Mr. Petros Barmpas | Statistical Analysis | Best Researcher Award
Mr at University of The, Greece
Petros Barmpas is a dedicated researcher with a strong background in computer science and biomedical informatics. He has been advancing his academic journey at the University of Thessaly since 2020, following his graduation from the University of Patras with a degree in Computer Engineering and Informatics in 2019. His career is marked by an impressive series of publications and contributions to significant projects in the fields of machine learning, clustering methodologies, and the study of healthcare informatics. Petros is married and a proud father, balancing his professional and personal life with dedication and resilience. His contributions in AI and data analysis have cemented his reputation in both academic and practical applications of computational science.
Profile
Education 🎓
Petros Barmpas embarked on his academic career by completing high school in Kozani from 2010 to 2013. He then pursued his Bachelor’s degree in Computer Engineering and Informatics at the University of Patras, graduating in 2019. Currently, Petros is enrolled at the University of Thessaly, where he has been deepening his expertise in Computer Science and Biomedical Informatics since 2020. His educational path has equipped him with extensive knowledge and skills in computational algorithms, data analysis, and advanced informatics, supporting his impactful research and publications.
Experience 🧑🏫
Since the onset of his academic pursuits, Petros Barmpas has engaged in various research initiatives, focusing on the practical application of AI and machine learning. His participation in the ATHLOS project exemplifies his commitment to large-scale data analysis and unsupervised learning. Throughout his time at the University of Thessaly and during collaborations with peers, he has demonstrated exceptional expertise in hierarchical clustering, ensemble regressors, and hyperdimensional computing approaches. Petros’ professional journey showcases a consistent dedication to solving complex computational problems and advancing methodologies in biomedical informatics.
Awards and Honors 🏆
Petros Barmpas has earned recognition through numerous high-impact publications in prestigious conferences and journals. His co-authored works in IEEE Congresses and journals like the Springer Journal Health Information Science and Systems underscore his influential presence in the research community. The breadth of his collaborative work reflects the trust and respect he commands among fellow researchers, showcasing his commitment to advancing scientific understanding in AI applications and public health data analysis.
Research Focus 🔍
Petros Barmpas’ research primarily explores innovative approaches to machine learning and data clustering. His interests lie in hyperdimensional computing, hierarchical clustering ensemble methods, and the development of predictive models for healthcare studies. He has applied these techniques to diverse areas, including opioid management during the pandemic and socio-demographic analysis in large-scale studies. Through collaborations and independent work, Petros has contributed to refining algorithms that enhance prediction power and adaptability, positioning his work at the intersection of AI and public health informatics.
Conclusion📝
Petros Barmpas is a distinguished researcher with significant accomplishments in computer science and biomedical informatics. His extensive publication record, innovative contributions to data clustering and health informatics, and strong collaborative efforts underscore his suitability for the Best Researcher Award. Addressing areas such as publishing in higher-impact journals and increasing international engagement could further augment his candidacy. Overall, his academic achievements, commitment to interdisciplinary research, and consistent output make him a deserving nominee for recognition.
Publications 📚
- Conference Paper: Hyperdimensional Computing Approaches in Single Cell RNA Sequencing Classification
Year: 2024
Authors: Barmpas, P., Tasoulis, S.K., Georgakopoulos, S.V., Plagianakos, V.P.
Source: 2024 IEEE Congress on Evolutionary Computation, CEC 2024 – Proceedings
- Conference Paper: HCER: Hierarchical Clustering-Ensemble Regressor
Year: 2024
Authors: Barmpas, P., Anagnostou, P., Tasoulis, S.K., Georgakopoulos, S.V., Plagianakos, V.P.
Source: Communications in Computer and Information Science, 2141 CCIS, pp. 369–378
- Article (Open access): The Development and Validation of the Pandemic Medication-Assisted Treatment Questionnaire for the Assessment of Pandemic Crises Impact on Medication Management and Administration for Patients with Opioid Use Disorders
Year: 2023
Authors: Leventelis, C., Katsouli, A., Stavropoulos, V., Veskoukis, A.S., Tsironi, M.
Source: NAD Nordic Studies on Alcohol and Drugs, 40(1), pp. 76–94
- Conference Paper: Neural Networks Voting for Projection Based Ensemble Classifiers
Year: 2023
Authors: Anagnostou, P., Barmpas, P., Tasoulis, S.K., Georgakopoulos, S.V., Plagianakos, V.P.
Source: Proceedings of the 2023 IEEE International Conference on Big Data, BigData 2023, pp. 4567–4574
- Article (Open access): A Divisive Hierarchical Clustering Methodology for Enhancing the Ensemble Prediction Power in Large Scale Population Studies: The ATHLOS Project
Year: 2022
Authors: Barmpas, P., Tasoulis, S., Vrahatis, A.G., Plagianakos, V.P., Panagiotakos, D.
Source: Health Information Science and Systems, 10(1), 6