Luciano Alessandro Ipsaro Palesi | Computer Science | Research Excellence Award

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Research Excellence Award

Luciano Alessandro Ipsaro Palesi
University of Florence, Italy

Luciano Alessandro Ipsaro Palesi
AffiliationUniversity of Florence
CountryItaly
Scopus ID57226812823
Documents24
Citations282
h-index9
Subject AreaComputer Science
EventInternational Research Awards
ORCID0000-0001-8992-2084

Luciano Alessandro Ipsaro Palesi is a researcher affiliated with the University of Florence whose scholarly activities focus on computer science, artificial intelligence, smart cities, digital twins, intelligent transportation systems, and Internet of Things applications. His publication record demonstrates consistent contributions to data-driven urban intelligence and decision-support technologies. His research profile reflects interdisciplinary collaboration and sustained engagement with emerging computational methodologies.[1]

Abstract

This article summarizes the academic profile and research accomplishments of Luciano Alessandro Ipsaro Palesi. His work emphasizes intelligent mobility, explainable artificial intelligence, digital twin infrastructures, and smart city technologies. Through peer-reviewed publications and collaborative projects, he has contributed to practical solutions addressing urban planning, transportation optimization, and data-centric decision support systems.[2]

Keywords

Artificial Intelligence, Explainable AI, Smart Cities, Digital Twins, Intelligent Transportation Systems, Internet of Things, Deep Learning, Urban Mobility Analytics, Decision Support Systems, Data-Driven Computing.

Introduction

Modern computer science increasingly intersects with urban intelligence and connected infrastructures. Luciano Alessandro Ipsaro Palesi has participated in research addressing these evolving challenges through innovative computational models and scalable architectures. His publications highlight the integration of artificial intelligence with real-world mobility and smart city environments, creating measurable benefits for public services and urban sustainability.[3]

Research Profile

Affiliated with the University of Florence, Palesi has developed a research portfolio spanning digital twins, machine learning, mobility analytics, and Internet of Things ecosystems. His publication metrics include 24 indexed documents, 282 citations, and an h-index of 9. These indicators demonstrate active scholarly engagement and growing influence within applied computer science research communities.[1]

Research Contributions

His contributions include AI-driven traffic optimization, privacy-preserving mobility analysis, digital twin frameworks, and explainable artificial intelligence applications. Research outputs have explored predictive transportation models, public mobility demand matching, smart parking solutions, and urban environmental analytics. These studies support evidence-based decision making for municipalities and intelligent service platforms.[4]

Publications

Recent publications include studies on human-centered artificial intelligence, dynamic mobility demand matching, traffic signal optimization, digital twin architectures, and explainable AI methodologies. His work appears in recognized venues such as IEEE Access, Expert Systems with Applications, Computer Networks, Applied Soft Computing, and Big Data and Cognitive Computing. These publications collectively illustrate a consistent focus on intelligent systems and urban innovation.[5]

Research Impact

The practical orientation of his research has contributed to advancements in mobility management, smart infrastructure monitoring, and AI-enabled public services. Citation activity and collaborations across interdisciplinary teams indicate the relevance of his work within academic and applied settings. His studies support scalable solutions for contemporary urban and technological challenges.[2]

Award Suitability

The International Research Awards recognize individuals demonstrating sustained scholarly achievement and meaningful scientific contributions. Palesi’s publication record, citation performance, and involvement in advanced computational research align with these objectives. His interdisciplinary approach and engagement with emerging technologies support his suitability for recognition within international academic award programs.

Conclusion

Luciano Alessandro Ipsaro Palesi has established a notable academic profile through research focused on intelligent systems, smart cities, and artificial intelligence. His contributions demonstrate technical rigor, practical relevance, and collaborative scholarship. Continued research activity within these domains is expected to further strengthen his academic impact and professional recognition.

References

  1. Elsevier. (n.d.). Scopus author details: Luciano Alessandro Ipsaro Palesi, Author ID 57226812823. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57226812823
  2. Fanfani, M., Ipsaro Palesi, L. A., & Nesi, P. (2026). Human-Centered AI for Decision Support Systems: A Systematic Review of Application Domains, Architecture Designs, Current Trends and Future Directions. Big Data and Cognitive Computing.
    https://doi.org/10.3390/bdcc10060186
  3. Bellini, P., Ipsaro Palesi, L. A., Giovannoni, A., & Nesi, P. (2023). Managing Complexity of Data Models and Performance in Broker-Based Internet/Web of Things Architectures. Internet of Things.
    https://doi.org/10.1016/j.iot.2023.100834
  4. Fereidooni, Z., Ipsaro Palesi, L. A., & Nesi, P. (2025). Multi-Agent Optimizing Traffic Light Signals Using Deep Reinforcement Learning. IEEE Access.
    https://doi.org/10.1109/ACCESS.2025.3578518
  5. Bilotta, S., Ipsaro Palesi, L. A., & Nesi, P. (2025). Exploiting Open Data for CO2 Estimation via Artificial Intelligence and Explainable AI. Expert Systems with Applications.
    https://doi.org/10.1016/j.eswa.2025.128598

Yan-Jiang Zhao | Robotics and Automation | Excellence in Research Award

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Prof. Yan-Jiang Zhao | Robotics and Automation | Excellence in Research Award 

Harbin University of Science and Technology | China

Prof. Yan-Jiang Zhao is a distinguished researcher and academic leader in the field of mechanical engineering and medical robotics, currently serving as Professor and Head of the Department of Engineering Graphics at Harbin University of Science and Technology, China. With extensive international research exposure, including postdoctoral training at Thomas Jefferson University, USA, he has established a strong interdisciplinary research program that bridges mechanical engineering, robotics, and medical applications. Prof. Zhao’s primary research interests encompass medical robotics, flexible and steerable needle insertion systems, multi-fingered dexterous robotic hands, composite filament winding technologies, and TRIZ-based mechanical system innovation. His work focuses on integrating mechanics-based modeling, multimodal sensing, and intelligent control strategies to improve precision, safety, and adaptability in robotic systems, particularly for minimally invasive medical procedures. His recent research efforts include variable-curvature flexible needle insertion, multimodal perception frameworks, and advanced control methods for medical robotic platforms. Throughout his academic career, Prof. Zhao has led or participated in more than 20 competitive research projects, including eight national-level grants such as the National Natural Science Foundation of China (NSFC) General and Young Scientist Funds, alongside multiple provincial and enterprise-funded projects. These projects have resulted in the development of functional robotic prototypes, innovative mechanical designs, and industry-ready technologies. In parallel, he has completed several consultancy and industry collaborations, contributing to composite material structural component development and industrial product design optimization. Prof. Zhao has an impressive scholarly output, having published over 40 peer-reviewed journal articles, with more than 30 indexed in SCI and EI databases. His research has appeared in leading international journals including Expert Systems with Applications, IEEE Robotics and Automation Letters, IEEE Access, Medical Engineering & Physics, Physics in Medicine & Biology, and the International Journal of Medical Robotics and Computer-Assisted Surgery. According to the Web of Science Core Collection, his indexed publications have received over 90 citations, with an h-index of 6 (2025).

Citation Metrics (Scopus)

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Documents
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h-index
8

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View Scopus Profile

Featured Publications

 

Carlo Alessi | Robotics and Automation | Best Researcher Award

Published on by Research Awards

Mr. Carlo Alessi | Robotics and Automation | Best Researcher Award

Ph.D. Candidate at The Bio Robotics Institute, Sant’Anna School of Advanced Studies, Italy.

Carlo Alessi is a Ph.D. candidate specializing in BioRobotics at Scuola Superiore Sant’Anna in Pisa, Italy. His research focuses on reinforcement learning for soft robot control, supported by a prestigious three-year grant under the EU project PROBOSCIS. Carlo’s academic journey includes M.Sc. and B.Sc. degrees in Computer Science from the University of Pisa, where he explored artificial intelligence and robotics through international study grants and research internships. He has contributed to notable publications and actively participates in leading conferences, showcasing his expertise in machine learning frameworks and robotics technologies. Carlo’s future endeavors include a post-doctoral position at the Italian Institute of Technology, Genova, where he will continue advancing robotic manipulation using tactile sensors in projects like ergoCub and FAIR. His interdisciplinary approach and commitment to innovation position him as a promising leader in the field of robotics and AI.

Professional Profiles:

Education 🎓

Carlo Alessi holds a Bachelor of Science degree in Computer Science with honors from the University of Pisa, Italy, complemented by an Erasmus+ Study grant at the University of Bristol. His research during this period centered on convolutional neural networks for species detection in natural images. He proceeded to earn a Master of Science in Computer Science (Artificial Intelligence) at the same institution, achieving distinction. His master’s thesis focused on modulating motor commands using FORCE-trained Spiking Neural Networks, with additional studies at the University of Barcelona and Polytechnic University of Catalonia through another Erasmus+ grant. Currently a Ph.D. candidate in BioRobotics at The BioRobotics Institute, Scuola Superiore Sant’Anna Pisa, Carlo’s research is funded by a three-year grant under the EU project PROBOSCIS, exploring Reinforcement Learning for Soft Robot Control. He actively tutors M.Sc. students and participates in leading conferences like the IEEE-RAS International Conference on Soft Robotics and Mediterranean Machine Learning Summer School.

Professional Experience

Carlo Alessi’s professional journey is marked by significant roles in cutting-edge robotics research. As a Research Assistant at The BioRobotics Institute, Scuola Superiore Sant’Anna Pisa, Italy, he developed educational tools for soft robotics control using machine learning. His Ph.D. Research Internship at Bristol Robotics Laboratory, UK, focused on reinforcement learning for soft robot control, supported by an Erasmus+ Traineeship grant. Moving forward, Carlo is poised to join the Italian Institute of Technology, Genova, Italy, as an (Incoming) Post-Doctoral Researcher in the HumanoiD Sensing and Perception Group under Dr. Lorenzo Natale. Here, he will advance his expertise in machine learning for robot manipulation using tactile sensors, contributing to projects like ergoCub and FAIR. Carlo’s career highlights his dedication to robotics and artificial intelligence, coupled with a commitment to pushing the boundaries of research in bio-robotics. His contributions in academia and industry underscore his role as a leader in the field, driving innovation and technological advancement.

Research Interest

Carlo Alessi’s research interests revolve around the intersection of robotics, artificial intelligence, and machine learning, particularly in the domain of soft robotics. His doctoral research focuses on reinforcement learning techniques for enhancing the control and adaptability of soft robotic systems. Carlo is keenly interested in applying these methodologies to improve robot manipulation and sensory perception, with a specific emphasis on tactile sensors and their integration into robotic platforms. He is also intrigued by the broader implications of his research in advancing human-robot interaction and exploring new paradigms in robotic control strategies. Carlo’s work aims to contribute significantly to the development of intelligent robotic systems capable of autonomous learning and adaptation in dynamic environments. His interdisciplinary approach combines theoretical insights with practical applications, aiming to address real-world challenges in robotics and pave the way for future innovations in bio-robotics and beyond.

Award and Honors

Carlo Alessi has earned several prestigious accolades and honors throughout his academic and professional journey. He secured a significant Ph.D. research grant to advance his studies in BioRobotics at The BioRobotics Institute, Scuola Superiore Sant’Anna Pisa, Italy, where his focus lies in reinforcement learning for enhancing soft robot control capabilities. His academic pursuits were further enriched by Erasmus+ Study Grants, facilitating enriching experiences at the University of Bristol, UK, and the University of Barcelona & Polytechnic University of Catalonia, Spain, where he explored cutting-edge applications in computer science and artificial intelligence. Carlo’s commitment to research excellence is underscored by impactful internships at institutions like Bristol Robotics Laboratory, UK, and Technical University of Munich, Germany, where he contributed to pioneering research in robotics and AI. He actively engages in the global academic community through participation in esteemed conferences such as the IEEE-RAS International Conference on Soft Robotics and the Mediterranean Machine Learning Summer School, where he shares insights and collaborates on advancing technological frontiers in his field. These honors highlight Carlo’s dedication to pushing the boundaries of robotics research and his significant contributions to the academic community.

Research Skills

Carlo Alessi possesses a strong foundation in robotics and artificial intelligence research, emphasizing expertise in reinforcement learning and machine learning frameworks such as Stable-baselines, OpenAI Gym, and PyTorch. His practical skills extend to ROS, Gazebo simulation, Arduino, and Lego Mindstorms, facilitating the development and integration of robotic systems. Proficient in C, C++, Python, and Java, Carlo applies these languages alongside Matlab, Bash scripting, and SQL for algorithm development and data analysis. He excels in experimental design, execution, and statistical analysis, pivotal for deriving insights in his research. Carlo’s ability to communicate findings effectively through publications and presentations at international conferences underscores his dedication to advancing the field, making significant contributions to robotics and AI research.

Publications

  1. Learning a Controller for Soft Robotic Arms and Testing its Generalization to New Observations, Dynamics, and Tasks
    • Authors: Alessi, C., Hauser, H., Lucantonio, A., Falotico, E.
    • Conference: 2023 IEEE International Conference on Soft Robotics, RoboSoft 2023
    • Year: 2023
    • Citations: 2
  2. Ablation Study of a Dynamic Model for a 3D-Printed Pneumatic Soft Robotic Arm
    • Authors: Alessi, C., Falotico, E., Lucantonio, A.
    • Journal: IEEE Access, 2023, 11, pp. 37840–37853
    • Year: 2023
    • Citations: 7