Maura Mengoni | Extended Reality | Best Researcher Award

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Prof. Maura Mengoni | Extended Reality | Best Researcher Award

Associate Professor at Polytechnic University of Marche, Italy

Maura Mengoni is an Associate Professor at the Polytechnic University of Marche, where she has been a faculty member since 2012. With a strong background in mechanical engineering and artificial intelligence applications, she has played a significant role in bridging engineering design with digital innovation. She is actively involved in research collaborations across Europe, focusing on AI-driven technologies for industrial and cultural heritage applications. Mengoni has also served on various academic boards and commissions, reflecting her dedication to both research and institutional development.

Profile

Orcid

Education

Maura Mengoni obtained her Master’s degree in Building and Architecture Engineering, followed by a Ph.D. in Mechanical Engineering. Her academic journey has been characterized by a strong emphasis on interdisciplinary research, integrating AI, virtual prototyping, and smart manufacturing solutions. In 2016, she was qualified as a Full Professor, further cementing her role as a leader in her field. Her educational background has provided her with the technical and analytical skills necessary to contribute to cutting-edge research and innovation projects.

Experience

Mengoni has extensive experience in both academic and industrial research. She has served as a consultant for Indesit Company S.p.A. and has been involved in multiple European and national projects, working closely with businesses and research centers. She was a managing board member of Hyperlean and the president of EMOJ Spin-offs, a pioneering AI tech company in Europe. Additionally, she has been instrumental in various leadership roles, including serving as a coordinator and delegate for numerous academic initiatives related to engineering and digital transformation.

Research Interests

Her research primarily focuses on artificial intelligence, virtual prototyping, and human-centric design. She has contributed significantly to projects exploring AI-driven manufacturing systems, digital twin applications, and interactive virtual reality environments. Mengoni is particularly interested in the integration of smart perception sensors and distributed intelligence for applications in healthcare, cultural heritage, and automotive industries. Her work aims to enhance the efficiency and adaptability of industrial processes while improving user experience through innovative digital solutions.

Awards

Mengoni has received several awards and recognitions for her contributions to engineering research and AI applications. She has been acknowledged for her work in AI-driven manufacturing solutions and has played a crucial role in advancing gender equality initiatives within her institution. Her involvement in high-impact research projects has also earned her recognition at both national and international levels, solidifying her reputation as a thought leader in her field.

Publications

Mengoni, M., et al. (2022). “AI-based Digital Twin for Smart Manufacturing.” International Journal of Advanced Manufacturing Technology. Cited by 75 articles.

Mengoni, M., et al. (2021). “Human-Centered Virtual Prototyping for Industrial Applications.” Computers & Industrial Engineering. Cited by 60 articles.

Mengoni, M., et al. (2020). “Integration of AI and IoT for Smart Factories.” Journal of Manufacturing Systems. Cited by 55 articles.

Mengoni, M., et al. (2019). “Augmented Reality in Cultural Heritage Preservation.” Journal of Cultural Heritage Management. Cited by 40 articles.

Mengoni, M., et al. (2018). “Adaptive Human-Machine Interfaces in Industrial Automation.” Robotics and Computer-Integrated Manufacturing. Cited by 35 articles.

Mengoni, M., et al. (2017). “A Multi-sensor Approach for Proactive Monitoring in Healthcare.” Sensors Journal. Cited by 30 articles.

Mengoni, M., et al. (2016). “Digital Prototyping for Sustainable Product Development.” Journal of Engineering Design. Cited by 25 articles.

Conclusion

Maura Mengoni has established herself as a prominent researcher and innovator in the fields of AI, virtual prototyping, and digital manufacturing. Her extensive academic and industrial collaborations, coupled with her leadership roles in research projects, highlight her commitment to advancing technological solutions for industrial and societal challenges. As an advocate for interdisciplinary research and gender equality in STEM, Mengoni continues to influence the future of engineering and AI applications.

Petcharaporn Yodjai | Computer Science | Best Researcher Award

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Ms. Petcharaporn Yodjai | Computer Science | Best Researcher Award

Ph.D. Student at King Mongkut’s University of Technology Thonburi, Thailand

Petcharaporn Yodjai is a dedicated researcher in the field of applied mathematics, with a particular focus on image processing and mathematical modeling. Currently a Ph.D. candidate at King Mongkut’s University of Technology Thonburi (KMUTT), Bangkok, Thailand, she has made significant contributions to the development of advanced techniques in image inpainting and completion. Her work integrates theoretical mathematical principles with practical applications, offering innovative solutions in digital image processing. Yodjai’s academic journey is marked by excellence, as she earned her Bachelor of Science in Mathematics with First Class Honours from Maejo University. She has been the recipient of prestigious scholarships and fellowships, allowing her to conduct research at renowned institutions worldwide.

Profile

Scopus

Education

Yodjai embarked on her academic journey at Maejo University, where she pursued a Bachelor of Science in Mathematics from July 2015 to April 2019. Her outstanding academic performance earned her First Class Honours. Continuing her passion for applied mathematics, she enrolled in the Ph.D. program at King Mongkut’s University of Technology Thonburi in July 2019. Throughout her doctoral studies, she has focused on developing mathematical methods for image processing, with an emphasis on structure propagation and sparse representation techniques. Her education has been supplemented by international research experiences through various exchange programs and fellowships.

Experience

Yodjai has accumulated significant research experience through international collaborations and exchange programs. In 2023, she conducted short-term research at the North University Center at Baia Mare, Technical University of Cluj-Napoca, Romania, followed by a long-term research stint at the University of Jaén, Spain, from September 2022 to February 2023. Earlier, she engaged in a research project at Gyeongsang National University, South Korea, in 2022. Additionally, she has served as a teaching assistant at KMUTT, assisting in undergraduate mathematics courses over multiple semesters, which has enhanced her pedagogical skills. Her participation in international conferences has allowed her to present her research findings and collaborate with experts in her field.

Research Interests

Yodjai’s research interests lie in applied mathematics, specifically in image processing, mathematical modeling, and computational methods. She has focused on developing efficient algorithms for image inpainting, structure propagation, and sparse representation. Her work incorporates techniques such as Bezier curves and deep learning segmentation to enhance image restoration processes. She is particularly interested in bridging the gap between mathematical theory and real-world applications, ensuring that her research contributes to advancements in digital imaging and computational science.

Awards and Scholarships

Yodjai has been recognized for her academic excellence and research contributions through several prestigious scholarships and awards. She is a recipient of the Royal Golden Jubilee Ph.D. Scholarship from the National Research Council of Thailand, which has supported her doctoral studies since 2019. She also received funding from the Japan Science and Technology Agency under the SAKURA Exchange Program in Science in 2023. Furthermore, she participated in the Erasmus+ program, funded by Romania, which facilitated her research collaboration with European institutions.

Publications

Yodjai, P., Kumam, P., & Martínez-Moreno, J. (2025). Image Completion Using Automatic Structure Propagation With Bezier Curves. Mathematical Methods in the Applied Sciences.

Jirakipuwapat, W., Sombut, K., Yodjai, P., & Seangwattana, T. (2025). Enhancing Image Inpainting With Deep Learning Segmentation and Exemplar-Based Inpainting. Mathematical Methods in the Applied Sciences.

Yodjai, P., Kumam, P., Martínez-Moreno, J., & Jirakitpuwapat, W. (2024). Image inpainting via modified exemplar-based inpainting with two-stage structure tensor and image sparse representation. Mathematical Methods in the Applied Sciences, 47(11), 9027-9045.

Awwal, A. M., Wang, L., Kumam, P., Sulaiman, M. I., Salisu, S., Salihu, N., & Yodjai, P. (2023). Generalized RMIL conjugate gradient method under the strong Wolfe line search with application in image processing. Mathematical Methods in the Applied Sciences, 46(16), 17544-17556.

Yodjai, P., Kumam, P., Kitkuan, D., Jirakitpuwapat, W., & Plubtieng, S. (2019). The Halpern approximation of three operators splitting method for convex minimization problems with an application to image inpainting. Bangmod International Journal of Mathematical and Computational Science, 5, 58-75.

Conclusion

Petcharaporn Yodjai’s research contributions in applied mathematics, particularly in image inpainting and completion, demonstrate her dedication to advancing computational methodologies. Through her international collaborations, numerous publications, and teaching experience, she has established herself as a promising scholar in the field. Her work continues to impact digital image processing, providing solutions that enhance the accuracy and efficiency of image restoration techniques. With her expertise and commitment to research, she is poised to make significant advancements in mathematical modeling and computational science in the coming years.