Pei Ren | Security | Best Researcher Award

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Ms. Pei Ren | Security | Best Researcher Award

Student at Shaanxi Normal University in China

Pei Ren is a dedicated early-career researcher in computer science specializing in privacy-preserving systems, cryptographic protocols, and blockchain-based crowd intelligence. He is currently pursuing a Ph.D. in Computer Science and Technology at Shaanxi Normal University. Ren’s scholarly work centers on addressing security challenges in decentralized systems, ensuring identity protection, and safeguarding user data in federated environments. His research is supported by a solid academic foundation and technical proficiency in programming and cryptographic tools. Pei Ren has co-authored several peer-reviewed articles in reputed journals such as the Journal of Systems Architecture and International Journal of Intelligent Systems. Through a blend of theoretical innovation and practical system design, he continues to make meaningful contributions to the field of information security.

Professional Profile

ORCID

Strengths for the Award

Pei Ren’s research profile demonstrates a clear focus and progression in the fields of cryptography, information security, and blockchain-based federated systems. His most recent journal article, “Secure task-worker matching and privacy-preserving scheme for blockchain-based federated crowdsourcing” published in Journal of Systems Architecture (2025), addresses complex challenges in decentralized task allocation and user privacy—an emerging and impactful research area. The integration of privacy-preserving computation with blockchain shows a deep understanding of both secure computation and distributed architectures.

Earlier work such as “IPSadas: Identity‐privacy‐aware secure and anonymous data aggregation scheme” published in the International Journal of Intelligent Systems (2022) further emphasizes Pei Ren’s expertise in data privacy and secure aggregation, particularly in federated systems. The emphasis on identity protection and anonymous data sharing reflects a consistent research direction aimed at real-world applicability, especially in privacy-sensitive environments like healthcare and IoT.

Moreover, Pei Ren has contributed to anonymous communication systems, as evidenced by the 2021 publication in Security and Communication Networks, which proposed an efficient scheme to protect the location privacy of IoT nodes. His technical skill set includes cryptographic tools (OpenSSL, GnuPG), programming (JavaScript), and system modeling (Visio, CTeX), enabling him to work across different layers of secure system design—from theoretical model to implementation.

Education 

Pei Ren has cultivated a progressive academic path in the field of computer science and technology. He is currently enrolled in a Ph.D. program at Shaanxi Normal University (since 2022), focusing on privacy-preserving mechanisms for secure data exchange and decentralized systems. Prior to this, he obtained a Master’s degree (2019–2022) and a Bachelor’s degree (2015–2019) from Qufu Normal University, where he developed a strong grounding in software engineering and cryptographic principles. Across all stages of his academic career, Ren has demonstrated a keen interest in the convergence of blockchain, cybersecurity, and data aggregation techniques. His solid educational background is further reinforced by relevant certifications and extensive experience with cryptographic software, programming environments, and data privacy frameworks.

Experience 

Pei Ren’s academic and research experience spans over eight years, primarily within university-led research labs. While pursuing his master’s and doctoral degrees, Ren actively engaged in secure systems design, federated learning environments, and anonymous communication protocols. He has co-authored multiple journal articles and conference papers, often in collaboration with experienced researchers and interdisciplinary teams. His experience includes designing privacy-preserving communication schemes, developing blockchain-based task-matching systems, and contributing to identity-protection models in IoT environments. In addition to research, he has gained expertise in using security tools such as OpenSSL and GnuPG, along with programming and modeling software like JavaScript, PyCharm, and Visio. This blend of theoretical knowledge and practical implementation has allowed Ren to contribute meaningfully to the development of secure, scalable, and privacy-aware digital infrastructures.

Research Focus 

Pei Ren’s research focuses on cryptography, blockchain security, and privacy-preserving mechanisms in decentralized systems. He explores secure identity authentication methods across systems, task matching frameworks for federated crowdsourcing, and pseudonym-based anonymity schemes. His work often intersects cryptographic techniques with real-world applications such as the Internet of Things (IoT), secure data aggregation, and decentralized marketplaces. A key component of his research involves balancing usability with security—designing systems that not only protect user data but also maintain performance and trust in distributed environments. Ren also investigates cross-system authentication and the implementation of reputation mechanisms in collaborative networks. His long-term vision is to contribute to frameworks that empower digital ecosystems to function with minimal privacy risks and maximum operational integrity.

Publication Top Notes

1. Secure Task-Worker Matching and Privacy-Preserving Scheme for Blockchain-Based Federated Crowdsourcing

Journal: Journal of Systems Architecture, 2025
Authors: Pei Ren, Bo Yang, Tao Wang, Yanwei Zhou, Feng Zhu
Summary:
This paper introduces a privacy-preserving protocol for task-worker matching in federated crowdsourcing platforms built on blockchain. By leveraging cryptographic techniques and smart contracts, the authors ensure that neither the identity nor task data of participants is exposed during matching and reward distribution. The design utilizes pseudonym identities and zero-knowledge verification to preserve privacy while maintaining the system’s transparency and trustworthiness.

2. IPSadas: Identity‐Privacy‐Aware Secure and Anonymous Data Aggregation Scheme

Journal: International Journal of Intelligent Systems, 2022
Authors: Pei Ren, Fengyin Li, Ying Wang, Huiyu Zhou, Peiyu Liu
Summary:
IPSadas is a novel aggregation protocol aimed at secure data sharing in decentralized environments. The scheme ensures anonymity and data integrity while mitigating identity leakage risks. The paper details a privacy model built using homomorphic encryption and privacy-preserving credentials that enable users to contribute data without revealing personal identity. Applications in healthcare and distributed AI systems are discussed.

3. An Efficient Anonymous Communication Scheme to Protect the Privacy of the Source Node Location in the Internet of Things

Journal: Security and Communication Networks, 2021
Authors: Fengyin Li, Pei Ren, Guoyu Yang, Yuhong Sun, Yilei Wang, Yanli Wang, Siyuan Li, Huiyu Zhou, Wenjuan Li
Summary:
This work proposes a communication scheme designed to shield source node locations in IoT networks. The protocol utilizes dynamic pseudonyms and bilinear pairing to ensure end-to-end anonymity, even under active surveillance. The research tackles a key IoT vulnerability—source traceability—by offering a scalable and low-latency solution suitable for smart environments and connected infrastructure.

4. An Anonymous Communication Scheme Between Nodes Based on Pseudonym and Bilinear Pairing in Big Data Environments

Conference: 6th International Conference on Data Mining and Big Data (DMBD 2021)
Authors: Pei Ren, Liu B., Li F.Y.
Summary:
This paper presents a communication scheme designed to ensure anonymity and confidentiality in big data environments, particularly focusing on node-to-node communication. The proposed model uses pseudonym-based identities combined with bilinear pairing cryptographic mechanisms to protect node identity and prevent message traceability. The method is effective in dynamic networks where node privacy is at risk due to frequent data exchange. The paper also evaluates the performance of the scheme in terms of computational cost and security resilience, demonstrating its applicability to privacy-sensitive big data applications such as distributed sensor networks and decentralized IoT infrastructures.

Conclusion

Pei Ren presents a strong candidacy for the Best Researcher Award, particularly in the domains of blockchain security, privacy-preserving data processing, and federated systems. His focused research agenda, technical proficiency, and consistent publication record in respected venues mark him as a promising early-career researcher. With continued growth in publication impact and leadership in collaborative projects, Pei Ren is poised to make significant contributions to the field of secure and intelligent computing systems.

Ataollah Shirzadi | Natural Hazards | Best Researcher Award

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Dr. Ataollah Shirzadi | Natural Hazards | Best Researcher Award

University of Kurdistan, Iran.

Dr. Ataollah Shirzadi is an Assistant Professor at the University of Kurdistan, Iran, specializing in Watershed Management Engineering. With over 90 peer-reviewed publications, he has become an internationally recognized figure in the field of natural hazard modeling. His expertise spans flash flood forecasting, shallow landslide prediction, soil erosion modeling, and geospatial risk assessment, all enhanced through artificial intelligence techniques. Notably, he was listed among the top 2% and 1% of global scientists in 2022 and 2023 by Stanford University and ISC. Dr. Shirzadi has contributed to several high-impact collaborative projects, including an international Iran-China initiative on flood susceptibility. He serves as a reviewer and editorial member for multiple international journals and actively mentors graduate researchers. His research bridges theoretical modeling and real-world disaster management, making significant strides in environmental resilience.

🔎Author Profile

🏅Awards and Honors

  1. Ranked among the Top 2% Scientists in the World

    • Source: Stanford University Ranking

    • Years: 2022 and 2023

  2. Ranked among the Top 1% Scientists in the Islamic World

    • Source: ISC (Islamic World Science Citation Center)

    • Year: 2023

  3. Elite and Talent Recognition Awards

    • By: Iran’s National Elite Foundation (for undergraduate and postgraduate academic excellence)

    • Context: Received for high GPA and top-ranking achievements during B.Sc. and M.Sc. studies

  4. National University Admission Ranks

    • Ranked 1st in M.Sc. entrance exam for the field of Mass Movements in Watershed Management

    • Ranked 3rd in B.Sc. entrance exam for Hydrology and Climatology

  5. Excellence Awards

    • University Honors: Recognized multiple times as top student in academic performance at both undergraduate and postgraduate levels

🏆Strengths for the Award

  1. High-Impact Research Contributions
    Dr. Shirzadi has co-authored over 90 international publications, with many articles appearing in top-tier journals such as Science of The Total Environment, Journal of Hydrology, Environmental Modelling & Software, Geomatics, Natural Hazards and Risk, and Remote Sensing. His research has garnered significant citations (e.g., 756 citations for a 2018 paper and 699 for a 2019 paper), reflecting the impact and relevance of his work in the scientific community.

  2. Pioneering Work in Natural Hazard Modeling
    His expertise lies in landslide, flash flood, and flood susceptibility modeling, with a strong emphasis on machine learning, deep learning, and hybrid AI techniques. Notably, he has developed several novel hybrid intelligence models integrating techniques like ANFIS, Genetic Algorithms, SVMs, Decision Trees, and ensemble approaches, widely cited and validated across diverse geographies.

  3. Recognition & Global Ranking
    He has been recognized among the top 2% and 1% of scientists globally by Stanford University and the Islamic World Science Citation Center (ISC) in 2022 and 2023 — a rare distinction that underlines his international academic standing.

  4. Editorial and Reviewer Excellence
    He holds editorial roles in journals such as Frontiers in Earth Science and has reviewed for more than 30 ISI-indexed journals, including Scientific Reports, Journal of Hydrology, CATENA, and Environmental Earth Sciences. This affirms his reputation as a trusted evaluator of cutting-edge scientific work.

  5. Academic Mentorship and Leadership
    Dr. Shirzadi has supervised over 17 M.Sc. and Ph.D. students, contributing actively to capacity building in environmental modeling and risk assessment. He has also been involved in international collaborations, including a recent Iran-China flood monitoring project, reflecting both leadership and global outreach.

🎓 Education 

Dr. Shirzadi earned his Ph.D. in Watershed Management Engineering (2014–2017) from Sari Agricultural Sciences and Natural Resources University, where his thesis focused on spatial prediction of shallow landslides using advanced data mining algorithms. He previously obtained an M.Sc. in Mass Movements (2007–2009) with a GPA of 19/20, also from Sari University, where he developed regional models for rockfall hazard mapping. His B.Sc. in Hydrology and Climatology (2003–2007) was awarded by the University of Agricultural Science and Natural Resources of Gorgan . Over the years, Dr. Shirzadi has cultivated robust expertise in geospatial modeling, sediment transport, and flood risk analysis. He also holds certifications in GIS, SPSS, AutoCAD, and multiple machine learning platforms, demonstrating both strong academic credentials and technical fluency.

🔬 Research Focus on Natural Hazards

Dr. Shirzadi’s research centers on the integration of AI, machine learning, and geospatial analysis to assess and mitigate natural hazards. His core focus includes:

  • Flood Susceptibility Mapping using hybrid machine learning models (e.g., ANFIS, rotation forests).

  • Shallow Landslide Prediction with ensemble algorithms combining decision trees, support vector machines, and neural networks.

  • Risk Zonation for erosion and sediment transport based on satellite data and multi-criteria analysis.

  • Uncertainty Quantification in hazard models to improve resilience strategies.

  • Remote Sensing Applications using Sentinel-1/2 and Landsat data for urban and rural hazard detection.

His goal is to create smart, scalable, and interpretable models that guide land use planning, policy-making, and emergency preparedness in climate-sensitive regions.

📘 Publication Top Notes

  1. A Comparative Assessment of Decision Trees Algorithms for Flash Flood Susceptibility Modeling
    Authors: Khosravi K., Pham B.T., Chapi K., Shirzadi A., et al.
    Journal: Science of The Total Environment (2018)
    Summary: Compared multiple decision tree algorithms to model flash flood susceptibility in Iran’s Haraz watershed, showing ensemble methods outperformed traditional single classifiers.

  2. Flood Susceptibility Modeling Using MCDM and Machine Learning
    Authors: Khosravi K., Shahabi H., Adamowski J., Shirzadi A., et al.
    Journal: Journal of Hydrology (2019)
    Summary: Evaluated and contrasted MCDM techniques and AI models; hybrid ML models proved superior in spatial flood risk analysis.

  3. A Novel Hybrid AI Approach for Flood Susceptibility
    Authors: Chapi K., Singh V.P., Shirzadi A., et al.
    Journal: Environmental Modelling & Software (2017)
    Summary: Developed a hybrid model combining several AI algorithms to predict flood risk, improving classification accuracy in complex terrains.

  4. Flood Susceptibility Using ANFIS-GA-DE Hybrid
    Authors: Hong H., Panahi M., Shirzadi A., et al.
    Journal: Science of the Total Environment (2018)
    Summary: Applied ANFIS enhanced with genetic and differential evolution algorithms for flood prediction in China’s Hengfeng area.

  1. Forecasting Floods Using ML & Statistical Models
    Authors: Shafizadeh-Moghadam H., Shahabi H., Shirzadi A., et al.
    Journal: Journal of Environmental Management (2018)
    Summary: Combined ML and traditional statistical techniques to forecast flood-prone zones, optimizing accuracy and runtime efficiency.

🚀Conclusion

Dr. Ataollah Shirzadi stands out as an exceptionally qualified candidate for the Best Researcher Award, with an impressive combination of scholarly output, innovative AI-based methodologies, global recognition, and academic service. His work not only advances theoretical models but also addresses urgent environmental and societal challenges. With continued growth in communication and cross-disciplinary application, he is poised to make even greater contributions to science and practice.

Kwaghgba Elijah Gbabe | Technology Scientists Innovations | Nanotechnology Innovation Award

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Dr. Kwaghgba Elijah Gbabe | Technology Scientists Innovations | Nanotechnology Innovation Award

Senior Research Officer at Nigerian Stored Products Research Institute, Nigeria

Dr. Kwaghgba Elijah Gbabe is a Senior Research Officer at the Nigerian Stored Products Research Institute, Ilorin, Nigeria. With over 9 years of experience, he specializes in food processing, postharvest technology, and agricultural nanotechnology. His research focuses on prolonging the shelf-life of perishable crops using eco-friendly nano-fibre systems and enhancing food quality through advanced preservation methods. Dr. Gbabe earned his M.Eng. in Agricultural and Environmental Engineering from the University of Agriculture, Makurdi, and is pursuing his Ph.D. in Food Processing and Technology at Benue State University. He has conducted international research at the Centre for Agricultural Nanotechnology, TNAU, India, and published multiple peer-reviewed articles. He also contributes actively to training farmers, artisans, and technical personnel. Dr. Gbabe’s work bridges the gap between sustainability and innovation in food preservation, making him a standout candidate in the technological innovation domain.

Author Profile

Strengths for the Award

  1. Strong Foundation in Agricultural Nanotechnology
    Dr. Gbabe has established a niche in the application of nanotechnology to agricultural and food preservation challenges. His Ph.D. research focuses on developing an electrospun hexanal nano-fibre matrix—a cutting-edge innovation aimed at extending the shelf-life of perishable fruits like banana, mango, and tomato.

  2. International Exposure and Training
    He completed a prestigious internship at the Centre for Agricultural Nanotechnology, TNAU, India, where he conducted nanotoxicity, biosafety, and electrospinning-based preservation studies—highlighting both cross-cultural collaboration and technological advancement.

  3. Peer-Reviewed Nanotech Publications
    Dr. Gbabe has authored several relevant papers in reputed journals:

    • Journal of the Indian Chemical Society (2025): On hexanal nano-fiber matrices for tomato preservation.

    • IJETT (2025): Development of nano-fiber matrices for mango shelf-life extension.

    • Nano Plus (2023): On banana fruit preservation using electrospun nanotechnology.
      These works clearly demonstrate applied innovation, rigorous experimentation, and measurable societal impact in reducing food loss.

  4. Technical Skills Aligned with Nanotech Innovation
    Proficient in electrospinning, FTIR, GC-MS, SEM & TEM, and statistical software (R, SPSS), showing an interdisciplinary approach involving both materials science and food technology.

  5. Leadership in National Innovation Projects
    As a Senior Research Officer at the Nigerian Stored Products Research Institute, he actively leads R&D on postharvest loss reduction and food quality enhancement technologies—bridging innovation with policy and field deployment.

🎓 Education 

Dr. Gbabe holds a Master of Engineering in Agricultural and Environmental Engineering (2019) from the University of Agriculture, Makurdi, Nigeria. His thesis focused on eco-building materials using rice husk and sawdust, reflecting an early interest in sustainable engineering. He is currently completing his Ph.D. in Food Processing and Technology (2020–2025) at Benue State University, Makurdi. His doctoral research is centered on the development of electrospun hexanal nano-fibre matrices aimed at extending the shelf-life of tropical fruits like bananas, mangoes, and tomatoes. He is a registered engineer with COREN Nigeria and a member of the Nigerian Institution of Agricultural Engineers. In 2023, he was a research intern at the Centre for Agricultural Nanotechnology, TNAU, India, where he gained hands-on experience in nanotoxicology, electrospinning, and biosafety. His academic journey reflects a strong foundation in multidisciplinary innovation and food systems sustainability.

🔬 Research Focus on Technology Scientists Innovations

Dr. Gbabe’s research is rooted in postharvest technology, agricultural nanotechnology, and food quality preservation. His core contributions lie in the design and development of nanostructured packaging and preservation systems using biodegradable hexanal-based nano-fibers, created via electrospinning. These innovations target tropical fruit shelf-life extension and nutrient retention during storage. He is equally involved in evaluating postharvest handling systems, including the construction of solar dryers and inert-atmosphere silos. His projects align closely with SDG 2 (Zero Hunger) and SDG 12 (Sustainable Consumption & Production). Dr. Gbabe also explores sustainable materials (like rice husk-based eco-panels), biosafety assessments in nanoformulations, and pest management using botanicals. His work is highly applied, integrating field deployment, engineering fabrication, and local capacity building—benefiting smallholder farmers and food industries across West Africa.

📚 Publication Top Notes

  1. Gbabe et al. (2025)
    Effect of Hexanal Nano-fiber Matrix on Quality Parameters of Tomato Fruits during Storage
    Journal: Journal of the Indian Chemical Society
    Summary: Demonstrates improved shelf-life and reduced spoilage in tomato fruits using hexanal-loaded nano-fiber packaging developed via electrospinning.
    DOI: 10.1016/j.jics.2025.101912

  2. Gbabe et al. (2025)
    Development of Novel Hexanal Nano-fibre Matrix by Electrospinning for Shelf-life Extension of Mango Fruits
    Journal: International Journal of Engineering Trends and Technology
    Summary: Describes the fabrication and optimization of mango-preserving nano-matrices, with a focus on temperature resilience and biodegradability.
    DOI: 10.14445/22315381/IJETT-V73I3P132

  3. Chukwu et al. (2025)
    Implication of Different Storage Techniques on Physical Attributes of African Okra
    Journal: IJABR
    Summary: Assesses how traditional vs. improved storage impacts okra firmness, color, and moisture, with relevance to rural postharvest systems.

  4. Idris et al. (2024)
    Maize grains milling efficiency: A performance analysis of a hammer mill
    Journal: International Journal of Agronomy and Agricultural Research
    Summary: Compares efficiency metrics of hammer mills to suggest design improvements for rural grain processing.
    Link

  5. Adeniyi et al. (2024)
    Insecticidal and Toxicity Studies of Heliotropium Indicum Leaf Extracts
    Journal: Journal of Exposure Toxicology
    Summary: Investigates natural pest control agents for stored grain insects—highlighting bio-safety and efficacy.

  6. Oyewole et al. (2020)
    Commercial Utilization of Inert Atmosphere Silo for Maize Storage
    Journal: IOP Conf. Series: Earth and Environmental Science
    Summary: Presents the benefits of modified atmosphere storage in reducing maize spoilage.

Conclusion

Dr. Kwaghgba Elijah Gbabe is highly suitable for the Research for Nanotechnology Innovation Award. His work represents a strong blend of scientific depth, practical relevance, and innovation in nanotechnology applications for agriculture and food preservation. With further strides in international publication, commercialization, and cross-sectoral collaborations, Dr. Gbabe has the potential to become a leading figure in agricultural nanotech innovation across Africa and globally.

Valeria Cera | AI applied to Architectural Heritage | Women Researcher Award

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Dr. Valeria Cera | AI applied to Architectural Heritage | Women Researcher Award

Tenure-Track Assistant Professor at University of Naples Federico II, Italy.

Dr. Valeria Cera is a Tenure-Track Assistant Professor at the Department of Architecture, University of Naples Federico II. With a Ph.D. in Surveying and Representation of Architecture and Environment, she contributes extensively to heritage digitization, urban survey, and AI-based semantic modeling. A founding member of REAACH and collaborator with institutions such as CNRS (France), University of Tianjin (China), and University of Valladolid (Spain), her international research fosters digital transitions in heritage studies. She teaches Architectural Drawing and Surveying across multiple academic levels and contributes to the editorial and scientific boards of key journals and book series. She holds memberships in ICOMOS, UID, and the Europeana Network. Recognized for her role in blending cultural heritage with digital technologies, she has authored over 70 publications and led 30+ research projects.

Author Profile

Strengths for the Award

Innovative Expertise in Digital Cultural Heritage
Dr. Valeria Cera is a leading scholar in the field of architectural documentation, semantic 3D modeling, and digital representation of heritage assets. Her research integrates Scan-to-BIM, semantic annotation, and AI-based tools to enhance the documentation, analysis, and conservation of historical and urban environments. Her work stands at the intersection of technology and humanities, where she uses computational innovation to preserve and promote cultural identity.

Strong Academic and Editorial Credentials
Dr. Cera holds a Ph.D. in Surveying and Representation of Architecture and Environment and currently serves as a tenure-track Assistant Professor at the University of Naples Federico II. She has published over 70 journal and Award papers (many in Scopus-indexed venues) and contributed as a reviewer and editorial board member to journals such as MDPI’s Remote Sensing and Sustainability, DisegnareCon, and the International Journal of Computational Methods in Heritage Science.

Project Leadership and Global Collaborations
She has contributed to over 30 research projects, including international efforts with the University of Valladolid (Spain), University of Tianjin (China), CNRS (France), and regional cultural heritage bodies in Italy. These collaborations highlight her global outlook and commitment to impactful, interdisciplinary research in heritage science.

Institutional and Professional Engagement
Dr. Cera plays a pivotal role in academia through her teaching in Bachelor’s, Master’s, and advanced restoration programs. She is a founding member of REAACH (Representation Advances and Challenges APS), and an active member of respected professional organizations such as ICOMOS, UID, ENA Europeana Network, and IBIMI Building Smart. Her cross-sectoral influence spans academia, policy, and cultural institutions.

🎓 Education 

Dr. Cera earned her Ph.D. in Surveying and Representation of Architecture and Environment from the University of Naples Federico II. Her advanced studies integrated architectural geometry, photogrammetry, and computational modeling. During her doctoral work, she explored emerging methods for spatial data capture and semantic 3D modeling, laying the foundation for her later work on Scan-to-BIM systems and H-BIM processes. She has continuously built upon her educational background through academic teaching and applied research in heritage documentation, visualization, and urban modeling. In 2020, she was awarded National Scientific Qualification as Associate Professor, underscoring her scholarly contributions and academic leadership.

🔍 Research Focus on AI applied to Architectural Heritage

Dr. Valeria Cera’s research is situated at the intersection of digital cultural heritage, semantic 3D modeling, and human-centered interface design. Her work advances the representation and conservation of historic architecture through techniques such as Scan-to-BIM, natural user interfaces, and semantic annotation. With a strong foundation in survey science, her research extends to multi-sensor fusion, low-cost documentation systems, and real-time AR/AI-based monitoring. She also investigates gamification and immersive technologies to enhance public engagement with built heritage. Her aim is to optimize processes for heritage analysis, documentation, and communication—making use of digital twins and intelligent systems that preserve cultural identity in an accessible way.

📚Publication Top Notes

🔬 1. Semantically Annotated 3D Material Supporting the Design of Natural User Interfaces for Architectural Heritage

Authors: V. Cera, A. Origlia, F. Cutugno, M. Campi
Conference: AVI*CH (Advanced Visual Interfaces for Cultural Heritage), 2018
Citations: 13
Summary:
This work proposes a method for enriching 3D architectural models with semantic data, enabling interaction through natural user interfaces (NUIs). Targeted at non-experts—tourists, students, or citizens—it enables intuitive exploration of architectural data through gestures and voice. The study also integrates linguistic linked open data with spatial datasets, creating a hybrid model that bridges computational linguistics, 3D graphics, and cultural storytelling.

📡 2. Evaluating the Potential of Imaging Rover for Automatic Point Cloud Generation

Authors: V. Cera, M. Campi
Journal: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017
Citations: 11
Summary:
The authors develop a low-cost mobile rover system equipped with photogrammetric sensors for autonomous data acquisition in complex heritage sites. It evaluates imaging geometry, stability, and point cloud density to determine accuracy. The paper advances field-based survey methodologies where traditional tools are infeasible, especially in confined or fragile environments.

🧱 3. Segmentation Protocols in the Digital Twins of Monumental Heritage: A Methodological Development

Authors: V. Cera, M. Campi
Journal: DisegnareCon, 2021
Citations: 9
Summary:
This paper introduces standardized segmentation protocols for processing 3D scans of monumental architecture. These protocols improve the quality and interpretability of digital twins used in restoration, conservation, and analysis. The methodology addresses semantic and geometric partitioning in HBIM models, providing a repeatable workflow for complex heritage assets.

🏛️ 4. Knowledge and Valorization of Historical Sites through Low-Cost, Gaming Sensors and H-BIM Models: The Case of Liternum

Author: V. Cera
Journal: Archeologia e Calcolatori, 2017
Citations: 8
Summary:
Using Microsoft Kinect and similar gaming sensors, this study constructs cost-effective 3D reconstructions of the ancient Roman town of Liternum. The paper presents an H-BIM model that integrates historical layers, semantic annotation, and interactive visualization. It contributes to democratizing heritage access and documentation, especially for small-scale or underfunded archaeological projects.

🏙️ 5. Fast Survey Procedures in Urban Scenarios: Some Tests with 360° Cameras

Authors: V. Cera, M. Campi
Journal: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022
Citations: 6
Summary:
The authors assess 360° panoramic cameras as tools for rapid urban data acquisition. Through field trials, they compare image quality, georeferencing accuracy, and integration with BIM workflows. This technique offers fast, scalable solutions for documenting complex urban heritage, particularly in dynamic or inaccessible environments.

Conclusion

Dr. Valeria Cera is highly deserving of the Women Researcher Award. Her pioneering contributions to digital modeling, semantic systems, and architectural heritage documentation exemplify excellence in research and interdisciplinary collaboration. Her work has advanced both academic knowledge and public policy approaches to cultural preservation. With her ongoing research momentum and leadership roles, she is well-positioned to shape the future of digital heritage science, making her an outstanding representative for women in science and technology.

Nuttapat Jittratorn | Renewable Energy | Best Researcher Award

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Mr. Nuttapat Jittratorn | Renewable Energy | Best Researcher Award

Ph.D. Candidate in Electrical Engineering, National Cheng Kung University, Taiwan.

Nuttapat Jittratorn is a passionate Ph.D. candidate in Electrical Engineering at National Cheng Kung University, Taiwan. With a deep-rooted commitment to renewable energy innovation, he has led over 10 collaborative projects across Taiwan and Japan, applying AI to enhance energy forecasting systems. His academic and industrial experience spans solar PV, wind power, and hybrid energy systems. Nuttapat’s interdisciplinary expertise merges machine learning with real-time deployment, helping industries such as TSMC and Delta Electronics optimize energy use. Recognized with the Best Oral Presentation Award at the 2025 IEEE IAS Annual Meeting, he also contributes to academic leadership as a session chair and student mentor. A forward-thinking researcher fluent in English and Thai, he continues to bridge research with sustainable industrial solutions.

🧾Author Profile

🎓 Education

Nuttapat Jittratorn began his academic journey at Kasetsart University, Thailand, earning a Bachelor of Engineering in Electrical Engineering (2014–2018). He then pursued his Master’s degree at National Chung Cheng University in Taiwan, where he deepened his focus on renewable energy systems and intelligent computation (2018–2021). Currently, he is a Ph.D. candidate in Electrical Engineering at National Cheng Kung University, Taiwan (2021–present). His doctoral research centers on enhancing the reliability and accuracy of energy forecasting using artificial intelligence. Throughout his studies, Nuttapat has maintained a strong interdisciplinary approach, integrating engineering principles with emerging technologies like deep learning and hybrid modeling. His academic path reflects a consistent commitment to solving global energy challenges through intelligent system design and applied machine learning in energy grids.

💼 Experience 

Since 2021, Nuttapat has played pivotal roles as Team Leader, Project Advisor, and Researcher across Taiwan and Japan. He has collaborated with leading institutions and corporations such as TSMC, Delta Electronics, FarEasTone Telecom, and the National Science and Technology Council. His work involves real-time AI-powered forecasting systems for solar, wind, and multi-load applications in power and steam. Nuttapat has led the development and deployment of models in real-world industrial settings, optimizing power generation and usage. As a Thesis Advisor at Ton Duc Thang University (2022–2023), he mentored students in AI-energy research and thesis defense preparation. His projects span Changhua, Hsinchu, Tainan, Taoyuan, and Kagoshima, showcasing his ability to drive innovation in dynamic, multinational environments.

🏅 Honors & Awards 

Nuttapat Jittratorn was awarded the Best Oral Presentation Award in the Renewable and Sustainable Energy Conversion track at the 2025 IEEE IAS Annual Meeting, recognizing his research impact in intelligent PV and wind power forecasting. Additionally, he served as the Session Chair at the same Award, a testament to his leadership and recognition in the energy research community. His collaborative research and advisory roles in academia and industry have positioned him as a standout researcher in applied energy systems. These achievements underscore his ability to produce not just high-quality publications, but also real-world, industry-transforming outcomes that align with global sustainability goals.

🔬 Research Focus 

Nuttapat’s research is centered on AI-based renewable energy forecasting. He develops intelligent models for very short-term and short-term prediction of solar PV and wind power generation. His focus includes hybrid techniques that combine LSTM, Markov models, and probabilistic correction based on environmental data like wind speed. He also explores energy storage integration, such as BESS (Battery Energy Storage Systems), to enhance operational efficiency. His work bridges data science and engineering, ensuring models are not only accurate in labs but also viable for real-world deployment in industrial energy management. His interdisciplinary projects support Taiwan and Japan’s energy industries in transitioning toward smarter and more reliable grid systems. His research is forward-looking, contributing directly to the goals of a low-carbon economy and sustainable industrial operations.

Publication Top Notes

1. A Hybrid Method for Hour-Ahead PV Output Forecast with Historical Data Clustering

Authors: N. Jittratorn, G.W. Chang, G.Y. Li
Conference: 2022 IET International Conference on Engineering Technologies
Citations: 4
Summary: This paper proposes a clustering-based hybrid model for predicting hour-ahead PV output. Historical meteorological data are clustered to create more accurate baseline patterns, improving forecast accuracy. The model has industrial applications for solar plant operation scheduling.

2. Very Short-Term Wind Power Forecasting Using a Hybrid LSTM-Markov Model Based on Corrected Wind Speed

Authors: A.N. Jittratorn, B.C.M. Huang, C.H.T. Yang
Journal: Renewable Energy and Power Quality Journal, Vol. 21, pp. 433–438
Year: 2023 | Citations: 2
Summary: A hybrid forecasting framework combining LSTM and a Markov decision structure, this study corrects input wind speed for improving wind power forecasts within minutes to hours. Effective for wind turbine operational control and energy market participation.

3. A Deterministic and Probabilistic Framework Based on Corrected Wind Speed to Improve Short-Term Wind Power Forecasting Accuracy

Authors: N. Jittratorn, C.M. Huang, H.T. Yang
Journal: International Journal of Electrical Power & Energy Systems, Vol. 170, 110859
Year: 2025
Summary: This journal article presents an advanced dual-framework model integrating deterministic forecasts with probabilistic corrections, improving reliability in fluctuating wind environments. It’s particularly useful for risk-aware grid management and dispatch.

4. Short-Term Forecasting of Wind Power Plant Generation Based on Machine Learning Models

Authors: M.N. Phan, K.P. Nguyen, V. Van Huynh, C.M. Huang, H.T. Yang, N. Jittratorn, et al.
Conference: 2025 IEEE 1st International Conference on Smart and Sustainable Developments
Year: 2025
Summary: Collaborative paper exploring various machine learning models for short-term wind forecasting. Nuttapat contributed to model selection, tuning, and integration with real-time plant data.

5. PV Power Forecasting for Operation of BESS Integrated with a PV Generation Plant

Authors: N. Jittratorn, C.S. Liu, C.M. Huang, H.T. Yang
Conference: 2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA)
Year: 2024
Summary: Proposes a new forecasting model to manage PV+BESS operation, ensuring optimal battery use while minimizing forecast error. Critical for smart energy storage deployment in renewable infrastructure.

🏅 Conclusion

Nuttapat Jittratorn is a highly promising early-career researcher with solid technical, academic, and leadership credentials. His contributions to AI-driven energy forecasting and integration with industrial applications stand out. While still in the Ph.D. phase, his research maturity, real-world impact, and academic service position him as a strong candidate for the Best Researcher Award, particularly in the applied energy systems or smart grid technologies domain.

Xiangyu Zhang | Public Health | Best Researcher Award

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Mr. Xiangyu Zhang | Public Health | Best Researcher Award

Doctoral Researcher, CAS Institute of Automation, China

Dr. Xiangyu Zhang is a doctoral researcher at the Institute of Automation, Chinese Academy of Sciences (CASIA). With a strong foundation in mechanical engineering and robotics, Dr. Zhang transitioned into the realm of social computing and artificial intelligence. His research addresses real-world crises by developing cognitive AI agents and multi-agent systems for public health emergency response. His collaborative work with the Chinese CDC and the Beijing CDC has resulted in intelligent models that predict disease outbreaks and optimize response strategies using advanced machine learning and large language models. His forward-thinking research contributes significantly to the growing intersection between AI and epidemiology, aiming to build resilient, data-driven health systems. A rising voice in the AI and public health community, Dr. Zhang continues to lead impactful research published in leading journals and presented at top international Awards.

Author’s Profile

🎓 Education

Dr. Zhang’s educational journey exemplifies his interdisciplinary approach. He is currently pursuing his Ph.D. in Social Computing at CASIA under the University of Chinese Academy of Sciences. His research integrates AI, epidemiology, and intelligent systems at the State Key Laboratory of Multimodal Artificial Intelligence Systems. Prior to this, he earned his M.Sc. in Mechanical Engineering from the University of Electronic Science and Technology of China (UESTC), where he specialized in biomimetic actuators in robotics. His undergraduate studies in Mechanical Design (B.Eng., UESTC) laid the foundation for his engineering acumen, particularly in robotic arm systems and control mechanisms. This unique blend of mechanical design and cognitive AI enables him to craft deeply technical yet socially responsive research, blending physical systems with computational intelligence to solve contemporary global health challenges.

🏢 Experience

Dr. Zhang’s research experience spans across both engineering innovation and public health intelligence. As a Ph.D. researcher at CASIA, he is engaged in groundbreaking work on cognitive agent-based systems for pandemic response and crisis management. He has worked extensively with high-stakes research projects funded by the National Natural Science Foundation of China and the Next-Generation AI Development Plan (2015–2030). His key contributions include developing simulation models for outbreak prediction and adaptive intervention frameworks using LLMs and multi-agent systems. He has collaborated directly with the Chinese CDC and Beijing CDC, grounding his work in critical, real-world public health needs. His previous research in robotics labs focused on elastic actuators and autonomous learning systems, bringing an engineering lens to his AI-driven innovations. Despite his early career stage, his experience is already making waves in intelligent public health system design.

🔬 Research Focus

Dr. Zhang’s research centers on the development of intelligent decision-support systems for public health emergencies. His primary focus lies in the integration of multi-agent systems, epidemiological modeling, and large language models (LLMs) to create dynamic, real-time frameworks for disease surveillance, prediction, and intervention. He has proposed agent-based simulation architectures that replicate human-like decision-making in health crisis contexts, enabling systems to not only forecast disease spread but also adaptively respond with optimal strategies. His novel work in spatiotemporal forecasting for urban epidemics (IEEE ISI 2025) and cognitive frameworks for crisis modeling (Frontiers of Engineering Management) positions him as a forward-thinking researcher in AI for social good. His contributions are pioneering the use of AI to enhance epidemiological intelligence and resilience against future pandemics—an area of urgent global need.

📚 Publication Top Notes

  1. Agent-Based Modeling of Epidemics: Approaches, Applications, and Future Directions
    Technologies, 2025, 13, 272.
    This paper reviews the current methodologies and advancements in agent-based modeling (ABM) for epidemics. Dr. Zhang explores how ABMs simulate human behaviors and interactions to better understand disease spread and the effectiveness of policy interventions.

  2. Large Language Models: Technology, Intelligence, and Thought 
    Frontiers of Engineering Management
    Co-authored with Z. Cao and D. Zeng, this paper examines the philosophical and functional implications of large language models (LLMs) in understanding intelligence and cognition, particularly in the context of public service applications.

  3. LLM-Driven Spatiotemporal Forecasting of Urban Infectious Diseases
    IEEE ISI 2025 Conference
    A real-world case study on Haidian District, this work presents a cutting-edge LLM-integrated system for forecasting infection patterns, enabling early interventions based on data-driven spatial and temporal analysis.

  4. ShadowPainter: Robotic Painting via Active Learning
    Journal of Intelligent & Robotic Systems, 2022, 105(3), 61
    This research introduces an AI-powered robotic system capable of learning artistic techniques through visual replication. While outside public health, it showcases Dr. Zhang’s skills in human-like machine learning.

  5. Novel Multi-Configuration Elastic Actuator
    Advanced Intelligent Systems, 2024, 6(10): 2400079
    This paper introduces an elastic actuator capable of energy modulation, relevant for dynamic robotic systems. It highlights Dr. Zhang’s prior contributions in robotics and mechanical innovation.

Conclusion 

Dr. Xiangyu Zhang presents a strong and promising research portfolio marked by high-impact publications, innovative interdisciplinary work, and societal relevance in AI-driven public health crisis management. While still early in his career, his contributions clearly demonstrate leadership potential and research excellence, especially in emerging fields.

Fazilet Gokbudak | Sustainable Tech Solutions | Best Researcher Award

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Dr. Fazilet Gokbudak | Sustainable Tech Solutions | Best Researcher Award

ML Researcher, Apple, United Kingdom.

Fazilet Gokbudak is a machine learning researcher at Apple, specializing in generative models, computational photography, and inverse rendering. She received her PhD in Computer Science from the University of Cambridge in 2024, where she worked on neural rendering and efficient image manipulation techniques. Her academic path began at Bogazici University with a high honors degree in Electrical and Electronics Engineering, followed by an MSc with distinction from the University of Edinburgh. Her industrial journey includes pivotal contributions at Amazon as an Applied Scientist Intern. Fazilet actively promotes diversity in AI as a co-chair at Women@CL and reviewer for prestigious AI Awards. Her impactful work has been recognized through awards, publications in top-tier Awards like ECCV and SIGGRAPH, and cutting-edge research contributing to sustainable visual technologies.

Author’s Profile

Education 

Fazilet Gokbudak pursued her undergraduate studies in Electrical and Electronics Engineering at Bogazici University (2014–2018), graduating with High Honors and multiple scholarships. She then completed her MSc in Signal Processing and Communications at the University of Edinburgh in 2019, earning a distinction. Between 2020 and 2024, she conducted her PhD research in Computer Science at the University of Cambridge under full funding from the Computer Laboratory Studentship. Her doctoral work focused on generative neural techniques for photorealistic appearance manipulation and inverse rendering. Fazilet’s academic training spanned signal processing, computer vision, and machine learning. Throughout her education, she demonstrated a consistent commitment to innovation, excellence, and sustainability in computational research, reflected by her active participation in research projects and her growing publication record.

Experience

Fazilet currently serves as an ML Researcher at Apple (since October 2024), where she develops advanced camera algorithms to enhance mobile photography. Previously, she interned at Amazon (July–November 2022), working on high-fidelity conditional image generation using GANs with local histogram losses for skin tone realism. She also worked as a part-time Research Assistant at the University of Cambridge (Nov 2020–Jan 2022), where she led the Cambridge team on a joint blackgrass detection project using deep convolutional networks. Her work achieved 80% classification accuracy on novel agricultural datasets, showcasing practical impacts of AI in sustainable agriculture. Her career blends foundational machine learning research with real-world, production-level deployments.

Awards and Honors 

Fazilet has earned multiple accolades recognizing her academic excellence and research impact. At the University of Cambridge, she received the prestigious Computer Laboratory Studentship (2020–2024), which fully funded her doctoral studies. She also earned a Graduate Student Travel Award from Queens’ College in 2023 to attend SIGGRAPH, a top-tier graphics Award. At Bogazici University, she graduated with High Honors and received the Dean’s High Honor Certificate in 2018. She was also a recipient of the TEKFEN Holding Private Scholarship (2009–2018) for her outstanding performance in national exams, ranking 185 out of over two million candidates. Her high school career concluded as Valedictorian, earning Summa Cum Laude. These honors reflect her long-standing dedication to academic excellence and societal impact.

Research Focus 

Fazilet’s research spans several cutting-edge areas in machine learning and computer vision, with a core focus on generative models, neural rendering, and appearance manipulation. Her PhD work centered on data-efficient methods for realistic image generation, including patch-based transformations and BRDF modeling. She explores inverse rendering techniques that enable physically consistent reconstructions of visual scenes, contributing to advancements in sustainable graphics systems. At Apple, her focus includes developing intelligent algorithms for mobile camera systems, aligning technological performance with energy efficiency and visual realism. Her research also supports sustainable tech by optimizing neural computations and minimizing training overhead, critical for eco-conscious AI applications. She brings a unique blend of academic depth and practical innovation.

Publication Top Notes

Multispectral Fine-Grained Classification of Blackgrass in Wheat and Barley Crops  (2025, Computers and Electronics in Agriculture)

Co-authored with M. Darbyshire et al. This study develops a deep learning model using multispectral image data to accurately identify blackgrass weed species in cereal crops. Fazilet was responsible for model training and evaluation, contributing to a high-accuracy, fine-grained classification system. The work supports sustainable agriculture by enabling targeted herbicide use and reducing environmental impact.

Spatial Receptor Allocation for a Multiple Access Hub in Nanonetworks (2019, IEEE Transactions on Molecular, Biological and Multi-Scale Communications)

Fazilet’s early research explored theoretical models for receptor allocation in nanoscale communication networks. The study introduces simulation-based methods to optimize signal clarity and reduce cross-interference in molecular communication systems, marking a foundational step in nano-IoT frameworks.

Hypernetworks for Generalizable BRDF Representation (2024, European Conference on Computer Vision)

This paper presents a novel hypernetwork design that enables generalization across various materials when estimating Bidirectional Reflectance Distribution Functions (BRDFs). Fazilet contributed to network architecture and experimentation, demonstrating the model’s ability to capture complex material appearances using fewer parameters, facilitating efficient rendering in graphics pipelines.

Data-efficient Neural Appearance Manipulations (2025, in preparation)

This upcoming work proposes neural models for photo editing that require less training data and computational power. Fazilet introduces modular architectures that allow intuitive and efficient image edits, especially suited for low-resource devices. The approach balances realism and sustainability by minimizing hardware dependency.

Physically Based Neural BRDF (2024, arXiv preprint)

Fazilet co-authored this paper which merges physical reflectance models with neural networks to improve accuracy in appearance modeling. The technique enhances inverse rendering applications and supports high-fidelity visual reconstruction by incorporating physical consistency into the learning process.

One-shot Detail Retouching with Patch Space Neural Transformation Blending (2023, ACM SIGGRAPH CVMP)

The paper introduces a one-shot learning-based approach for image retouching that uses patch-space neural blending. Fazilet’s contributions include model design and testing, enabling fast, high-quality transformations from a single reference image—a technique ideal for low-data environments.

Patch Space Neural Field based Transformation Blending (2022, CoRR)

A precursor to her 2023 SIGGRAPH CVMP paper, this work investigates neural field-based image transformation using patch-level information. It showcases the potential for detail-preserving edits with minimal training samples, advancing low-data generative editing.

Conclusion

Fazilet Gokbudak is an exceptionally strong candidate for a Best Researcher Award in the fields of Machine Learning, Computer Vision, and Generative AI. Her multidisciplinary background, strong publication record, and industry-academic synergy position her as a next-generation leader in AI research.

Yoshitada Morikawa | Quantum Simulations | Best Researcher Award

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Prof. Dr Yoshitada Morikawa | Quantum Simulations | Best Researcher Award

Professor, The University of Osaka, Japan.

Professor Yoshitada Morikawa is a leading Japanese physicist and materials scientist specializing in quantum simulations. Born in Osaka in 1966, he currently serves as a Professor in the Department of Precision Engineering at Osaka University. With a rich academic journey spanning Kyoto University and the University of Tokyo, he has significantly contributed to computational physics, surface science, and AI-driven materials design. Professor Morikawa is known for combining quantum mechanics with machine learning to explore and optimize surface/interface phenomena, catalysis, and semiconductor behavior. His scholarly work includes over 218 peer-reviewed publications and a remarkable h-index of 49. His impact is further demonstrated through leadership roles in the Japan Society of Vacuum and Surface Science and the Physical Society of Japan. Widely respected for his visionary research and scientific leadership, Professor Morikawa is a strong advocate for a carbon-neutral society through fundamental science.

  📌Author’s Profile

🎓 Education 

Yoshitada Morikawa received his B.Sc. in Physics and Chemistry in 1989 and M.Sc. in Chemistry in 1991, both from Kyoto University. He then earned his Ph.D. in Physics in 1994 from the Institute for Solid State Physics, University of Tokyo. His education laid a robust foundation in theoretical and computational science, equipping him with the necessary tools to explore the intersections of quantum mechanics, chemistry, and material interfaces. During his doctoral studies, he held a prestigious Japan Society for the Promotion of Science (JSPS) Fellowship (DC), followed by a postdoctoral fellowship (PD) at Kyoto University. These early roles catalyzed his deep involvement in atomic-scale material analysis and first-principles simulations. Professor Morikawa’s academic path exemplifies a seamless integration of multi-disciplinary domains and a commitment to scientific rigor, establishing him as a globally recognized figure in quantum materials research and theory-driven computational modeling.

🧪 Experience 

Professor Morikawa’s career spans over three decades of distinguished service in academic and national research institutions. After his Ph.D., he joined the Joint Research Center for Atom Technology (JRCAT) and later served at the National Institute of Advanced Industrial Science and Technology (AIST). He held visiting positions at JAIST and the Technical University of Denmark. Since 2004, he has been with Osaka University, first as an Associate Professor at ISIR and then, from 2009, as a full Professor in the Graduate School of Engineering. He has supervised major projects involving surface physics, electrochemistry, and materials simulations. His leadership roles include serving as Vice President of the Japan Society of Vacuum and Surface Science and Representative of the Physical Society of Japan’s Division 9. Professor Morikawa’s vast experience in academic, industrial, and international contexts makes him a valuable leader and a mentor in materials science innovation.

🔬 Research Focus

Professor Morikawa’s research explores quantum mechanical simulations of surfaces and interfaces, targeting real-world problems in energy, catalysis, and semiconductor technology. His lab develops first-principles electronic structure methods integrated with molecular dynamics, Monte Carlo, and machine learning algorithms (including deep learning and Gaussian processes). The primary goal is to bridge the microscopic quantum world with macroscopic material properties. Applications range from designing efficient CO₂ conversion catalysts to improving fuel cell performance. His recent focus on AI-enhanced materials design supports the global drive toward a carbon-neutral society. By decoding physical origins of material behavior, he provides theoretical guidelines for improving functionality, efficiency, and sustainability. His comprehensive approach offers insights into both fundamental and applied materials science.

📚Publication Top Notes

1. Experimental and Theoretical Investigations on pH-Dependent Molecular Structure, Electronic Structure, and Absorption Spectra of Ruthenium(II) Complexes with Extended Ligand

Journal of Molecular Structure, November 2025
Contributors: Zi Ying Yeoh, Yoshitada Morikawa, Siow-Ping Tan, Mohammad B. Kassim, Siew San Tan
Summary: This work combines experimental spectroscopy and first-principles simulations to analyze how pH variation influences the molecular geometry and electronic structure of ruthenium(II) complexes. The study demonstrates that protonation states significantly affect the absorption spectra, providing insights into their electronic transitions and potential in sensing and catalytic applications.

2. VibIR-Parallel-Compute: Enhancing Vibration and Infrared Analysis in High-Performance Computing Environments

Journal of Open Source Software, April 15, 2025
Contributors: Kurt Irvin M. Rojas, Yoshitada Morikawa, Ikutaro Hamada
Summary: This publication presents a new open-source computational tool designed to improve the efficiency of vibrational and infrared spectral analysis in large-scale simulations. The tool utilizes parallel computing to accelerate data processing, enabling high-throughput simulations of complex molecular systems in quantum chemistry and materials research.

3. Stabilization of Oxygen Vacancy Ordering and Electrochemical-Proton-Insertion-and-Extraction-Induced Large Resistance Modulation in Strontium Iron Cobalt Oxides Sr(Fe,Co)Oₓ

Nature Communications, January 2, 2025
Contributors: Yosuke Isoda, Thanh Ngoc Pham, Ryotaro Aso, Shuri Nakamizo, Takuya Majima, Saburo Hosokawa, Kiyofumi Nitta, Yoshitada Morikawa, Yuichi Shimakawa, Daisuke Kan
Summary: This collaborative study investigates resistance changes in Sr(Fe,Co)Oₓ caused by reversible proton insertion and oxygen vacancy ordering. Using both experimental data and theoretical modeling, it uncovers mechanisms relevant to next-generation memory and switching devices based on complex oxides.

4. CO Hydrogenation Promoted by Oxygen Atoms Adsorbed onto Cu(100)

Journal of Physical Chemistry C, 2024
Contributors: K. Nagita, K. Kamiya, S. Nakanishi, Y. Hamamoto, Y. Morikawa
Summary: This research explores how the presence of adsorbed oxygen atoms on a copper (100) surface alters the catalytic pathway for carbon monoxide hydrogenation. The study combines surface science experiments and density functional theory to propose a more efficient CO-to-methanol conversion mechanism, relevant for sustainable fuel production.

5. Effect of Fluorine Substitution on the Electronic States and Conductance of CuPc on Cu(100)

Applied Surface Science, 2024
Contributors: H. Okuyama, S. Kuwayama, S. Hatta, T. Aruga, Y. Hamamoto, T. Shimada, I. Hamada, Y. Morikawa
Summary: This paper investigates the electronic behavior of copper phthalocyanine (CuPc) molecules modified with fluorine atoms when adsorbed on a Cu(100) surface. The study reveals how fluorine substitution modifies the molecule–metal interaction, enhancing electronic tunability for organic semiconductor and device engineering applications.

🏆 Conclusion 

Professor Yoshitada Morikawa is highly suitable for the Best Researcher Award, especially for awards that prioritize:

  • Long-term scholarly excellence,

  • Interdisciplinary research, and

  • Cutting-edge integration of AI with quantum materials science.

His career is marked by rigorous academic scholarship, leadership in the scientific community, and a forward-looking research agenda tackling environmental and energy-related grand challenges.