Bao Liu | Emerging Technologies | Best Researcher Award

Published on by Scientist Day

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

Published on by Scientist Day

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

Published on by Scientist Day

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

Published on by Scientist Day

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.