Ms. Federica Goffi, DEIB, Politecnico di Milano, Italy
Federica Goffi is an Italian biomedical engineer and neuroscience researcher, currently pursuing her PhD in Bioengineering at Politecnico di Milano in collaboration with the Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico. Her work is internationally recognized and she is currently a visiting PhD researcher at both Harvard Medical School (Spaulding Rehabilitation Hospital) and the Massachusetts Institute of Technology (MIT), where she is conducting innovative investigations at the intersection of neuroimaging, physiology, and bioengineering.
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📚 Early Academic Pursuits
Federica’s academic excellence began with her Diploma di maturità scientifica, which she completed with a perfect score of 100/100 at Liceo Scientifico Sperimentale Leonardo in Brescia. She pursued her Bachelor’s (106/110) and then Master’s Degree (110 cum laude/110) in Biomedical Engineering at Politecnico di Milano, focusing on neuroengineering, biomedical signal processing, and machine learning. Her master thesis, exploring ECG-fMRI integration in late-onset depression, laid the foundation for her subsequent research into the brain-heart axis.
🧠 Professional Endeavors
Federica has built a strong career path in cutting-edge research environments. After a short-term research fellowship in 2022, she began her PhD at Politecnico di Milano. In 2024, she was selected for a highly competitive PhD visiting program at MIT and Harvard, where she is currently contributing to projects investigating the gut-brain-autonomic axis using multimodal signal integration techniques.
🔬 Contributions and Research Focus
Her primary research interests lie in the multimodal integration of neuroimaging (fMRI), electrophysiological (EEG/ECG), piezoelectric, and respiratory signals, to study the central autonomic network in both physiological and pathological conditions, including major depressive disorder and gut-brain axis dysfunctions. Her work incorporates advanced signal processing, machine learning, and dynamic network modeling, contributing to both methodological innovation and clinical understanding.
🌍 Impact and Influence
Federica’s research has been presented and published in prestigious international venues, including the IEEE EMBC, MetroXRAINE, and ECNP Congress. She has co-authored multiple peer-reviewed papers, some of which are part of large-scale collaborations across Europe, indicating her growing influence in the neuroscience and bioengineering communities.
📊 Academic Citations and Publications
Her work has been published in notable proceedings and journals, including Neuroscience Applied and IEEE Conferences, with topics spanning from HRV-fMRI-DTI frameworks to machine learning in psychiatry. She is frequently cited in her field for contributions on central autonomic connectivity, environmental effects on psychopathology, and functional network dynamics.
🧪 Research Skills
Federica is proficient in a broad spectrum of research tools and programming languages including MATLAB, Python, C, SQL, and bash scripting. She is skilled in using neuroimaging software such as SPM, FSL, EEGLAB, BrainVision Analyzer, and FreeSurfer. She has hands-on experience in multimodal EEG-ECG-fMRI data acquisition and integration, positioning her as a technically strong and innovative researcher.
👩🏫 Teaching & Mentoring Experience
In addition to her research, Federica has contributed to academic life as a math and physics tutor, and has experience in team coordination in research and educational contexts. She has also demonstrated leadership as a Board Member of MIT’s Visiting Student Association (VISTA) and as a dance instructor, highlighting her ability to teach, mentor, and lead in both scientific and artistic domains.
🏅 Awards and Honors
Her excellence has been recognized through multiple prestigious awards:
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🏆 Progetto Rocca Award 2024 – Roberto Rocca doctoral fellowship (MIT-Italy Program)
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🥇 Best Conference Paper Award 2024 – IEEE MetroXRAINE, UK
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🌍 ECNP Excellence Award & Travel Grant 2023 – European College of Neuropsychopharmacology, Spain
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🎖️ Poster Award Nomination 2023 – National Group of Bioengineering, Italy
These honors affirm the novelty, quality, and relevance of her scientific work on an international stage.
🌱 Legacy and Future Contributions
Federica Goffi is a rising star in the intersection of bioengineering, neuroscience, and data science. Her trajectory demonstrates a commitment to scientific advancement, global collaboration, and translational research that bridges technology and human health. Her future contributions are expected to shape precision neuropsychiatry, develop integrative diagnostic frameworks, and foster innovation in multimodal brain-body research.
Top Publications
Brain-heart interaction: An ECG-fMRI integrated study in physiology and major depressive disorder
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Authors: Goffi, F., Reali, P., Ferro, A., Pescuma, V., Schiena, G., Barone, Y., Enrico, P., Torrente, Y., Triulzi, F., Bianchi, A. M., Brambilla, P., & Maggioni, E.
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Journal/Conference: Proceedings of VIII Congress of the National Group of Bioengineering
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Year: 2023
Multi-Scale Assessment of Harmonization Efficacy on Resting-State Functional Connectivity
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Authors: Tassi, E., Goffi, F., Bianchi, A., Maggioni, E., Rossetti, M., Brambilla, P., Bellani, M., Vai, B., Calesella, F., & Benedetti, F.
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Journal/Book Chapter: Springer Lecture Notes in Computer Science
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Year: 2024
Multivariate assessment of autonomic regulation in major depressive disorder: The role of environment and symptomatology
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Authors: Goffi, F., Ferro, A., Pescuma, V. R., Enrico, P., Schiena, G., Barone, Y., Triulzi, F. M., Bianchi, A., Maggioni, E., & Brambilla, P.
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Journal: Neuroscience Applied, Volume 2, Article 103899
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Year: 2023
Dynamic functional connectivity of the central autonomic network in major depressive disorder
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Authors: Goffi, F., Reali, P., Ferro, A., Pescuma, V. R., Enrico, P., Schiena, G., Barone, Y., Triulzi, F. M., Bianchi, A., Brambilla, P., & Maggioni, E.
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Journal: Neuroscience Applied, Volume 2, Article 103698
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Year: 2023
Exploring psychiatric conditions through machine learning: A comprehensive analysis of large-scale data from the ECNP data network
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Authors: Khuntia, A., Buciuman, M.-O., Fanning, J., Herrera, A., Wiegand, A., Hahn, L., From, T., Goffi, F., Kaufmann, T., Laurikainen, L., Maggioni, E., Martinez, I., Stolicyn, A., Schwarz, E., Squarcina, L., Andreassen, O., Bellani, M., Brambilla, P., Hietala, J., & Koutsouleris, N.
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Journal: Neuroscience Applied, Volume 2, Article 102621
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Year: 2023