Yasaman | Data Science and Deep Learning | Editorial Board Member

Published on by Mechanics Awards

Dr. Yasaman | Data Science and Deep Learning | Editorial Board Member

Research Scholarat at Lille Univesity | France

Dr. Yasaman is a computer engineer and independent researcher from Tehran, Iran, whose academic journey spans a B.Sc. in puzzle-game mechatronic design and microcontroller-based control systems, an M.Sc. in multi-core chip testability with on-chip 3D-memory banks, and a Ph.D. focused on deep learning accelerator architectures built on networks-on-chip communication infrastructures; throughout her career she has distinguished herself through top national academic rankings, excellence awards in robotics competitions, and recognition for her highly cited research in medical-AI literature, complemented by the publication of a specialized book chapter on deep learning accelerators; her multidisciplinary expertise extends across robotics, integrated digital circuits, FPGA testability, NoC-based architectures, IoT, machine learning, AI algorithms, and advanced medical applications; her current research concentrates on machine learning and deep learning algorithms for hardware-aware intelligence, voice detection, audio recognition, and sound-based assistive systems to support individuals with neurological disorders such as stroke and dementia, while also exploring neural pattern interpretation for resilient AI-driven architectures; she has contributed as a reviewer for leading scientific journals, served as a guest editor and technical program committee member across notable international conferences, and delivered advanced teaching in digital design, VHDL, and engineering courses at major universities; her professional experience includes managing automation and environmental control systems in industrial composting facilities, engineering roles in EMS and OEM companies, and long-term research appointments at the Islamic Azad University Science and Research Branch; equipped with multilingual proficiency in French, Persian, English, and Arabic, and technical skills spanning VHDL, C-family languages, Python, Java, Matlab, SystemC tools, simulation environments, network simulators, CAD tools, and scientific typographic platforms, she continues to contribute impactful interdisciplinary research shaping advanced intelligent systems for both hardware and healthcare domains.

Profile: Google Scholar

Featured Publications:

Rahmani, A. M., & Hosseini Mirmahaleh, S. Y. (2021). Coronavirus disease (COVID-19) prevention and treatment methods and effective parameters: A systematic literature review. Sustainable Cities and Society, 64, 102568.

Hosseini Mirmahaleh, S. Y., Reshadi, M., Shabani, H., Guo, X., & Bagherzadeh, N. (2019). Flow mapping and data distribution on mesh-based deep learning accelerator. In Proceedings of the 13th IEEE/ACM International Symposium on Networks-on-Chip (NoC).

Hosseini Mirmahaleh, S. Y., & Rahmani, A. M. (2019). DNN pruning and mapping on NoC-based communication infrastructure. Microelectronics Journal, 94, 104655.

Hosseini Mirmahaleh, S. Y., Reshadi, M., & Bagherzadeh, N. (2020). Flow mapping on mesh-based deep learning accelerator. Journal of Parallel and Distributed Computing, 144, 80–97.

Rahmani, A. M., & Hosseini Mirmahaleh, S. Y. (2022). Flexible-clustering based on application priority to improve IoMT efficiency and dependability. Sustainability, 14(17), 10666.

Lubna Aziz | Data Science and Deep Learning | Best Researcher Award

Published on by Mechanics Awards

Assoc. Prof. Dr. Lubna Aziz | Data Science and Deep Learning | Best Researcher Award

Associate Professor at Iqra University Karachi | Pakistan

Assoc. Prof. Dr. Lubna Aziz is an accomplished AI and MLOps Engineer, Researcher, and Academic Leader with over fifteen years of multidisciplinary experience in artificial intelligence, machine learning, and higher education leadership, currently serving as Assistant Professor and Head of Artificial Intelligence at Iqra University, Karachi. She holds a PhD in Computer Science from Universiti Teknologi Malaysia and has earned dual Gold Medals in both her MS and BS in Computer Engineering from BUITEMS, reflecting her consistent record of academic excellence. Her professional expertise spans AI model development, scalable ML pipeline automation, MLOps deployment, Explainable AI, Computer Vision, and Generative AI, integrating research-driven innovation with real-world engineering impact. Dr. Aziz has designed and led AI curricula, supervised numerous student projects, and directed institutional initiatives aligned with HEC, NCEAC, and ABET accreditation standards. Her research advances Computer Vision, Large Language Models (LLMs), and Explainable AI (XAI) with applications across healthcare, finance, and creative AI, focusing on interpretable, multimodal, and human-centric intelligent systems. She has contributed to IEEE Access, Nature Scientific Reports, Springer, and MDPI journals, with publications exploring object detection, medical imaging, energy optimization, multimodal AI, and generative modeling. As an active reviewer for leading international journals and a keynote and technical chair for major AI and engineering conferences, she has significantly shaped discourse in emerging technologies. Her research projects include AI-driven healthcare diagnostics, cardiovascular risk modeling, and LLM intelligence benchmarking, funded by HEC, NIH, and the Royal Academy of Engineering UK. Known for her academic leadership, technical depth, and commitment to inclusive innovation, Lubna Aziz continues to bridge the gap between AI research and practical deployment, fostering the next generation of intelligent systems and ethical AI solutions.

Profile: Orcid 

Featured Publications:

Deebani, W., Aziz, L., Alawad, W. M., Alahmari, L. A., Al‐Ahmary, K. M., Alqurashi, Y., & Alwabel, A. S. A. (2025). Advancing electronic noses with transformers: Real‐time classification of hazardous odors and food freshness. Journal of Food Science.

Aziz, L., Adil, H., & Sarwar, R. (2025). Artificial sensing: AI-driven electronic nose for real-time gas leak detection and food spoilage monitoring. Sir Syed University Research Journal of Engineering & Technology.

Deebani, W., Aziz, L., Aziz, A., Basri, W. S., Alawad, W. M., & Althubiti, S. A. (2025). Synergistic transfer learning and adversarial networks for breast cancer diagnosis: Benign vs. invasive classification. Scientific Reports.

Aziz, L., Salam, M. S. B. H., Sheikh, U. U., Khan, S., Ayub, H., & Ayub, S. (2021). Multi-level refinement feature pyramid network for scale imbalance object detection. IEEE Access.

Arfeen, Z. A., Sheikh, U. U., Azam, M. K., Hassan, R., Shehzad, H. M. F., Ashraf, S., Abdullah, M. P., & Aziz, L. (2021). A comprehensive review of modern trends in optimization techniques applied to hybrid microgrid systems. Concurrency and Computation: Practice and Experience.

Aziz, L., Salam, M. S. B. H., Sheikh, U. U., & Ayub, S. (2020). Exploring deep learning-based architecture, strategies, applications and current trends in generic object detection: A comprehensive review. IEEE Access.