Basant Farag | Computational Neuroscience | Best Innovation Award

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Dr. Basant Farag | Computational Neuroscience | Best Innovation Award

Dr. Basant Farag | Zagazig University Faculty of Science | Egypt

Basant Farag is a dedicated Egyptian organic chemist whose work focuses on the synthesis and biological evaluation of diverse heterocyclic ring systems using modern and versatile synthetic routes. As a Lecturer of Organic Chemistry, she specializes in designing and preparing organic molecules with significant industrial and pharmacological potential, while also integrating computational chemistry approaches to enhance structural prediction and activity evaluation. Her academic pathway includes substantial experience as both a Teaching Assistant and Assistant Lecturer, where she contributed to lecture preparation, student instruction, exam evaluation, and the development of research-led teaching environments. Basant has established a strong publication record, reflected in 409 citations, an h-index of 13, and an i10-index of 18, demonstrating her growing impact in the fields of organic synthesis and medicinal chemistry. She has served extensively as an international reviewer for numerous reputable journals, covering areas such as bioorganic chemistry, molecular structure, drug design, biological macromolecules, oncology research, and chemical sciences, highlighting her expertise and recognition in the global scientific community. Her research contributions span synthetic organic chemistry, biological screening, and mechanistic analysis, with many of her works addressing the development of bioactive compounds with promising therapeutic importance.

Profile: Google Scholar

Featured Publications

Abolibda, T. Z., Fathalla, M., Farag, B., Zaki, M. E. A., & Gomha, S. M. (2023). Synthesis and molecular docking of some novel 3-thiazolyl-coumarins as inhibitors of VEGFR-2 kinase. Molecules, 28(2), 689.

Ibrahim, M. S., Farag, B., Al-Humaidi, J. Y., Zaki, M. E. A., Fathalla, M., & Gomha, S. M. (2023). Mechanochemical synthesis and molecular docking studies of new azines bearing indole as anticancer agents. Molecules, 28(9), 3869.

Gomha, S. M., Riyadh, S. M., Alharbi, R. A. K., Zaki, M. E. A., Abolibda, T. Z., & Farag, B. (2023). Green route synthesis and molecular docking of azines using cellulose sulfuric acid under microwave irradiation. Crystals, 13(2), 260.

Hussein, A. M., Gomha, S. M., El-Ghany, N. A. A., Zaki, M. E. A., Farag, B., … (2024). Green biocatalyst for ultrasound-assisted thiazole derivatives: Synthesis, antibacterial evaluation, and docking analysis. ACS Omega, 9(12), 13666–13679.

Al-Humaidi, J. Y., Gomha, S. M., El-Ghany, N. A. A., Farag, B., Zaki, M. E. A., … (2023). Green synthesis and molecular docking study of some new thiazoles using terephthalohydrazide chitosan hydrogel as eco-friendly biopolymeric catalyst. Catalysts, 13(9), 1311.

Mokbel, W. A., Hosny, M. A., Gomha, S. M., Zaki, M. E. A., Farag, B., El Farargy, A. F., … (2024). Synthesis, molecular docking study, and biological evaluation of new thiadiazole and thiazole derivatives incorporating isoindoline-1,3-dione moiety as anticancer and …. Results in Chemistry, 7, 101375.

Gomha, S. M., Abolibda, T. Z., Alruwaili, A. H., Farag, B., Boraie, W. E., … (2024). Efficient green synthesis of hydrazide derivatives using L-proline: Structural characterization, anticancer activity, and molecular docking studies. Catalysts, 14(8), 489.

Gomha, S. M., El-Sayed, A. A. A. A., Alrehaily, A., Elbadawy, H. M., Farag, B., … (2024). Synthesis, molecular docking, in silico study, and evaluation of bis-thiazole-based curcumin derivatives as potential antimicrobial agents. Results in Chemistry, 7, 101504.

Abolibda, T. Z., El-Sayed, A. A. A. A., Farag, B., Zaki, M. E. A., Alrehaily, A., … (2025). Novel thiazolyl-pyrimidine derivatives as potential anticancer agents: Synthesis, biological evaluation, and molecular docking studies. Results in Chemistry, 13, 102008.

Alzahrani, A. Y. A., Gomha, S. M., Zaki, M. E. A., Farag, B., Abdelgawad, F. E., … (2024). Chitosan–sulfonic acid-catalyzed green synthesis of naphthalene-based azines as potential anticancer agents. Future Medicinal Chemistry, 16(7), 647–663.

Fabiano Papaiz | Computational Neuroscience | Best Researcher Award

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Prof. Fabiano Papaiz | Computational Neuroscience | Best Researcher Award

Prof. Fabiano Papaiz, IFRN, Brazil.

Fabiano Papaiz is a dedicated academic and professional in the field of education and technology, affiliated with the Federal Institute of Education, Science, and Technology of Rio Grande do Norte (IFRN) in Brazil. With a strong foundation in the intersection of education and technology, his work focuses on integrating modern technological innovations into educational practices. Through his research and professional endeavors, Papaiz has contributed significantly to advancing educational methods and improving learning environments. His accolades reflect his influence both within Brazil and internationally. His research aims to enhance educational outcomes by leveraging digital tools and resources, benefiting the academic community and shaping the future of learning.

Profile

Orcid

 

📚 Early Academic Pursuits

Fabiano Papaiz began his academic journey in Brazil, where he cultivated a strong foundation in the field of education and technology. His early academic pursuits were centered around exploring the intersections of education and technological advancements. With a keen interest in applied sciences, he honed his knowledge and skills through his academic experiences, leading him to a path of lifelong learning and research. Papaiz’s commitment to education in Brazil is evident, and his passion for technology-driven academic progress is one of the key pillars of his professional career.

💻 Professional Endeavors

Currently affiliated with the Federal Institute of Education, Science, and Technology of Rio Grande do Norte (IFRN), Fabiano Papaiz plays a pivotal role in shaping the future of education and technology. At IFRN, he is part of the DATINF (Department of Information Technology), where he works on integrating modern technological solutions with academic practices. His professional journey reflects his dedication to advancing educational methodologies and bridging the gap between technology and learning. As part of the institution, Papaiz has contributed to a wide range of educational initiatives that aim to enhance the learning experience in Brazil, especially in the field of information technology.

🔬 Contributions and Research Focus

Papaiz’s research interests lie in the dynamic field of information technology and its application to educational contexts. His research focuses on leveraging technological innovations to improve educational outcomes, develop new learning tools, and address contemporary challenges in the digital age. Fabiano’s academic contributions have been significant, with a strong emphasis on the role of technology in shaping modern education. His work not only influences the academic community but also helps to create a more tech-savvy generation of students who can navigate and thrive in a rapidly evolving digital world.

🏆 Accolades and Recognition

Throughout his career, Fabiano Papaiz has received numerous accolades for his contributions to education and technology. His work at IFRN has been recognized not only within Brazil but also internationally, as he continues to share his expertise with global academic and technological communities. His dedication to advancing the integration of information technology into education has earned him admiration from peers, students, and academic institutions alike. His reputation as a thought leader in the intersection of education and technology is well-established, marking him as an influential figure in his field.

🌍 Impact and Influence

Fabiano Papaiz’s work has made a profound impact on both the academic and technological landscapes of Brazil. His influence extends beyond the classroom, as his research and professional endeavors have shaped the way information technology is applied in education. Through his leadership and innovation, he has fostered the growth of more effective learning environments, enhanced by the use of digital tools and resources. His contributions have not only benefited his institution but also contributed to the wider educational community by offering solutions that address modern teaching and learning needs.

Publication

  • Title: Predicting ALS progression using Autoregressive deep learning models
    Authors: Fabiano Papaiz, Mario Emílio Teixeira Dourado, Jr, Ricardo Alexsandro de Medeiros Valentim, Felipe Ricardo dos Santos Fernandes, João Paulo Queiroz dos Santos, Antonio Higor Freire de Morais, Fernanda Brito Correia, Joel Perdiz Arrais
    Year: 2025

 

  • Title: RR3D: Uma solução para renderização remota de imagens médicas tridimensionais
    Author: Fabiano Papaiz
    Year: 2013

 

Conclusion

Fabiano Papaiz’s career exemplifies the transformative power of technology in education. His contributions, ranging from research to institutional leadership, have made a lasting impact on the integration of technology in educational settings. As he continues to innovate and lead, Papaiz’s legacy will undoubtedly shape the future of education, paving the way for more inclusive and effective learning environments. His ongoing work ensures that technology will remain a key driver in educational progress, with the potential to benefit generations of students and educators worldwide.

 

Francisco Mena | Computational Neuroscience | Best Researcher Award

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Mr. Francisco Mena | Computational Neuroscience | Best Researcher Award

Mr. Francisco Mena, University of Kaiserslautern-Landau, Germany.

Francisco Mena is a dynamic researcher in the field of machine learning, currently pursuing a PhD at the University of Kaiserslautern-Landau (RPTU), Germany. His academic roots trace back to Federico Santa María Technical University (UTFSM) in Chile, where he developed a strong foundation in computer engineering and data science. With a specialization in unsupervised learning, deep learning, and multi-view data fusion, his work focuses on building robust and scalable models that minimize human intervention and adapt to incomplete or noisy datasets—particularly in the context of Earth observation and crowdsourced data. He has worked across international research institutes like DFKI in Germany and Inria in France, contributing to global advancements in AI and data science. His teaching and mentoring roles, combined with his innovative research, mark him as a rising contributor to the future of intelligent systems.

Profile

Google Scholar
Scopus
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🎓 Early Academic Pursuits

Francisco Mena’s academic journey began with a strong foundation in computer engineering at Federico Santa María Technical University (UTFSM) in Chile. Demonstrating exceptional academic performance, he ranked in the top 10% of his class, securing the 4th position among 66 students. He pursued an integrated path that led him to obtain a Bachelor of Science, a Licenciado, and later the Ingeniería Civil en Informática degree. Driven by curiosity and a passion for machine learning, he transitioned seamlessly into postgraduate studies, earning a Magíster en Ciencias de la Ingeniería Informática at UTFSM. His master’s thesis, focused on mixture models in crowdsourcing scenarios, set the stage for his growing interest in unsupervised learning and probabilistic models.

💼 Professional Endeavors

Alongside his studies, Francisco actively engaged in diverse professional roles that enriched his technical and academic expertise. He served as a research assistant at the Chilean Virtual Observatory (CHIVO), contributing to astroinformatics projects by processing and organizing astronomical datasets from ALMA and ESO observatories. His early professional stint as a front-end and back-end developer provided him with hands-on industry experience. In academia, he held several teaching roles, progressing from laboratory assistant to lecturer in key courses such as computational statistics, artificial neural networks, and machine learning. Currently, as a Student Research Assistant at the German Research Centre for Artificial Intelligence (DFKI), he contributes to Earth observation projects, enhancing models for crop yield prediction using multi-view data.

🔬 Contributions and Research Focus

Francisco’s research is anchored in machine learning with a special emphasis on unsupervised learning, deep neural architectures, multi-view learning, and data fusion. His doctoral work at University of Kaiserslautern-Landau (RPTU) focuses on handling missing views in Earth observation data, an increasingly important issue in remote sensing. He explores innovative methods that challenge traditional domain-specific models by advocating for approaches that minimize human intervention and labeling. His core research areas include autoencoders, deep clustering, dimensionality reduction, and latent variable modeling, with applications extending to vegetation monitoring, neural information retrieval, and crowdsourcing.

🌍 Global Collaborations

Francisco’s commitment to impactful research is evident in his international collaborations. In addition to his work in Germany, he undertook a research visit to Inria in Montpellier, France, where he explored cutting-edge topics such as multi-modal co-learning, multi-task learning, and mutual distillation. These collaborations allow him to expand the practical relevance of his research across geographical and disciplinary boundaries, contributing to global discussions in artificial intelligence and data science.

🧠 Impact and Influence

Through his extensive academic involvement, Francisco has shaped the understanding of machine learning models that are both scalable and adaptable to real-world challenges. His contributions in crowdsourcing, particularly the use of latent group variable models for large-scale annotations, reflect his commitment to developing resource-efficient models. His influence extends into education, where he has mentored students and shaped curriculum delivery in machine learning-related subjects. By leveraging tools like PyTorch, QGIS, and Slurm, he ensures his work remains at the cutting edge of technological advancement.

🏆 Recognition and Growth

Francisco’s academic excellence is evident from his consistent achievements and recognition. His GPA of 94% during his master’s program stands as a testament to his dedication and intellect. Being ranked #4 in his undergraduate program highlights his sustained academic brilliance. His teaching roles at UTFSM and lecturing at RPTU further underscore the trust institutions place in his knowledge and teaching abilities.

🚀 Legacy and Future Contributions

With a clear research vision and a strong international presence, Francisco Mena is poised to leave a lasting impact in the field of artificial intelligence, particularly in unsupervised learning and Earth observation. His focus on reducing dependency on human intervention, increasing model generalizability, and handling incomplete or noisy data reflects a future-forward approach. As his doctoral journey progresses, he is expected to continue influencing how machine learning models are conceptualized, designed, and deployed in real-world applications—especially those that require scalable, domain-agnostic solutions.

Publication

 

  • Harnessing the power of CNNs for unevenly-sampled light-curves using Markov Transition Field – M Bugueño, G Molina, F Mena, P Olivares, M Araya – 2021

 

  • Common practices and taxonomy in deep multiview fusion for remote sensing applications – F Mena, D Arenas, M Nuske, A Dengel – 2024

 

  • A binary variational autoencoder for hashing – F Mena, R Ñanculef – 2019

 

  • Refining exoplanet detection using supervised learning and feature engineering – M Bugueño, F Mena, M Araya – 2018

 

  • Predicting crop yield with machine learning: An extensive analysis of input modalities and models on a field and sub-field level – D Pathak, M Miranda, F Mena, C Sanchez, P Helber, B Bischke, … – 2023

 

  • Adaptive fusion of multi-modal remote sensing data for optimal sub-field crop yield prediction – F Mena, D Pathak, H Najjar, C Sanchez, P Helber, B Bischke, P Habelitz, … – 2025

 

  • A comparative assessment of multi-view fusion learning for crop classification – F Mena, D Arenas, M Nuske, A Dengel – 2023

 

  • Self-supervised Bernoulli autoencoders for semi-supervised hashing – R Ñanculef, F Mena, A Macaluso, S Lodi, C Sartori – 2021

 

  • Impact assessment of missing data in model predictions for Earth observation applications – F Mena, D Arenas, M Charfuelan, M Nuske, A Dengel – 2024

 

  • Increasing the robustness of model predictions to missing sensors in Earth observation – F Mena, D Arenas, A Dengel – 2024

 

🧩 Conclusion

Driven by curiosity and innovation, Francisco Mena is reshaping the landscape of machine learning through his pursuit of generalizable, efficient, and human-independent models. His research not only addresses technical limitations but also responds to the growing need for AI systems that are adaptable across domains and disciplines. With a solid academic background, global collaborations, and a clear research vision, he is set to make lasting contributions to unsupervised learning and its applications in critical areas like Earth observation and neural information retrieval. As he continues to build on his expertise, his work promises to influence both the academic world and the practical deployment of intelligent systems in complex, real-world scenarios.