Ipek Atik | Health | Research Excellence Award

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Assoc. Prof. Dr. Ipek Atik | Health | Research Excellence Award

Assoc. Prof. Dr. Ipek Atik | Health | Gaziantep Islam Science and Technology University | Turkey

Health plays a central role in the scientific contributions of Assoc. Prof. Dr. Ipek Atik, whose career spans advanced research in deep learning, machine learning, renewable energy forecasting, medical image analysis, and computational modeling. Assoc. Prof. Dr. Ipek Atik has built a strong academic foundation through her formal education, progressing from engineering studies into specialized research areas involving artificial intelligence applications to health systems, environmental sustainability, and complex data-driven prediction models. Her professional experience includes serving as an academic and researcher at Gaziantep Islam Science and Technology University, where she has contributed extensively to AI-driven classification systems, forecasting methodologies, radiation shielding research, and medical imaging solutions, particularly in pneumonia detection and COVID-19 case prediction—fields where health, technology, and engineering effectively converge. Throughout her career, Assoc. Prof. Dr. Ipek Atik has cultivated broad research interests spanning convolutional neural networks, short-term energy forecasting, satellite image classification, landform analysis, LED technologies, renewable energy systems, and advanced material characterization, demonstrating an interdisciplinary approach that strengthens the health and engineering research ecosystem. Her demonstrated research skills include algorithm development, regression learning, deep learning model optimization, transfer learning, optical systems analysis, neural network-based forecasting, materials computation, and the integration of AI with medical and environmental datasets. These skills are strongly supported by her impactful publication record, which has earned awards and honors through high citation counts, international collaborations, and recognition within journals focused on engineering, energy, radiation sciences, and computational technologies. With over 220 citations, an h-index of 8, and an i10-index of 6, Assoc. Prof. Dr. Ipek Atik has established a meaningful global research presence. Her work on CNN-based classification systems, drone detection enhancement, short-term energy load forecasting, and deep learning–supported medical analysis highlights the significant influence of her studies on public health, technological advancement, and industrial applications. The sustained academic output of Assoc. Prof. Dr. Ipek Atik, combined with her dedication to interdisciplinary innovation, positions her as a leading contributor to modern AI-enabled solutions. In conclusion, Assoc. Prof. Dr. Ipek Atik represents a dynamic and forward-focused researcher whose work consistently bridges health, engineering, and artificial intelligence, contributing valuable insights and technologies that support societal progress, scientific advancement, and long-term sustainable development.

Academic Profile: ORCID | Scopus | Google Scholar

Featured Publications:

Atik, I. (2022). Classification of electronic components based on convolutional neural network architecture. 39 citations.
Atik, I. (2022). A new CNN-based method for short-term forecasting of electrical energy consumption in the COVID-19 period: The case of Turkey. 32 citations.
Dinçer, F., Atik, İ., Yılmaz, Ş., & Çıngı, A. (2017). Hidrolik enerjisinden yararlanmada ülkemiz ve gelişmiş ülkelerin mevcut durumlarının analizi. 26 citations.
Atik, I. (2023). CB-YOLOv5: Enhancing drone detection with BottleneckCSP and cross convolution for improved performance. 16 citations.
Tuncel, N., Akkurt, I., Atik, I., Malidarre, R. B., & Sayyed, M. I. (2024). Neutron-gamma shielding properties of chalcogenide glasses. 11 citations.
Atik, I. (2022). Performance comparison of regression learning methods: COVID-19 case prediction for Turkey. 10 citations.

 

 

Annalisa Barsotti | Telemedicine | Best Researcher Award

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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.