Boyuan Bai | Artificial Intelligence and Machine Learning | Best Researcher Award

Published on by New Scientists

Dr. Boyuan Bai | Artificial Intelligence and Machine Learning | Best Researcher Award

Doctor | Beijing University of Posts and Telecommunications | China

Dr. Boyuan Bai is an emerging researcher in advanced visual computing, with a focused contribution to 3D reconstruction, Gaussian Splatting, multi-view scene modeling, and uncertainty-aware machine learning. His work integrates computer graphics, deep learning, and computational geometry to develop intelligent systems capable of producing highly accurate and stable indoor scene reconstructions. With 83 citations, 4 Scopus-indexed publications, and an h-index of 3, he is rapidly establishing a strong research footprint. Dr. Boyuan Bai’s notable scientific contribution centers on UncertainGS, an uncertainty-aware indoor reconstruction framework published in Neurocomputing (SCI/Scopus). This research introduces a novel pipeline that integrates cross-modal uncertainty prediction to guide the optimization of Gaussian Splatting. His methodological innovation improves the fidelity of reconstructed surfaces, especially in textureless or geometrically ambiguous indoor regions. His incorporation of Manhattan-world constraints into the Gaussian Splatting process represents a significant leap forward in aligning 3D surface geometry with real-world structural patterns. His research areas broadly span multi-view 3D reconstruction, Gaussian Splatting, uncertainty modeling, scene understanding, and deep reinforcement learning for geometric perception. He actively contributes to the development of next-generation 3D vision technologies, with applications in robotics, digital twins, AR/VR environments, and autonomous spatial intelligence. His work shows strong potential for large-scale deployment in real-time virtual reconstruction and simulation systems. Dr. Boyuan Bai’s scholarly output includes peer-reviewed journal publications, research project leadership, and scientific contributions that address fundamental challenges in computational imaging. His research achievements demonstrate clear innovation, technical depth, and growing influence in the fields of computer vision and graphics. Through ongoing academic collaborations and continued focus on high-impact research problems, he is emerging as a promising researcher in intelligent 3D scene modeling and uncertainty-aware visual computing.

Profiles: Scopus | IEEE Xplore | ACM Digital Library 

Featured Publications

1. Bai, B., Qiao, X., Lu, P., Zhao, H., Shi, W., & others. (2025). Two grids are better than one: Hybrid indoor scene reconstruction framework with adaptive priors. Neurocomputing, 618(C). https://doi.org/10.1016/j.neucom.2024.129118

2. Huang, Y., Bai, B., Zhu, Y., Qiao, X., Su, X., Yang, L., & others. (2024). ISCom: Interest-aware semantic communication scheme for point cloud video streaming on Metaverse XR devices. IEEE Journal on Selected Areas in Communications, 42(4). https://doi.org/10.1109/JSAC.2023.3345430

3. Zhu, Y., Huang, Y., Qiao, X., Tan, Z., Bai, B., & others. (2023). A semantic-aware transmission with adaptive control scheme for volumetric video service. IEEE Transactions on Multimedia, 25. https://doi.org/10.1109/TMM.2022.3217928

4. Huang, Y., Zhu, Y., Qiao, X., Tan, Z., & Bai, B. (2021). AITransfer: Progressive AI-powered transmission for real-time point cloud video streaming. In Proceedings of the 29th ACM International Conference on Multimedia (MM ’21). https://doi.org/10.1145/3474085.3475624

Ibrahim Aromoye | Artificial Intelligence and Machine Learning | Editorial Board Member

Published on by New Scientists

Mr. Ibrahim Aromoye | Artificial Intelligence and Machine Learning | Editorial Board Member

Graduate Research Assistant | Universiti Teknologi PETRONAS | Malaysia

Mr. Ibrahim Aromoy is a promising researcher in Electrical and Electronic Engineering with a growing scholarly footprint in hybrid UAV systems, artificial intelligence, and intelligent surveillance technologies. His research is centered on the development of a Pipeline Inspection Air Buoyancy Hybrid Drone, a novel UAV concept that combines lighter-than-air and heavier-than-air technologies to improve flight endurance, stability, and inspection efficiency. By integrating deep learning–based object detection architectures into UAV platforms, his work advances real-time pipeline monitoring, anomaly identification, and autonomous decision-making for industrial applications. His contributions span AI-driven automation, robotics, swarm intelligence, energy-efficient IoT systems, and 5G-enabled surveillance technologies. He has authored several research papers in reputable international journals and conferences, including publications in IEEE Access, Neurocomputing, and Elsevier venues. These works address UAV reconnaissance, transformer-based detection models for pipeline integrity assessment, and optimization frameworks inspired by swarm behavior. His research output reflects measurable scholarly influence, with 15 Scopus citations, 7 indexed documents, and an h-index of 2. Mr. Ibrahim Aromoy has participated in multiple research and development projects, contributing to UAV design, embedded hardware integration, machine learning pipelines, and cyber-secure control systems. His work in hybrid drone architecture and automated surveillance has been supported by competitive institutional funding, reinforcing the technological relevance and innovation potential of his research. His scientific contributions extend beyond publications to academic service. He serves as a peer reviewer for high-impact journals such as IEEE Access and Results in Engineering, supporting the advancement of rigorous research dissemination in engineering and applied sciences. His expertise also includes AI vision systems, OpenCV-based automation, and embedded cybersecurity applications for unmanned systems, further strengthening his interdisciplinary research profile. Mr. Ibrahim Aromoy has been recognized with research-focused scholarships and academic distinctions that support his ongoing work in UAV innovation and intelligent automation. Through his integrated expertise in UAV engineering, deep learning, and intelligent inspection systems, he continues to contribute meaningfully to the evolution of smart surveillance, autonomous robotics, and AI-augmented engineering technologies.

Profiles: Scopus | ORCID | Google Scholar | ResearchGate | Web of Science

Featured Publications

1. Aromoye, I., Lo, H., Sebastian, P., Ghulam, E., & Ayinla, S. (2025). Significant advancements in UAV technology for reliable oil and gas pipeline monitoring. Computer Modeling in Engineering & Sciences, 142(2), 1155.

2. Aromoye, I. A., Hiung, L. H., & Sebastian, P. (2025). P-DETR: A transformer-based algorithm for pipeline structure detection. Results in Engineering, 26, 104652.

3. Zahid, F., Ali, S. S. A., & Aromoye, I. A. (2025). Exploring the potential benefits and overcoming the constraints of virtual and augmented reality in operator training. Transportation Research Procedia, 84, 625–632.

4. Mansoor, Y., Zahid, F., Azhar, S. S., Rajput, S., & Aromoye, I. A. (2025). Energy-efficient solar water pumping: The role of PLCs and DC-DC boost converters in addressing water scarcity. Transportation Research Procedia, 84, 681–688.

5. Zahid, F., Rajput, S., Ali, S. S. A., & Aromoye, I. A. (2025). Challenges and innovations in 3D object recognition: The integration of LiDAR and camera sensors for autonomous applications. Transportation Research Procedia, 84, 618–624.

Amir R. Masoodi | Artificial Intelligence and Machine Learning | Editorial Board Member

Published on by New Scientists

Assist. Prof. Dr. Amir R. Masoodi | Artificial Intelligence and Machine Learning | Editorial Board Member

Assistant Professor | Ferdowsi University of Mashhad | Iran

Assist. Prof. Dr. Amir R. Masoodi is a highly accomplished structural engineering researcher whose work spans nonlinear mechanics, composite structures, vibration analysis, finite element modeling, and advanced material systems. With 1,789 Scopus citations, 77 publications, and an h-index of 29, he has established a strong international research footprint in computational mechanics, structural stability, soil–structure interaction, wave propagation, and multiscale modeling of advanced composites. His research contributions include developing novel finite element formulations for beams, plates, and shells, particularly for functionally graded materials (FGMs), carbon nanotube (CNT)-reinforced composites, graphene nanocomposites, and porous structural systems. Assist. Prof. Dr. Amir R. Masoodi’s work on nonlinear dynamic analysis, thermal–mechanical coupling, shell instability, and multiscale behavior of nano-engineered materials has been widely cited and influential in advancing modern structural design methodologies. He has published extensively in leading journals such as Composite Structures, Engineering Structures, Mechanics of Advanced Materials and Structures, Aerospace Science and Technology, Scientific Reports, and Applied Sciences. His publications address cutting-edge topics including vibration of hybrid nano-reinforced shells, multiscale characterization of nanocomposites, nonlinear buckling behavior of tapered beams, thermomechanical modeling of composite cables, and smart materials incorporating shape-memory alloys. Assist. Prof. Dr. Amir R. Masoodi has presented his findings at numerous international conferences and contributed several book chapters, including work on nanofillers and thermal properties in advanced materials. His research output extends to R&D projects, predictive modeling, and computational innovations in structural and nano-engineered systems. He has been recognized with multiple distinguished researcher awards, national elite recognitions, and research excellence honors. His expertise is further reflected in his editorial board memberships and contributions as a reviewer for reputable journals. Overall, Assist. Prof. Dr. Amir R. Masoodi’s research stands at the intersection of computational mechanics, smart materials, and multiscale structural engineering, offering impactful advances for next-generation civil, mechanical, and aerospace systems.

Profiles: Scopus | ORCID | Google Scholar | Sci Profiles | Web of Science

Featured Publications

1. Sobhani, E., Masoodi, A. R., & Ahmadi-Pari, A. (2021). Vibration of FG-CNT and FG-GNP sandwich composite coupled conical–cylindrical–conical shell. Composite Structures, 273, 114281.

2. Sobhani, E., Masoodi, A. R., Civalek, O., & Ahmadi-Pari, A. R. (2021). Agglomerated impact of CNT vs. GNP nanofillers on hybridization of polymer matrix for vibration of coupled hemispherical–conical–conical shells. Aerospace Science and Technology, 120, 107257.

3. Rezaiee-Pajand, M., Sobhani, E., & Masoodi, A. R. (2020). Free vibration analysis of functionally graded hybrid matrix/fiber nanocomposite conical shells using multiscale method. Aerospace Science and Technology, 105, 105998.

4. Rezaiee-Pajand, M., Arabi, E., & Masoodi, A. R. (2019). Nonlinear analysis of FG-sandwich plates and shells. Aerospace Science and Technology, 87, 178–189.

5. Rezaiee-Pajand, M., Masoodi, A. R., & Mokhtari, M. (2018). Static analysis of functionally graded non-prismatic sandwich beams. Advances in Computational Design, 3(2), 165–190.

Soufiane Bacha | Artificial Intelligence | Best Researcher Award

Published on by New Scientists

Mr. Soufiane Bacha | Artificial Intelligence | Best Researcher Award

PhD Student, University of Science and Technology Beijing, Algeria

Mr. Soufiane Bacha is a promising young researcher in Artificial Intelligence and Data Quality with a strong academic background and growing international exposure. He is currently pursuing a Ph.D. in Data Quality at the University of Science and Technology Beijing (2023–ongoing) and a Ph.D. in Cancer Epidemiology at the Department of Computer Science, Ibn Khaldoun University of Tiaret, Algeria (2021–2025). He also holds a Master’s degree in Software Engineering (2019–2021), where he ranked first in his class and completed a thesis on imbalanced datasets and boosting methods, and a B.Sc. in Computer Science (2016–2019) with strong foundations in algorithms, cryptography and programming. Professionally, Mr. Soufiane Bacha gained valuable international research experience through an internship at the Faculty of Polytechnic Mons, UMONS University in Belgium, where he worked on Internet of Things (IoT) applications involving Raspberry Pi, Arduino and sensor technologies. He has served as a part-time lecturer in Graph Theory and as an ICT trainer in web development, demonstrating strong teaching, leadership, and communication skills. His research interests span artificial intelligence, data quality, machine learning for imbalanced datasets, cancer epidemiology, distributed applications and business analytics. He is proficient in Python, C/C++, Java, SQL and data analysis tools, with expertise in OLAP, data mining, and deep learning frameworks. His achievements include an NVIDIA Deep Learning Institute Certificate, participation in AI workshops, and a Scopus-indexed publication. With a dual doctoral training and interdisciplinary focus, Mr. Soufiane Bacha is well-positioned to make impactful contributions to AI-driven data quality research and healthcare analytics on a global scale.

Profile: ORCID | Google Scholar | ResearchGate

Featured Publications

1. Bacha, S., Ning, H., Mostefa, B., Sarwatt, D. S., & Dhelim, S. (2025). A novel double pruning method for imbalanced data using information entropy and roulette wheel selection for breast cancer diagnosis (arXiv:2503.12239).