Pouya Sepehr | Artificial Intelligence |Best Researcher Award

Dr. Pouya Sepehr | Artificial Intelligence |Best Researcher Award

Research Fellow, Siena University, Italy

Pouya Sepehr is a researcher and urban planner specializing in the intersections of science, technology, and urban studies. He explores how technological infrastructures influence urban environments, focusing on sustainability and socio-environmental innovation.

Profile

ORCID

 

📚 Education:

Pouya holds a PhD in Science-Technology-Society from the University of Vienna, completed in 2023, with a dissertation defense in February 2024. He also holds master’s degrees from the University of Vienna in Science-Technology-Society and from Oxford Brooks University in Development and Emergency Practice. His bachelor’s degree is from Tehran University in Restorations and Conservation of Historical Buildings.

💼 Experience:

Pouya has extensive experience in project management and research. He has served as a Post-Doc Research Fellow at the University of Siena, focusing on digital social innovation in European urban contexts. Previously, he worked as a Researcher at the Institute for Advanced Studies Vienna and as a Research Assistant at various academic institutions across Europe.

🔬 Research Interests:

His research interests include the governance of technology and innovation in urban settings, urban sustainability, and the societal impacts of technological infrastructures. He is particularly interested in advancing multimodal and multispecies urbanism and promoting inclusivity and resilience in urban environments.

🏆 Awards:

Pouya Sepehr is an elected council member of 4S (Society for Social Studies of Science), recognizing his contributions to the field of Science and Technology Studies (STS).

Publications

Sepehr, Pouya. (2024). Mundane Urban Governance and AI Oversight: The Case of Vienna’s Intelligent Pedestrian Traffic Lights. Journal of Urban Technology, 31(1).

Felt, Ulrike, and Pouya Sepehr. (2024). Infrastructuring Citizenry in Smart City Vienna: Investigating Participatory Smartification between Policy and Practice. Journal of Responsible Innovation, 11(2).

Sepehr, Pouya and Ulrike Felt. (2023). Urban Imaginaries as Tacit Governing Devices: The Case of Smart City Vienna. Science, Technology, & Human Values, 48(9).

 

Ali Raza | artificial intelligence | Best Researcher Award

Mr. Ali Raza | artificial intelligence | Best Researcher Award

Lecturer, The University of Lahore, Pakistan

Ali Raza is a dedicated research scholar specializing in data science, known for his expertise in machine learning and deep learning applications. With a strong academic background and extensive professional experience in software development, he has contributed significantly to research in artificial intelligence and health informatics.

Profile

Google Scholar

📚 Education:

Ali completed his Bachelor of Science in Computer Science at KFUEIT after graduating from Iqra Degree College with a degree in Pre-Engineering. He further pursued his passion for computer science by earning a Master’s degree in Computer Science from KFUEIT, where his research focused on novel approaches in deep learning for image detection.

💼 Experience:

Ali’s professional journey includes roles as a Research Assistant at KFUEIT, where he published research articles on artificial intelligence. He has also worked as a Desktop App Developer at DexDevs Company and as a Full Stack Python Developer at BuiltinSoft Company, gaining expertise in business application development and machine learning frameworks.

🔬 Research Interests:

Ali’s research interests revolve around data science, particularly in machine learning model optimization, health informatics, and artificial intelligence applications in diverse domains such as pregnancy health analysis and network security.

🏆 Awards:

Ali has contributed significantly to research, evident from his publications and contributions as a peer reviewer for IEEE Access and PLOS ONE, highlighting his recognition in the academic community.

📄 Publications:

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction, Plos one, 2022 (cited 46 times)

A novel deep learning approach for deepfake image detection, Applied Sciences, 2022 (cited 58 times)

Predicting employee attrition using machine learning approaches, Applied Sciences, 2022 (cited 44 times)

A novel methodology for human kinematics motion detection based on smartphones sensor data using artificial intelligence, Technologies, 2023 (cited 23 times)

Novel class probability features for optimizing network attack detection with machine learning, IEEE Access, 2023