Federica Goffi | Multimodal Imaging Techniques | Best Researcher Award

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Ms. Federica Goffi | Multimodal Imaging Techniques | Best Researcher Award

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:

  • 🏆 Progetto Rocca Award 2024 – Roberto Rocca doctoral fellowship (MIT-Italy Program)

  • 🥇 Best Conference Paper Award 2024 – IEEE MetroXRAINE, UK

  • 🌍 ECNP Excellence Award & Travel Grant 2023 – European College of Neuropsychopharmacology, Spain

  • 🎖️ 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

  • 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.

  • Journal/Conference: Proceedings of VIII Congress of the National Group of Bioengineering

  • Year: 2023

Multi-Scale Assessment of Harmonization Efficacy on Resting-State Functional Connectivity

  • Authors: Tassi, E., Goffi, F., Bianchi, A., Maggioni, E., Rossetti, M., Brambilla, P., Bellani, M., Vai, B., Calesella, F., & Benedetti, F.

  • Journal/Book Chapter: Springer Lecture Notes in Computer Science

  • Year: 2024

Multivariate assessment of autonomic regulation in major depressive disorder: The role of environment and symptomatology

  • Authors: Goffi, F., Ferro, A., Pescuma, V. R., Enrico, P., Schiena, G., Barone, Y., Triulzi, F. M., Bianchi, A., Maggioni, E., & Brambilla, P.

  • Journal: Neuroscience Applied, Volume 2, Article 103899

  • Year: 2023

Dynamic functional connectivity of the central autonomic network in major depressive disorder

  • 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.

  • Journal: Neuroscience Applied, Volume 2, Article 103698

  • Year: 2023

Exploring psychiatric conditions through machine learning: A comprehensive analysis of large-scale data from the ECNP data network

  • 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.

  • Journal: Neuroscience Applied, Volume 2, Article 102621

  • Year: 2023

Saeed Amal | Multimodal Imaging Techniques | Best Researcher Award

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Prof. Saeed Amal | Multimodal Imaging Techniques | Best Researcher Award

Professor at Northeastern University, United States

Saeed Amal  is an accomplished researcher in Artificial Intelligence (AI) with a strong focus on healthcare and precision medicine. Currently serving as an Assistant Research Professor at Northeastern University, his work integrates cutting-edge AI methodologies such as deep learning, machine learning, and generative AI to address complex challenges in healthcare. With extensive experience spanning academia and industry, Saeed’s contributions are shaping the future of personalized medicine and intelligent healthcare solutions.

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Education🎓

Saeed’s academic journey reflects his dedication to innovation and interdisciplinary learning. He earned his Ph.D. in Computer Science from Haifa University , where he conducted pioneering research on machine learning, information retrieval, and recommender systems. During his postdoctoral fellowships at Stanford University and Haifa University, he expanded his expertise in deep learning, natural language processing (NLP), and healthcare applications of AI. His educational background also includes a Master’s degree in Computer Science and a Bachelor’s degree in the same field from Technion – Israel Institute of Technology, graduating with honors.

Professional Experience💼

Saeed’s professional journey is marked by impactful roles across academia and industry . At Northeastern University, he leads research on precision medicine, leveraging AI to detect and prevent diseases. His industry tenure includes roles as a Data Scientist and Researcher at Cognitech Smart AI, Dynamic Yield, and General Motors, where he developed AI-driven solutions for healthcare, recommender systems, and NLP. Saeed also has experience as a software engineer at Attunity and AMDOCS, designing large-scale systems with a focus on quality and performance. These experiences underscore his ability to translate theoretical AI concepts into practical, scalable applications.

Research Interests🏥

Saeed’s research interests lie at the intersection of AI and healthcare . His work focuses on the use of AI for early disease detection, multi-modal data integration, and improving diagnostic accuracy. He specializes in deep learning, NLP, large language models (LLM), and image processing to create advanced systems for precision medicine. His vision is to revolutionize healthcare through technology, making diagnosis and treatment more efficient and accessible. Saeed also has significant interest in explainable AI and its application to improve the interpretability and trustworthiness of AI systems.

Awards and Recognitions🏆

Saeed has received several accolades for his contributions to AI and healthcare . He has been a keynote and plenary speaker at major international conferences, including the “3rd International Conference on AI, ML, Data Science, and Robotics” in Rome (2023) and the “Public Health and Healthcare Management Conference” in Dubai (2023). He also chairs special sessions, such as the “Artificial Intelligence and Multimodal Data for Improving Disease Care” at the International Conference on Medical and Health Sciences. These roles highlight his leadership and impact in the global AI community.

Publications📚

“Analysis and Visualization of Confounders and Treatment Pathways Leading to Amputation and Non-Amputation in Peripheral Artery Disease Patients Using Sankey Diagrams”

  • Year: 2025-01-21
  • DOI: 10.3390/biomedicines13020258

“Evaluating Neural Network Performance in Predicting Disease Status and Tissue Source of JC Polyomavirus from Patient Isolates Based on the Hypervariable Region of the Viral Genome”

  • Year: 2024-12-25
  • DOI: 10.3390/v17010012

“Artificial Intelligence and Digital Pathology for Histologic Growth Pattern Classification in Lung Adenocarcinoma”

  • Year: 2024-11-25 (Preprint)
  • DOI: 10.20944/preprints202411.1804.v1

“Digital Pathology and Ensemble Deep Learning for Kidney Cancer Diagnosis: Dartmouth Kidney Cancer Histology Dataset”

  • Year: 2024-11-21 (Preprint)
  • DOI: 10.20944/preprints202411.1615.v1

“Gastric Cancer Detection with Ensemble Learning on Digital Pathology: Use Case of Gastric Cancer on GasHisSDB Dataset”

  • Year: 2024-08-12
  • DOI: 10.3390/diagnostics14161746

“Segmenting Tumor Gleason Pattern Using Generative AI and Digital Pathology: Use Case of Prostate Cancer on MICCAI Dataset”

  • Year: 2024-06-28 (Preprint)
  • DOI: 10.20944/preprints202406.2019.v1

“Multi-Scale Digital Pathology Patch-Level Prostate Cancer Grading Using Deep Learning: Use Case Evaluation of DiagSet Dataset”

  • Year: 2024-06-18
  • DOI: 10.3390/bioengineering11060624

“Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology”

  • Year: 2024-06-14
  • DOI: 10.3390/cancers16122222

“Enhancing Prostate Cancer Diagnosis with a Novel Artificial Intelligence-Based Web Application”

  • Year: 2023-11-30
  • DOI: 10.3390/cancers15235659

Conclusion🌟

Dr. Saeed Amal is a highly suitable candidate for the Research for Best Researcher Award. With a strong foundation in AI for healthcare, an impressive publication record, and a clear demonstration of leadership in the scientific community, he embodies the qualities of an outstanding researcher. Enhancing public engagement and focusing on broader societal impacts can further bolster his profile for such recognitions.