Shadi Shayan | Emerging Technologies | Best Researcher Award

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Dr. Shadi Shayan | Emerging Technologies | Best Researcher Award

Dr. Shadi Shayan | Adelaide University | Australia

Dr. Shadi Shayan’s research lies at the intersection of project management, smart technologies, and social sustainability, focusing on how large-scale urban innovation programs can effectively manage social risks and deliver equitable outcomes. His scholarly work explores the dynamic relationships between technological transformation, social response, and governance frameworks in smart city development. By integrating change management models with risk management processes, Dr. Shayan has developed innovative frameworks that address the socio-demographic dimensions of smart city transitions—bridging theory, practice, and policy. His doctoral research, “Integrating change models and risk management processes: A framework to manage social risks in smart city programs”, provides a comprehensive model for mitigating community resistance and fostering inclusive participation in technologically driven urban initiatives. Dr. Shayan’s publications in leading journals such as Smart Cities, Sustainable Cities and Society, and International Journal of Construction Management advance understanding of how social factors, demographic variables, and stakeholder perceptions influence the success of smart city and infrastructure projects. A consistent theme in his research is the application of systems thinking and socio-technical analysis to enhance decision-making in project and program management. He also examines the evolving roles of professionals, including engineers and quantity surveyors, in adapting to emerging technological and societal challenges. Dr. Shayan’s work has significant implications for both academia and industry. It informs policy frameworks for smart urban governance, supports organizational strategies for managing social risk, and contributes to sustainable and resilient infrastructure planning. Through interdisciplinary collaborations and active engagement with the Smart Cities Council Australia New Zealand, he connects research with real-world impact—helping shape cities that are not only technologically advanced but also socially responsible and inclusive.

Profiles: Google Scholar | LinkedIn

Featured Publications 

Shayan, S., & Kim, K. P. (2025). Social responses and change management strategies in smart city transitions: A socio-demographic perspective. Smart Cities, 8(6), 188. https://doi.org/10.3390/smartcities8060188

Shayan, S., & Kim, K. P. (2023). Understanding correlations between social risks and sociodemographic factors in smart city development. Sustainable Cities and Society, 89, 104320. https://doi.org/10.1016/j.scs.2022.104320

Shayan, S., & Kim, K. P. (2022). A conceptual framework to manage social risks for smart city development programs. In Resilient and responsible smart cities (pp. 77–86). Springer, Cham. https://doi.org/10.1007/978-3-030-98423-6_6

Shayan, S., Kim, K. P., Ma, T., & Nguyen, T. H. D. (2020). The first two decades of smart city research from a risk perspective. Sustainability, 12(21), 9280. https://doi.org/10.3390/su12219280

Shayan, S., & Kim, K. P., & Tam, V. (2019). Critical success factor analysis for effective risk management at the execution stage of a construction project. International Journal of Construction Management, 22(3), 379–386. https://doi.org/10.1080/15623599.2019.1624678

Yong Liu | Research Excellence | Innovative Research Award

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Assoc. Prof. Dr. Yong Liu | Research Excellence | Innovative Research Award

Assoc. Prof. Dr Yong Liu | Tianjin University | China

Dr. Yong Liu is an Associate Professor at the School of Electrical and Information Engineering, Tianjin University, China. With an extensive record of scholarly contributions, he has authored and co-authored over 100 technical papers, three books or book chapters, and more than 50 patents in electrical engineering. His research primarily focuses on ageing evaluation, defect diagnosis, and performance monitoring of outdoor insulators and power cables under complex atmospheric and electrical conditions. Dr. Liu’s pioneering work integrates advanced sensing technologies, signal processing, and artificial intelligence to improve the reliability and safety of high-voltage power transmission systems. His studies on leakage current characteristics, surface discharges, and magnetic field-based condition evaluation have significantly advanced diagnostic techniques for high-voltage alternating current (HVAC) and direct current (HVDC) equipment. Notable recent contributions include research on multi-scale leakage current feature extraction using graph neural networks, magnetic field feature analysis for power cable evaluation, and dynamic discharge behavior of ice-covered insulators under DC voltages. He has made substantial progress in understanding the electro-thermal and mechanical ageing mechanisms of polymeric insulators and cables, developing novel monitoring strategies and predictive models for insulation failure. His publications in leading journals such as IEEE Transactions on Dielectrics and Electrical Insulation, IEEE Access, Energies, and Polymers demonstrate his sustained excellence and influence in high-voltage engineering and applied materials science. As a member of the Chinese Society for Electrical Engineering (CSEE) and the Institute of Electrical and Electronics Engineers (IEEE), Dr. Liu actively contributes to international collaboration and academic exchange. His innovative research continues to support the development of smart grid technologies, advanced cable diagnostics, and climate-resilient power systems, positioning him as a leading scholar in electrical insulation and power engineering research.

Profile: Orcid

Featured Publications 

Liu, Y., Lin, M., Wei, H., Duan, X., Li, Z., & Fu, Q. (2025). Cable outer sheath defect identification using multi-scale leakage current features and graph neural networks. Energies, 18(21), 5687. https://doi.org/10.3390/en18215687

Liu, Y., Wang, M., Huang, Y., Han, T., & Du, B. (2024). Feature extraction of magnetic field for condition evaluation of HVAC power cable. IEEE Transactions on Dielectrics and Electrical Insulation. https://doi.org/10.1109/TDEI.2023.3342774

Liu, Y., Li, C., Yin, F., Du, B. X., & Farzaneh, M. (2024). Nonlinear traits of leakage current and dynamic actions of surface discharges on ice-covered insulators under DC voltages. IEEE Transactions on Dielectrics and Electrical Insulation. https://doi.org/10.1109/TDEI.2024.3373549

Liu, Y., Guo, Y., Wang, B., Li, Q., Gao, Q., & Wan, Y. (2024). Research on influencing factors and wind deflection warning of transmission lines based on meteorological prediction. Energies, 17(11), 2612. https://doi.org/10.3390/en17112612

Liu, Y., Xin, Y., Huang, Y., Du, B., Huang, X., & Su, J. (2024). Optimal design and development of magnetic field detection sensor for AC power cable. Sensors, 24(8), 2528. https://doi.org/10.3390/s24082528

Liu, Y., Xin, Y., Du, B., Huang, X., & Su, J. (2024, March 22). Optimal design and development of magnetic field detection sensor for AC power cable [Preprint]. Preprints. https://doi.org/10.20944/preprints202403.1352.v1

Guangqin Li | Innovation Impact | Best Researcher Award

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Prof. Guangqin Li | Innovation Impact | Best Researcher Award

Prof. Guangqin Li | Anhui University of Finance & Economics | China

Dr. Guangqin Li began his academic journey with a doctorate in Urban Economics and Management from Shanghai University of Finance and Economics and now serves as a professor and master’s supervisor at Anhui University of Finance & Economics. With deep research interests in the digital economy, industrial economy, environmental economy, and regional economy, he has established himself as a versatile scholar bridging theoretical insights and empirical applications. His bibliometric profile reflects 37 published documents, 1,847 citations from 1,765 citing documents, and an h-index of 18, underscoring his growing impact within the academic community. His publications span studies on the green transformation of industry, the interface of urban innovation and infrastructure investment, and the spatial dynamics of economic development. He has contributed extensively through consultancy projects and collaborations with industry and regional authorities and serves on editorial boards of key journals in his fields. His research output has helped inform policy discussions on how new infrastructure investment can catalyze innovation in cities, particularly by influencing talent agglomeration and economic clustering. Poised at the cutting edge of his disciplines, he continues to mentor rising scholars and engage in interdisciplinary work, positioning his future contributions to shape the intersection of digitalization, regional growth, and sustainability.

Profiles: Scopus | Orcid 

Featured Publications 

Zhang, B., & Li, G. (2025). New infrastructure special debt, agglomeration and urban innovation: Evidence from China. Economic Modelling.

Liu, W., & Li, G. (2025). Sci-tech finance and urban entrepreneurial activity: Evidence from China. Economic Change and Restructuring.

Li, G., & Niu, W. (2025). How does fintech promote urban innovation? Empirical evidence from China. Economic Change and Restructuring.

Fang, X., Liu, M., & Li, G. (2024). Can the green credit policy promote green innovation in enterprises? Empirical evidence from China. Technological and Economic Development of Economy.

Li, G., Jin, Y., & Gao, X. (2023). Digital transformation and pollution emission of enterprises: Evidence from China’s micro-enterprises. Energy Reports.

Kristine Hallisy | Innovation Impact | Best Researcher Award

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Assoc. Prof. Dr. Kristine Hallisy | Innovation Impact | Best Researcher Award

Assoc. Prof. Dr. Kristine Hallisy | University of WI-Madison | United States

Dr. Kristine Marie (Kristi) Hallisy, PT, DSc, is an Associate Professor (CHS) in the Department of Family Medicine and Community Health at the University of Wisconsin–Madison School of Medicine and Public Health. She plays a pivotal role in the Doctor of Physical Therapy Program, where she integrates clinical expertise with innovative teaching practices. Dr. Hallisy’s professional focus lies in advancing physical therapy education, promoting evidence-based practice, and fostering interprofessional collaboration. Her research interests include clinical education models, patient-centered care, and strategies for enhancing student learning and professional identity formation. With years of academic and clinical experience, she has contributed to shaping future healthcare professionals through mentorship and curriculum development. She is actively involved in institutional service and community outreach initiatives that strengthen health education and practice. Dr. Hallisy has presented her work at national and international conferences and has published in peer-reviewed journals related to physical therapy and health sciences education. She emphasizes compassionate, ethical, and holistic approaches to healthcare delivery. Her leadership reflects a deep commitment to improving health outcomes and educational quality. Known for her collaborative spirit, she bridges the gap between research and clinical practice. Dr. Hallisy continues to inspire excellence among students, educators, and practitioners alike.

Profile: Orcid

Featured Publications

Salihu, E. Y., Hallisy, K., Baidoo, S., Malta, J. S., Ferrill, C., Melgoza, F., Sandretto, R., Culotti, P. C., & Chewning, B. (2025). Feasibility and acceptability of a “Train the Leader” model for disseminating Tai Chi Prime with fidelity in African American/Black and Latinx communities: A pilot mixed-methods implementation study. Healthcare, 13(20), 2622. https://doi.org/10.3390/healthcare13202622

Bao Liu | Emerging Technologies | Best Researcher Award

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Dr. Bao Liu | Emerging Technologies | Best Researcher Award

Dr. Bao Liu | Xi’an University of Science and Technology | China

Dr. Liu Bao is an Associate Professor and Academic Leader in the field of Pattern Recognition and Intelligent Systems at the School of Electrical and Control Engineering, Xi’an University of Science and Technology, where he also serves as a Graduate Supervisor and Project-based Ph.D. Supervisor. He earned his doctorate in engineering from Xi’an Jiaotong University, completed postdoctoral research at Xi’an University of Science and Technology, and broadened his academic experience as a visiting fellow at Macquarie University in Australia. Recognized as a Senior Data Analyst by the Ministry of Industry and Information Technology of China, Dr. Liu is an active member of several national academic societies and professional committees. His research focuses on multi-source information fusion and intelligent technologies for coal fire disaster prevention and control, integrating advanced computational and automation techniques to address complex industrial challenges. Throughout his career, he has led diverse national, provincial, and industry-based research projects and contributed extensively to scientific publications and technological innovation through patents and software developments. As a committed educator and mentor, Dr. Liu has inspired students to excel in academic and professional pursuits and has been honored with multiple awards recognizing his dedication to teaching, research, and academic service.

Profile: Orcid

Featured Publications

Liu, B., Liu, Q., & Wu, Z. (2026, February). A novel robust Student’s t scale mixture distribution based Kalman filter. Signal Processing. https://doi.org/10.1016/j.sigpro.2025.110296

Liu, B., Wu, Z., & Liu, Q. (2025). Gaussian mixture model-based variational Bayesian approach for extended target tracking. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/TIM.2025.3565347

Liu, B., & Jiang, W. (2024). DFKD: Dynamic focused knowledge distillation approach for insulator defect detection. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/TIM.2024.3485446

Liu, B., & Jiang, W. (2024, December). LA-YOLO: Bidirectional adaptive feature fusion approach for small object detection of insulator self-explosion defects. IEEE Transactions on Power Delivery. https://doi.org/10.1109/TPWRD.2024.3467915

Liu, B., Zhou, N., & Wang, Z. (2024, December 27). DFI-YOLOv8 based defect detection method for fan blades. In Proceedings of the 2024 Conference on [Insert Conference Name]. https://doi.org/10.1145/3722405.3722437

Sayak Chatterjee | Research Excellence | Best Researcher Award

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Dr. Sayak Chatterjee | Research Excellence | Best Researcher Award

Dr. Sayak Chatterjee | University of Massachusetts | United States

Dr. Sayak Chatterjee is a distinguished Postdoctoral Research Scholar in the Department of Physics at the University of Massachusetts Amherst, USA, specializing in experimental high-energy nuclear and particle physics. His research focuses on precision measurements, detector development, and high-rate data acquisition systems for frontier experiments such as MOLLER at Jefferson Lab and CBM at FAIR, Germany. With advanced expertise in Gas Electron Multipliers (GEM), Cherenkov detectors, and GEANT4-based simulations, he has contributed significantly to detector innovation and performance optimization. Dr. Chatterjee has an impressive academic record, authoring 44 research documents with 179 citations and an h-index of 7, reflecting the impact of his contributions to detector physics. His scholarly excellence has been recognized through multiple international honors, including the Ernest Rutherford Best Researcher Award and the Young Research Grant at the Pisa Meeting on Advanced Detectors, Italy. Beyond research, he serves on editorial boards, reviews for leading journals, and actively mentors students, embodying excellence in both scientific innovation and academic leadership.

Profiles:  ORCID | Scopus | Google Scholar | LinkedIn

Featured Publications

Chatterjee, S. (2025). Characterization of Cherenkov detectors for the MOLLER experiment. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment.

Mandal, S., Chatterjee, S., Sen, A., Gope, S., Dhani, S., Hegde, A. C., … (2024). Investigation of the stability in the performance of triple GEM detectors for High Energy Physics experiments. Nuclear Instruments and Methods in Physics Research Section A.

Chatterjee, S., Sen, A., Das, S., & Biswas, S. (2023). Charging-up effect and uniformity study of a single mask triple GEM detector. Nuclear Instruments and Methods in Physics Research Section A.

Chatterjee, S., Sen, A., Das, S., & Biswas, S. (2023). Effect of relative humidity on the long-term operation of a single mask triple GEM chamber. Nuclear Instruments and Methods in Physics Research Section A.

Sen, A., Chatterjee, S., Das, S., & Biswas, S. (2023). Characterization of a new RPC prototype using conventional gas mixture. Nuclear Instruments and Methods in Physics Research Section A.

Raffaele Marotta | Industry Collaboration | Young Innovator Award

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Dr. Raffaele Marotta | Industry Collaboration | Young Innovator Award

University of Naples Federico II | Baker Hughes | Italy

Dr. Raffaele Marotta is an accomplished researcher in vehicle dynamics, control systems, and AI-driven estimation, with proven academic and industrial impact. He earned his Ph.D. in Industrial Engineering (Mechatronics) with honors from the University of Naples Federico II, focusing on AI-enhanced vehicle dynamics. His career includes key roles at the Italian National Research Council (CNR), TU Ilmenau, Tenneco, ZF Group, and currently Baker Hughes, where he leads the development of advanced control algorithms for sustainable energy systems. He has contributed significantly to the European OWHEEL project, developing active chassis control and virtual sensing strategies. His research integrates Kalman filtering, neural networks, reinforcement learning, and digital twins into practical solutions for automotive and energy applications. He has published 22 documents, with 83 citations across 42 sources and an h-index of 6, reflecting strong scientific visibility and influence. His works, published in IEEE and SAE journals, include pioneering studies on wheel displacement estimation, traction force prediction, and vehicle mass estimation. International collaborations across Italy, Germany, Belgium, and Lithuania highlight his global network and impact. Recognized by Nova Talent’s top  global talent network, he also mentors young engineers in STEM leadership programs. With his blend of theoretical innovation, experimental validation, and industrial application, Dr. Marotta stands out as a promising candidate for global research excellence awards.

Profile: Scopus | Google Scholar | Orcid

Featured Publications

“Multi-output physically analyzed neural network for the prediction of tire–road interaction forces”

“Deep learning for the estimation of the longitudinal slip ratio”

“Estimation of the tire-road interaction forces by using Pacejka’s formulas with combined slips and camber angles”

“Active control of camber and toe angles to improve vehicle ride comfort”

“Improvement of traction force estimation in cornering through neural network”

“Camber angle estimation based on physical modelling and artificial intelligence”

“Electric vehicle corner architecture: driving comfort evaluation using objective metrics”

“A PID-Based Active Control of Camber Angles for Vehicle Ride Comfort Improvement”

“A strain-based estimation of tire-road forces through a supervised learning approach”

“On the prediction of the sideslip angle using dynamic neural networks”

“Neural Network-Based Virtual Measurement of Road Vehicle Wheel Displacements”

“Enhancing Wheel Vertical Displacement Estimation in Road Vehicles Through Integration of Model-Based Estimator with Artificial Intelligence”

“On the measurement of unsprung mass displacement of road vehicles through a model-based virtual sensor”

“Model-Based Vehicle Mass Estimation for Enhanced Adaptive Cruise Control Performance”

Zahra Amini | Emerging Technologies | Best Researcher Award

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Assist. Prof. Dr. Zahra Amini | Emerging Technologies | Best Researcher Award

Assist. Prof. Dr. Zahra Amini | Sharif University of Technology | Iran

Dr. Zahra Amini is an Assistant Professor of Civil Engineering at Sharif University of Technology, specializing in intelligent transportation systems, sustainable mobility, and operations research for urban traffic management. She earned her Ph.D. in Civil and Environmental Engineering from the University of California, Berkeley, and has led impactful projects in Iran and the U.S., ranging from corridor management to urban traffic resilience. With several high-impact publications, awards, and ongoing research on data-driven and machine learning approaches to transportation systems, she is recognized for advancing reliable, sustainable, and intelligent urban mobility solutions.

Academic Profile 

Google Scholar

Education

Dr. Zahra Amini completed her higher education in Civil and Environmental Engineering at the University of California, Berkeley, where she advanced her expertise in transportation systems and sustainable infrastructure. Alongside her major studies, she pursued minors in Industrial Engineering, Operations Research, and City and Regional Planning, which provided her with a strong interdisciplinary foundation. Her doctoral thesis focused on data-driven approaches for developing robust signal plans in urban transportation networks, reflecting her commitment to integrating advanced analytical methods with practical engineering challenges. This academic background equipped her with the technical knowledge and research skills that now define her scholarly and professional contributions.

Professional Experience

Dr. Amini serves as an Assistant Professor in the Department of Civil Engineering at Sharif University of Technology, where she teaches both undergraduate and graduate courses. Her teaching portfolio includes systems engineering, traffic engineering, and advanced transportation analysis, complemented by the design of a specialized course in Intelligent Transportation Systems. In addition to her academic role, she manages several research and development projects aimed at improving urban transportation and logistics planning. She has also gained international experience through her work at California Partners for Advanced Transportation Technology, contributing to projects focused on corridor management and traffic system optimization.

Research Interests

Dr. Amini’s research is centered on intelligent transportation systems, sustainable mobility solutions, and operations research applications in transportation system design. She focuses on integrating data-driven and machine learning methods to analyze and improve urban traffic flow, evaluate network resilience, and design reliable transport strategies. Her work also explores the operational sustainability of urban traffic networks and the role of intelligent systems in shaping resilient cities. By bridging theoretical models with applied research, she contributes innovative solutions to the pressing challenges of modern transportation.

Awards and Honors

Dr. Amini has received recognition for her teaching and research contributions through multiple awards. She has been honored for designing innovative courses in Intelligent Transportation Systems and has secured research grants supporting the development of advanced mobility solutions. Her scholarly excellence has been acknowledged with distinctions such as best paper and best presentation awards at international conferences. Additionally, she has been awarded prestigious fellowships that supported her academic journey and research endeavors, reflecting her status as a promising leader in the field of transportation engineering.

Publications

Agent-Based Modeling for Sustainable Urban Passenger Vehicle Mobility: A Case of Tehran
Author: M.E. Doraki, A. Avami, M. Boroushaki, Z. Amini
Journal: Transportation Research Part D: Transport and Environment
Year: 2024

Optimizing offsets in signalized traffic networks: A case study
Author: Z. Amini, S. Coogan, C. Flores, A. Skabardonis, P. Varaiya
Journal: IEEE Conference on Control Technology and Applications (CCTA)
Year: 2018

The Impact of Network Indices Integration on Traffic Flow Imputation Accuracy: A Machine Learning Approach
Author:  S. Sabzekar, A. Roudbari, A. Dehghani, A. Safaeiestalkhzir, Z. Amini
Journal: IEEE Transactions on Intelligent Transportation Systems
Year: 2025

Using neural network for predicting hourly origin-destination matrices from trip data and environmental information
Author: E. Hassanzadeh, Z. Amini
Journal: Scientia Iranica
Year: 2024

Unsupervised learning for topological classification of transportation networks
Author: S. Sabzekar, M.R.V. Malakshah, Z. Amini
Journal: arXiv preprint –
Year: 2023

Data-Driven Approaches for Robust Signal Plans in Urban Transportation Networks
Author: Z. Amini
Journal:  University of California, Berkeley (Thesis)
Year: 2018

Spatial network-wide traffic flow imputation with graph neural network
Author: S. Sabzekar, R. Bahmani, M. Ghasemi, Z. Amini
Journal:  International Journal of Intelligent Transportation Systems Research
Year: 2025

Analyzing the impacts of gasoline price change on nationwide trip demand and drivers’ behavior using regression discontinuity design
Author: S. Saeidi, Z. Amini
Journal: Scientia Iranica
Year: 2024

Conclusion

Dr. Zahra Amini’s distinguished academic background, impactful research in intelligent and sustainable transportation systems, and recognized leadership in advancing innovative solutions firmly establish her as an outstanding candidate for the Best Researcher Award. Her dedication to bridging research, education, and real-world applications makes her not only deserving of this recognition but also a valuable contributor to the global scientific community.