Prof. Dr. Tamara Gajic | Artificial Intelligence Awards | Top Researcher Award

Prof. Dr. Tamara Gajic, Geographical Institute “Jovan Cvijic” Serbian Academy of Sciences and Arts, Belgrade, Serbia

Tamara Gajić is a distinguished Senior Research Associate at the Geographical Institute “Jovan Cvijić” of the Serbian Academy of Sciences and Arts (SASA), specializing in social geography. She holds a Ph.D. in Geosciences from the University of Novi Sad and has extensive experience in research and education across various institutions. Her academic career spans several positions, including Senior Researcher at the Institute of Environmental Engineering, People’s Friendship University of Russia (RUDN University), and Associate Professor at Singidunum University in Belgrade. She has also served as a professor and assistant professor at various universities in Serbia, Bosnia, and Herzegovina. Gajić’s research focuses on rural development, tourism management, and sustainable practices in agritourism, gastrotourism, and sport tourism. She has contributed to numerous projects, including the modernization of tourism study programs in Serbia and feasibility studies for spa tourism. Gajić is an active member of various professional organizations, including the Serbian Geographical Society and the Tourist Organization of Serbia, and has mentored numerous graduate and doctoral students. Her expertise in integrating economics, service quality, and human resources in tourism management has earned her recognition as one of the top 10% of distinguished scientists in Serbia in 2024.

Professional Profile:

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Suitability of Tamara Gajić for the Top Researcher Award

Tamara Gajić is highly qualified for the Top Researcher Award due to her extensive academic and professional achievements in the fields of Geography, Rural Studies, and Tourism Management. Below are the key reasons why she is a suitable candidate for this prestigious award:

Academic Degrees:

🎓 Ph.D. in Geosciences
📅 2010
University of Novi Sad, Faculty of Sciences, Department of Geography, Tourism and Hotel Management, Serbia 🇷🇸

🎓 M.Sc. in Tourism Management
📅 2007
University of Novi Sad, Faculty of Sciences, Department of Geography, Tourism and Hotel Management, Serbia 🇷🇸

🎓 B.Sc. in Tourism Management
📅 2001
University of Novi Sad, Faculty of Sciences, Department of Geography, Tourism and Hotel Management, Serbia 🇷🇸

Research & Teaching Interests:

🌍 Research Areas:

Geography 🌍
Rural Studies 🌾
Tourism Management 🌐
Focus on Agrotourism, Gastrotourism, and Sport Tourism 🏞️🍴🏃‍♀️
Intersection of Economics in Tourism, Service Quality, and Human Resources 💼
Sustainability in Environment and Tourism 🌱

Previous Employment:

Associate Professor
📅 February 2021 – September 2021
Faculty of Tourism and Hotel Management, Singidunum University, Belgrade, Serbia 🇷🇸
Assistant Professor
📅 October 2018 – February 2022
University for Business Studies, Banja Luka, Bosnia and Herzegovina 🇧🇦
Professor of Vocational Studies
📅 October 2008 – February 2021
Novi Sad Business School, Novi Sad, Serbia 🇷🇸

Publication top Notes:

Innovative Approaches in Hotel Management: Integrating Artificial Intelligence (AI) and the Internet of Things (IoT) to Enhance Operational Efficiency and Sustainability

The Contribution of the Farm to Table Concept to the Sustainable Development of Agritourism Homesteads

Fostering Sustainable Urban Tourism in Predominantly Industrial Small-Sized Cities (SSCs)—Focusing on Two Selected Locations

Leveraging digital platforms for responsible sports tourism: Budapest’s role in the 2020 European football championship

Tourists’ Willingness to Adopt AI in Hospitality—Assumption of Sustainability in Developing Countries

The Adoption of Artificial Intelligence in Serbian Hospitality: A Potential Path to Sustainable Practice

Mr. Zhongwen Hao, Cranfield University, China

Zhongwen Hao is a Master’s candidate in Aerospace Manufacturing at Cranfield University, UK, and concurrently pursuing a Master of Mechanical Engineering at Nanjing University of Aeronautics and Astronautics, China. He completed his Bachelor’s degree in Electronic Information with a focus on Image Processing from China University of Mining and Technology. His research interests include robot control, visual servoing, image processing, and deep learning. Zhongwen has led notable projects such as visual servoing of robotic arms using deep learning techniques and galaxy image classification. His proficiency in programming with C++, Python, and MATLAB, coupled with his skills in deep learning and image processing, underscores his technical expertise. He has published research on motion prediction and object detection in visual servoing systems. Zhongwen is known for his strong project execution abilities, team spirit, and resilience.

Summary of Suitability:

Hao’s research direction aligns well with cutting-edge fields such as robot control, visual servoing, image processing, and deep learning. These areas are highly relevant and significant in contemporary technological advancements. Hao has a solid educational foundation with advanced studies in Aerospace Manufacturing and Mechanical Engineering, complemented by a bachelor’s degree in Electronic Information with a focus on Image Processing. This diverse yet interconnected educational background enhances his research capabilities.

Education

Cranfield University, Bedford, UK
Master’s Candidate of Aerospace Manufacturing
Major: Deep Learning and Image Processing
September 2023 – September 2024

Nanjing University of Aeronautics and Astronautics, Nanjing, China
Master of Mechanical Engineering
Major: Mechanical
September 2022 – June 2025 (Expected)

China University of Mining and Technology, Xuzhou, China
Bachelor of Electronic Information
Major: Image Processing
September 2017 – June 2021

Work Experience

Project Leader
Research on Visual Servoing of Robotic Arms Based on Deep Learning
June 2024 – September 2024

Led research on target detection using the DETR model, trajectory planning with the PSO algorithm, and motion prediction using BiLSTM and KAN neural networks.
Integrated and simulated algorithms in ROS using Gazebo to validate their effectiveness.

Participator
Galaxy Image Classification Based on Deep Learning
February 2024 – March 2024

Handled image preprocessing and reconstruction, and implemented galaxy image classification using the VIT model, achieving a classification accuracy of 90%.