Afşin Baran Bayezit | Data Science and Deep Learning | Excellence in Research Award

Mr. Afşin Baran Bayezit | Data Science and Deep Learning | Excellence in Research Award

Research Assistant at Istanbul Technical University | Turkey

Mr. Afşin Baran Bayezit is a research engineer specializing in maritime artificial intelligence and control systems with extensive experience in reinforcement learning, machine learning, and control theory for autonomous systems, demonstrating strong expertise in embedded programming, ROS-based development, and system modeling, with proven contributions in designing and experimentally validating ship control algorithms, including autopilot systems, dynamic positioning, and safety analysis, while actively engaging in advanced research on ship behavior prediction, sensor integration, and real-world implementation of intelligent marine technologies.

Citation Metrics (Scopus)

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Citations
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🟦 Citations 🟥 Documents 🟩 h-index


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Featured Publications

 

Yasaman | Data Science and Deep Learning | Editorial Board Member

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.