Mr. Ahmed Gueye is a PhD student at Cheikh Anta Diop University of Dakar, Senegal, specializing in Physical Measurements, Control, and Regulation Systems. His research focuses on innovative approaches to air quality forecasting using the Internet of Things (IoT), data assimilation, and neural networks. With a strong background in electronics, environmental monitoring, and renewable energy systems, he has contributed to several national and international research projects and conferences. Ahmed is committed to developing smart, data-driven solutions for sustainable environmental management in West Africa.
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Summary:
Mr. Ahmed Gueye is an emerging scholar with a compelling portfolio of interdisciplinary research in environmental physics, renewable energy systems, and smart sensor networks. His work is well-aligned with global sustainability goals and addresses urgent regional challenges through data-driven innovation. His combination of academic merit, publication output, and social relevance make him a strong contender for the Research for Best Scholar Award.
🎓 Education
Ahmed Gueye holds a Bachelor’s degree in Physics, Chemistry, and Material Science from Cheikh Anta Diop University of Dakar, earned in 2015. He later pursued a Master’s degree in Electronics, Systems, and Telecommunication with a specialization in Physical Measurements, Control, and Regulation Systems. His academic excellence is reflected in achieving high honors. Currently, he is a PhD student at Cheikh Anta Diop University, focusing on advanced methods for forecasting PM2.5 and PM10 pollution levels in Dakar. His doctoral research incorporates the Internet of Things, data assimilation, and neural networks.
💼Experience
Ahmed has amassed practical experience through several academic and professional roles. He worked with SEDIMA Group and served as a Design Engineer at Vigitec, where he contributed to the conceptual study of IoT systems and the design of communication layers for meteorological observation networks. He has also been involved in teaching roles at Cheikh Anta Diop University and the University of Sine Saloum El-Hadji Ibrahima Niass. As part of the World Bank’s Development Impact Evaluation (DIME) initiative, he has been actively contributing to field research and data analysis projects. Ahmed also participated in field assistant roles and has been involved in designing systems for air quality and energy measurement.
🔬Research Focus
Ahmed’s primary research interests lie in atmospheric monitoring, renewable energy, and smart systems integration. His doctoral work emphasizes the development of enhanced air pollution forecasting systems using machine learning and sensor networks. He has published in peer-reviewed journals on topics including wind and solar energy analysis, air quality modeling, and the development of low-cost IoT-based pollution monitoring systems. His research is characterized by interdisciplinary collaboration and a strong orientation toward practical, data-driven solutions for environmental and energy challenges in Africa.
🛠️Skills
Ahmed possesses a versatile skill set that spans electronics, data science, automation, and IoT applications. He is proficient in Python, home automation systems, electronic design, geographic information systems (GIS), and modeling. His technical capabilities extend to the development and implementation of measurement platforms, neural network integration for air quality forecasting, and the use of ESP8266 NodeMCU for pollution monitoring. He has hands-on experience in CCTV maintenance and the design of meteorological and pollution measurement devices.
🏆Awards
Ahmed’s academic and research work has earned him various recognitions. He contributed to award-winning projects such as the creation of innovative IoT-based air pollution monitoring systems and predictive models for PM2.5 levels. He has presented at notable events including the 9th IEEE International Conference on Smart Instrumentation, Measurement and Applications and the International Seminar on Air Quality and IoT-Based Sensors. He has also received certifications from Codecademy, including training in machine learning, PyTorch, and neural networks.