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Cristina Rea
Cristina Rea
Principal Research Scientist
Division Head, Data Science
crea@mit.edu
NW17-182
Lab Website

Dr. Rea is an internationally renowned leader in Artificial Intelligence and Machine Learning applications to design pre-dictive algorithms for disruptive instabilities, and further deploying them in real‑time control systems for active disruption avoidance and mitigation. Her interest focuses in equipping these black‑box tools with explainable models capable of bridg-ing their predictions to the physics understanding of the drivers of the instabilities leading to the final loss of control.

Dr. Rea has served on several DOE expert panels on AI since 2019, and separately served on the Fusion Energy Sciences Advisory Committee (FESAC) Decadal Plan Subcommittee, charged to reassess DOE‑FES program elements (2024).

Dr. Rea leads the ITPA MDC‑22 joint experiment since 2022 on developing the Disruption Mitigation System (DMS) trigger for ITER, where most of the research development is centred around ML‑enabled tools.

Dr. Rea has made crucial contributions in IAEA‑sponsored events, in particular leading the writing of the Nuclear Fusion chapter [7] of the publication following the IAEA Technical Meeting on Artificial Intelligence for Nuclear Tech-nologies and Applications.

Dr. Rea has become the PSFC Liaison Officer with IAEA in 2023, as PSFC got designated as the first fusion IAEA Collab-orating Centre: https://disruptions.mit.edu/news/2023/iaea-cc/.

Dr. Rea serves on several Program Committees for Fusion research and AI, lastly as Programme Chair of the 2023 IAEA Workshop on Artificial Intelligence for Accelerating Fusion and Plasma Science.

Organizer of the 2026 Long Program on “Multi‑Fidelity Methods for Fusion Energy” at UCLA’s Institute for Pure and Applied Mathematics (IPAM): https://tinyurl.com/ipam2026.

Organizer of the recurring summer program “Computational Physics School for Fusion Research”, sponsored by DOE Fusion Energy Sciences and hosted by MIT PSFC: https://sites.google.com/psfc.mit.edu/cps-fr-2024/home.

Education

PhD, Physics (2014)
University of Padova
-Investigation of local transport properties, magnetic topology modulation, and relaxation events in tokamak and reversed field pinch

MS, Physics (2011)
University of Pisa
-Modeling two‑neutron nuclear transfer in exotic ion beams

BS, Physics (2008)
University of Bologna

Data Science Division Head (2025 - present)

Accelerating plasma science and technology research via AI/ML at PSFC

Principal Research Scientist (2023 - present)

Leading PSFC Disruption Studies research and AI initiatives

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, 2008 – 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.
  • Cristina Rea, “Machine learning applications enabling fusion energy: Recent developments,” Journal of Fusion Energy, 44, no. 2, p. 39, 2025.
  • M. Wang, Cristina Rea, O. So, C. Dawson, D. T. Garnier, and C. Fan, “Active ramp‑down control and trajectory design for tokamaks with neural differential equations and reinforcement learning,” Communications Physics, vol. 8, p. 231, 2025. DOi: 10.1038/s42005-025-02146-6. [Online]. Available: https://doi.org/10.1038/s42005-025-02146-

6, **.

  • M. Wang, A. Pau, Cristina Rea, et al., “Learning plasma dynamics and robust rampdown trajectories with predict-first experiments at TCV,” Nature Communications, vol. 16, no. 1, p. 8877, 2025. DOi: 10.1038/s41467-025-63917-x. [Online]. Available: https://www.nature.com/articles/s41467-025-63917-x, **.
  • Keith, C. Nagpal, Cristina Rea, and R. A. Tinguely, “Risk‑aware framework development for disruption prediction: Alcator C‑Mod and DIII‑D survival analysis,” Journal of Fusion Energy, vol. 43, no. 1, p. 21, 2024, **.
  • Maris, Cristina Rea, A. Pau, et al., “Correlation of the L‑mode density limit with edge collisionality,” Nuclear Fusion, vol. 65, no. 1, p. 016 051, Dec. 2024. DOi: 10.1088/1741-4326/ad90f0, **.
  • Maris, A. Wang, Cristina Rea, R. Granetz, and E. Marmar, “The impact of disruptions on the economics of a tokamak power plant,” Fusion Science and Technology, pp. 1–17, 2023. DOi: 10.1080/15361055.2023.2229675, **.
  • Cristina Rea, Mordijck, M. Murillo, D. Humphreys, B. Spears, and M. Barbarino, “Fusion. Chapter 9,” in Artificial In-telligence for Accelerating Nuclear Applications, Science and Technology, 2022.
  • Cristina Rea, Montes, K. Erickson, R. Granetz, and R. Tinguely, “A real‑time machine learning‑based disruption pre-dictor in DIII‑D,” Nuclear Fusion, vol. 59, no. 9, p. 096 016, 2019.

Selected Invited Talks and Conference Presentations

Summer 2025. AI‑Accelerated Fusion: Advancements in Disruption Prevention and Performance Optimization. Invited Talk: Fusion For Energy Technology Development Planning Workshop, Barcelona, Spain.

Fall 2023. A review of explainable Machine Learning accelerating Fusion science. Invited Keynote: 4th Fusion HPC Workshop, 29‑30 November, 2023, https://hpcfusion.bsc.es/.

Summer 2023. Advances in Disruption Prevention via Machine Learning: challenges and opportunities. Invited Colloquium: 2023 IPP Symposium on ”New trends in experimental fusion plasma physics and plasma wall interaction”, 28 ‑ 29 June, Garching bei Munchen, Germany.

Summer 2023. A review of explainable Machine Learning accelerating Fusion science. Invited Talk: 2023 PhDiaFusion Sum-mer School, 19 ‑ 23 June, Niepołomice Royal Castle, Poland https://phdia2023.ifj.edu.pl/.

Fall 2022. Interpretable Machine Learning Accelerating Fusion Research. Invited Tutorial: 64th Annual Meeting of the APS Division of Plasma Physics, Spokane, Washington.

Media publications, webinars, and recent news can be consulted at: https://disruptions.mit.edu/news/

New prediction model could improve the reliability of fusion power plants (MIT News)

PSFC researcher successfully reprise CPS-FR summer school (PSFC News)
Taming fusion with machine learning (MIT News)
Three Questions: Robert Granetz on fusion research (MIT News)