Assist. Prof. Dr. Annalisa Barsotti | Telemedicine | Best Researcher Award

Assist. Prof. Dr. Annalisa Barsotti | Telemedicine | Researcher Assistant at Scuola Superiore Sant’Anna | Italy

Assist. Prof. Dr Annalisa Barsotti is an accomplished academic and researcher in the fields of Artificial Intelligence, Machine Learning, Causal Inference, and Data Management. With her interdisciplinary expertise, she has advanced computational methods that link human neural and muscular activities with adaptive technologies. Her scholarly work bridges neuroscience, data analytics, and intelligent systems, establishing her as a rising leader in digital innovation and biomedical informatics. She is widely recognized for her contributions to signal processing, causal discovery frameworks, and cross-domain transfer learning that address real-world clinical and industrial challenges.

Academic Profile:

ORCID

Scopus

Google Scholar

Education:

Dr Barsotti has pursued rigorous academic training in computer science and applied machine learning, culminating in a doctoral degree focused on predictive analytics and causal modeling. Her academic path has been strengthened by advanced studies in leading European institutions, where she developed specialization in EEG/EMG signal synchronization and human-computer interaction systems. Her education provided the foundation for integrating computational intelligence with neuroscience, a theme that continues to define her research trajectory.

Experience:

Currently serving as Assistant Professor at Scuola Superiore Sant’Anna, Dr Barsotti has contributed to cutting-edge projects involving clinical brain-body imaging, data-driven health monitoring, and digital transformation in telecommunication domains. Her collaborative engagements span multidisciplinary teams, including biomedical engineers, neuroscientists, and computer scientists, with whom she has delivered impactful research outcomes. She has also presented her work in major international conferences under IEEE and other reputed platforms, highlighting her global presence in the research community. Alongside academic contributions, she has been actively involved in mentoring graduate students, guiding them in experimental design, data analysis, and the responsible application of AI technologies.

Research Interest:

Her research interests encompass artificial intelligence applications in health, causal inference for complex systems, machine learning algorithms for predictive modeling, and transfer learning under partial information frameworks. She has worked extensively on integrating EEG and EMG signals to enhance neuroadaptive technologies, applied machine learning techniques for clinical diagnostics, and conducted domain-specific studies in telecommunications churn prediction. By blending causal discovery with data management, her research addresses both the theoretical depth and applied value of AI systems in contemporary digital transformation.

Awards:

Dr Barsotti’s contributions have been recognized through invitations to contribute in international conferences and collaborations across leading research institutions. Her work has drawn significant academic attention, with her highly cited papers being used by peers in neuroscience, biomedical engineering, and AI. Her inclusion in Scopus and Google Scholar metrics demonstrates the global recognition of her impact, with a growing citation record and a steadily increasing h-index.

Selected Publications:

• Effective synchronization of EEG and EMG for mobile brain/body imaging in clinical settings (2018) – 43 citations

• Muscle fatigue evaluation with EMG and acceleration data: a case study (2020) – 13 citations

• A decade of churn prediction techniques in the telco domain: a survey (2024) – 6 citations

• Heterogeneous transfer learning from a partial information decomposition perspective (2023) – 1 citation

Conclusion:

Assist. Prof. Dr Annalisa Barsotti exemplifies the qualities of a scholar dedicated to both research excellence and societal progress. Her impactful publications, growing citation metrics, and collaborative engagements demonstrate her capability to shape the future of AI, machine learning, and biomedical data analytics. By contributing to both applied and theoretical domains, she has built a foundation that promises sustained leadership in academic and professional communities. With her demonstrated commitment to innovation and global collaboration, she is a deserving nominee for research awards and recognition platforms, representing the potential to advance the next generation of AI-driven solutions for society.