2021).
• Organizer of the Nuclear Fusion working group session at the 2021 virtual IAEA Technical Meeting on Artificial Intelligence for Nuclear Technologies and Applications.
• Member of the International Tokamak Physics Activity (ITPA) MHD, Disruptions & Control Topical Group, and spokesperson of the MDC-22 joint activity “Development of ITER DMS trigger”.
• Member of the Sherwood Theory conference Executive Committee (2019-2022), and member of the Program Committee of the IAEA Technical Meeting on Plasma Disruptions and their Mitigation (2020, 2022) and of the joint IAEA/PPPL Theory and Simulation of Disruptions workshop (2021).
• Participant in the workshop cosponsored by Fusion Energy Sciences and Advanced Scientific Computing Research Programs “Advancing Fusion with Machine Learning Research”. Contributor of the final report for the Department of Energy: https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_R eport.pdf?la=en&hash=27C6DA2A9A92F884DC618FCB928A89F4C39BD764
• Proposals reviewer for the Department of Energy, Office of Energy Science.
• Editorial Board Member of the Nuclear Fusion IOP journal (starting 2021); guest editor of IEEE-TPS Special Issue on Machine Learning, Data Science, and Artificial Intelligence in Plasma Research (2019); guest editor for Journal of Fusion Energy (2022)
• Manuscript reviewer for the following journals: Nature Physics, Nuclear Fusion, Plasma Physics and Controlled Fusion, Fusion Engineering and Design, IEEE Transaction of Plasma Science, Journal of Plasma Physics.

UniCredit Business Integrated Solutions S.C.p.A., Milan, Italy, 2015 –2016

Data Scientist

• Data Scientist in UBIS ScpA in the Big Data group. Worked on Big Data demands from ideation, proof of value to delivery process and contributed to create statistical models that responded to specific business needs, such as Customer Relationship Management. Developed innovative data analysis solutions through advanced statistics and Machine Learning.

Consorzio RFX, National Research Council (CNR), Padua, Italy, 2012 – 2015

PhD student and Research Scientist

• Investigated local transport properties and their modulation depending on the magnetic topology in presence of externally applied magnetic perturbations. Studied the effects that relaxation events inside the plasma have on its boundary topology. Analysis were conducted on RFX-mod. The link between magnetic topology and local transport measurements was explored through a field line tracing code.
• Installation and analysis of data coming from the ExB probe, previously used on COMPASS and ASDEX-Upgrade.

Institute of Plasma Physics, CAS CR, Prague, Czech Republic, May 2014

Visiting Research Scientist

• One-month collaboration project under the Work Package ER-01/ENEA_RFX-02 “Magnetic reconnection in fusion plasmas”, approved by the EUROFUSION organization. Analysis of measurements of ion temperature profile coming from the ExB probe installed on COMPASS.

University of Pisa , Master Thesis, 2018 – 2011

• Development of a transfer model, by studying a two neutron process taking place in the reaction 13C(18O,16O)15C at 84 MeV incident beam energy. The experiment was realized using the large acceptance magnetic spectrometer MAGNEX, at LNS (“Laboratori Nazionali del Sud”) laboratories.
• Theoretical calculations were consistent with the experimental data and capable of describing the background that lays below the resonances by considering only the elastic part of the transfer to the continuum reaction: 14C(17O,16O)15C.

• C. Rea et al. “Disruption Prevention via Interpretable Data-Driven Algorithms on EAST and DIII-D”, (2021) 28th IAEA Fusion Energy Conference IAEA pp EX/P1–25.
• M.D. Boyer, C. Rea, M. Clement, “Toward active disruption avoidance via real-time estimation of the safe operating region and disruption proximity in tokamaks”, Nuclear Fusion (2021), Nuclear Fusion 62 (2022) 026005
• J.X. Zhu, C. Rea et al. “Scenario adaptive disruption prediction study for next generation burning-plasma tokamaks”, Nuclear Fusion 61 (2021) 114005
• W. Hu, C. Rea et al. “Real-time prediction of high density EAST disruptions using Random Forest”, Nuclear Fusion 61 (2021) 066034
• K.J. Montes, C. Rea et al. “A semi-supervised machine learning detector for physics events in tokamak discharges”, Nuclear Fusion 61 (2021) 026022 https://doi.org/10.1088/1741-4326/abcdb9
• J.X. Zhu, C. Rea et al. “Hybrid deep-learning architecture for general disruption prediction across multiple tokamaks”, Nuclear Fusion 61 (2021) 026007 https://doi.org/10.1088/1741-4326/abc664
• C. Rea et al. “Progress Towards Interpretable Machine Learning-based Disruption Predictors Across Tokamaks”, Fusion Science and Technology (2020), 76 912–924 ISSN 1536-1055 https://www.tandfonline.com/doi/full/10.1080/15361055.2020.1798589
• C. Rea et al. “A Real-Time Machine Learning-Based Disruption Predictor on DIII-D”, Nuclear Fusion 59 (2019) 096016 doi: https://doi.org/10.1088/1741-4326/ab28bf
• K.J. Montes, C. Rea et al. “Machine learning for disruption warning on Alcator C-Mod, DIII-D, and EAST”, Nuclear Fusion 59 (2019) 096015 doi: https://doi.org/10.1088/1741-4326/ab1df4
• R.A. Tinguely, K.J. Montes, C. Rea, et al. “An application of survival analysis to disruption prediction via Random Forests”, Plasma Physics and Controlled Fusion 61 (2019) 095009 doi: https://doi.org/10.1088/1361-6587/ab32fc
• C. Rea et al. “Disruption prediction investigations using Machine Learning tools on DIII-D and Alcator C-Mod”, Plasma Physics and Controlled Fusion 60 (2018) 084004 doi: https://doi.org/10.1088/1361-6587/aac7fe
• C. Rea and R.S. Granetz, “Exploratory Machine Learning studies for disruption prediction using large databases on DIII-D”, Fusion Science and Technology 74:1-2, 89-100 (2018) doi: 10.1080/15361055.2017.1407206
• C. Rea et al., “Comparative studies of electrostatic turbulence induced transport in presence of Resonant Magnetic Perturbations in RFX-mod”, Nuclear Fusion 55 (2015) 113021 https://stacks.iop.org/0029-5515/55/113021
• N. Vianello, C. Rea et al., “Magnetic perturbations as a viable tool for edge turbulence modification”, Plasma Physics and Controlled Fusion 57 (2015) 014027 https://stacks.iop.org/0741-3335/57/i=1/a=014027
• F. Cappuzzello, C. Rea et al., “New structures in the continuum of 15C populated by two-neutron transfer”, Physics Letters B 711 (2012) 347–352 https://www.sciencedirect.com/science/article/pii/S0370269312004042

Selected presentations

• C. Rea et al. invited tutorial “Interpretable Machine Learning Accelerating Fusion Research”, 64th Annual Meeting of the APS Division of Plasma Physics (2022).
• C. Rea et al., “Disruption Research for SPARC”, 64th Annual Meeting of the APS Division of Plasma Physics (2022) contributed talk.
• C. Rea, “Interpretable Machine Learning Accelerating Fusion Research – the disruption challenge”, invited lecture at the Joint ICTP-IAEA Advanced School/Workshop on Computational Nuclear Science and Engineering (2022).
• C. Rea et al. invited talk at the 2021 virtual Theory and Simulation of Disruptions Workshop organized in cooperation with International Atomic Energy Agency and PPPL- July 19-23, 2021.
• C. Rea et al. invited talk at the 1st IAEA Technical Meeting on Plasma Disruptions and their Mitigation, IAEA TM PDM 2020 https://conferences.iaea.org/event/217/contributions/16715/
• C. Rea et al. invited talk at the Conference on Data Analysis, CoDA 2020 https://custom.cvent.com/F6288ADDEF3C4A6CBA5358DAE922C966/files/d4f582b402aa4 eb991fe2ffadc4f5db8.pdf
• C. Rea et al. “Progress Towards Interpretable Machine Learning-based Disruption Predictors Across Tokamaks” 24th MHD Stability and Control Workshop (2019) contributed talk.
• C. Rea et al. “Interpretable Disruption Prediction Using Random Forest on EAST and DIII-D”, 61st Annual Meeting of the APS Division of Plasma Physics (2019) contributed talk in the “Research in support of ITER” session.
• C. Rea et al. “Characterized Disruption Predictions Using Random Forest Feature Contribution Analysis”, 3rd IAEA Technical Meeting on Fusion Data, Processing, Validation and Analysis (2019) invited talk.
• C. Rea et al. “Investigating disruption prediction with Machine Learning”, 22nd MHD Stability and Control Workshop (2017) invited talk.

Media

LinkedIn Profile
PSFC Profile
ORCID
Web of Science
CPS-FR media
The Verge
Careers for Women in Fusion IAEA Webinar
ITU-IAEA AI for Atoms Webinar

MIT News
Taming fusion with machine learning (MIT News)
Three Questions: Robert Granetz on fusion research (MIT News)