Prof. Dr. Abdelkrim Kamel Oudjida | Real-Time Robotic Control | Research Excellence Award

Centre de Développement des Technologies Avancées | Algeria

Prof. Dr. Abdelkrim Kamel Oudjida is a distinguished director of research at the Centre De Développement des Technologies Avancées, recognized for his extensive contributions to computer arithmetic and its applications in Cryptography, DSP, robotics, Real-Time Robotic Control and VLSI digital design. With a strong academic and professional background, he has authored 22 publications, delivered 35 communications, and contributed to 2 patents, establishing himself as a leading figure in advanced digital circuit design. his research achievements include developing advanced binary recoding methods and pioneering new arithmetic strategies for efficient asic/fpga implementation of linear systems. His work has earned him 328 citations, based on 263 citing documents, supported by 34 documents, and an h-index of 8, reflecting his growing scholarly impact. in addition to his role as a full-time researcher, he serves as a lecturer in computer arithmetic and cryptography at the national higher school of mathematics. He has supervised more than 50 engineering, master’s, and phd projects, further strengthening the scientific community through mentorship. As a reviewer for prestigious journals such as ieee tcas, elsevier microelectronics, and jolpe, and collaborating with several european research labs, oudjida continues to advance the field through innovation, leadership, and international engagement.

Profiles: Scopus | Orcid

Featured Publications

Liacha, A., Oudjida, A. K., & Bellal, R. N. (2025). On the complexity of constant multiplication problem. International Journal of Circuit Theory and Applications. Advance online publication.

Nait-Abdesselam, F., Oudjida, A. K., Khouas, A., & Liacha, A. (2025). On computing the double point multiplication in elliptic curve cryptography. Cryptologia. Advance online publication.

Echikr, A., Yachir, A., Kerrache, C. A., Oudjida, A. K., & Sahraoui, Z. (2024). Interoperable IoRT for healthcare: Securing intelligent systems with decentralized blockchain. Acta Informatica Pragensia.

Oudjida, A. K., & Liacha, A. (2021). Radix-2ʷ arithmetic for scalar multiplication in elliptic curve cryptography. IEEE Transactions on Circuits and Systems I: Regular Papers, 68(5), 1984–1997.

Oudjida, A. K. (n.d.). Multiple constant multiplication algorithm for high-speed and low-power design.

Ms. Tamanna | Neural Networks for Robot Control | Best Researcher Award

Goethe University, Frankfurt | Germany

Tamanna is a Ph.d. researcher at Goethe University, Frankfurt, Germany, specializing in geochemistry with a focus on integrating data science and machine learning into geo-scientific research. Her work aims to bridge the gap between traditional geoscience and modern computational methodologies by leveraging data-driven approaches to analyze complex geochemical systems. She holds a bs-ms degree in earth and environmental sciences from the Indian institute of science education and research, Bhopal. her expertise spans elemental and isotopic geochemistry, geospatial analysis, statistical modeling, and predictive analytics for geochemical processes. She is proficient in programming and data visualization using python, Neural Networks for Robot Control, matlab, qgis, and arcgis. Tamanna has authored two research documents with approximately one citation and an h-index of 1 for those publications; overall, her author-level record includes 99 citations and an h-index of 6, according to google scholar. Beyond her technical skills, she is actively involved in interdisciplinary research that combines quantitative methods, laboratory work, and field investigations to enhance the understanding of earth’s chemical evolution. fluent in english and hindi, with working knowledge of german, she represents the next generation of data-driven geoscientists.

Profile: Orcid

Featured Publications

Tamanna, Hezel, D. C., & Marschall, H. R. (2025, October). MRMinerals and MineralTD: Machine‐Readable Mineral Formula and Compositions Data Set for Data‐Driven Research. Geoscience Data Journal.

Tamanna, Hezel, D. C., Srivastava, N., & Faber, J. (2025, August 13). Using machine learning for automatic rock classification. American Mineralogist.