Angelos Athanasiadis | Machine Learning and AI Applications | Research Excellence Award

Mr. Angelos Athanasiadis | Machine Learning and AI Applications | Research Excellence Award

Aristotle University of Thessaloniki (AUTH) | Greece

Mr. Angelos Athanasiadis is a Ph.D. candidate in Electrical and Computer Engineering at Aristotle University of Thessaloniki, specializing in FPGA-based acceleration of Convolutional Neural Networks (CNNs) and heterogeneous computing systems. He holds an M.Eng. in Electronics and Computer Systems and an MBA with high distinction. His research focuses on energy-efficient FPGA architectures, full-precision CNN inference on reconfigurable hardware, drone-assisted monitoring systems, distributed embedded system emulation, and timing-accurate simulation of cyber-physical systems. Angelos has contributed to EU-funded research projects such as ADVISER and REDESIGN, and has gained industrial experience in embedded system development and R&D at Cadence Design Systems, EXAPSYS, and SEEMS PC. He developed a parameterizable high-level synthesis matrix multiplication library for AMD FPGAs and designed FUSION, an open-source framework integrating QEMU with OMNeT++ via HLA/CERTI for deterministic, sub-microsecond synchronized, multi-node emulation of heterogeneous computing systems. His work supports realistic prototyping of systems combining CPUs, GPUs, and FPGAs in accuracy-critical domains such as aerial monitoring and autonomous embedded platforms. With a strong foundation in both academic research and industrial applications, Angelos advances the field of FPGA-based acceleration and distributed embedded computing, bridging innovation with practical deployment.

Profile : Google Scholar

Featured Publications

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2025). An efficient open-source design and implementation framework for non-quantized CNNs on FPGAs. Integration, 102625.

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2025). Energy-efficient FPGA framework for non-quantized convolutional neural networks. arXiv preprint arXiv:2510.13362.

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2024). An open-source HLS fully parameterizable matrix multiplication library for AMD FPGAs. WiPiEC Journal – Works in Progress in Embedded Computing Journal, 10(2).

Katselas, L., Jiao, H., Athanasiadis, A., Papameletis, C., Hatzopoulos, A., … (2017). Embedded toggle generator to control the switching activity during test of digital 2D-SoCs and 3D-SICs. 2017 27th International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS).

Katselas, L., Athanasiadis, A., Hatzopoulos, A., Jiao, H., Papameletis, C., … (2017). Embedded toggle generator to control the switching activity. 2017. (Conference details same as above; possible short version or extended abstract.)

Marco Gaiotti | Shipbuilding | Best Researcher Award

Mr. Marco Gaiotti | Shipbuilding | Best Researcher Award

Università di Genova | Italy

Marco Gaiotti is an associate professor in ship structures and marine engineering whose work focuses on the structural behavior, fabrication effects, and ultimate strength of ships and offshore structures. Holding a Bachelor (2005), Master (2008), and PhD (2012) in Naval Architecture and Marine Engineering from the Università degli Studi di Genova, he has developed expertise in composite materials, fabrication-induced imperfections, and advanced simulation techniques. His academic career includes progressive research appointments leading to his current professorship, along with coordination roles for the Nautical Engineering and Yacht Design programs. Gaiotti has contributed internationally as a visiting researcher at NAOE–Osaka University and through long-standing service within the ISSC, where he has served as specialist member, award-winning expert, and Chairman of Committee III.1 on Ultimate Strength. He has led competitive research initiatives, notably the EU-funded LeaderSHIP project (2023–2027), promoting innovation, skills development, and collaboration in the maritime sector. His work also extends to technology transfer, including a patented method for validating robotic inspection technologies in naval environments. With 454 citations by 341 documents, 67 documents, and an h-index of 12, his research continues to advance materials, structural performance, and safety in marine engineering.

Profiles : Scopus | Orcid

Featured Publications

Gaiotti, M., Brubak, L., Chen, B.-Q., Darie, I., Georgiadis, D., Shiomitsu, D., Kõrgesaar, M., Lv, Y., Nahshon, K., Paredes, M., et al. (2026). “Evaluating numerical simulation accuracy for full-scale high-strength steel ship structures: Insights from the ISSC 2025 Ultimate Strength Committee benchmark on transversely stiffened panels” in Marine Structures.

Aguiari, M., Gaiotti, M., & Rizzo, C.M. (2022). “Ship weight reduction by parametric design of hull scantling” in Ocean Engineering.

Aguiari, M., Gaiotti, M., & Rizzo, C.M. (2022). “A design approach to reduce hull weight of naval ships” in Ship Technology Research.

Poggi, L., Gaggero, T., Gaiotti, M., Ravina, E., & Rizzo, C.M. (2022). “Robotic inspection of ships: inherent challenges and assessment of their effectiveness” in Ships and Offshore Structures.

Ringsberg, J.W., Darie, I., Nahshon, K., Shilling, G., Vaz, M.A., Benson, S., Brubak, L., Feng, G., Fujikubo, M., Gaiotti, M., et al. (2021). “The ISSC 2022 Committee III.1–Ultimate strength benchmark study on the ultimate limit state analysis of a stiffened plate structure subjected to uniaxial compressive loads” in Marine Structures.