Dr. Usharani Hareesh Govindarajan is an Assistant Professor of Information Management and Systems and the Founder of Technology Change, Informatics, and Forecasting (TCIF Lab) at the Business School, University of Shanghai for Science and Technology, Shanghai, China. He completed a Post-Doctoral Fellowship at the Antai College of Economics and Management, Shanghai Jiao Tong University, China, and received his PhD in Industrial Engineering from National Tsing Hua University, Taiwan. In 2024, Dr. Govindarajan was awarded the prestigious Japan Society for the Promotion of Science (JSPS) Invitational Fellowship for research on integrating machine learning with econometrics at the University of Tokyo. Dr. Hareesh’s research interests include technology management for international business, with a focus on intellectual property analytics, low-code tools, and open data-related frameworks for decision support. He is a member of several industrial-academic initiatives across China, India, and Europe, and has authored key research papers in leading international journals such as IEEE Transactions on Engineering Management, Complexity, IEEE Engineering Management Review, Advanced Engineering Informatics, and Computer & Security. Readers can follow updates and engage further on LinkedIn @Hareeshpillai.

Zhang Chuyi earned a Bachelor of Science (BS) in Information Management and Information Systems from the University of Shanghai for Science and Technology in 2024. He is currently pursuing a Master of Science (MS) in Management Science and Engineering and works as a research assistant at the Technology Change, Informatics, and Forecasting (TCIF) Lab. His research focuses primarily on emerging application trends in humanoid robotics, low-altitude technologies, digital twins, and their management.

Prof. Rakesh D. Raut is an Associate Professor of Operations and Supply Chain Management at the Indian Institute of Management, Mumbai, India. He holds a PhD in Operations and Supply Chain Management from NITIE, Mumbai, and a Post-Doctoral Fellowship from EPFL, Switzerland. He also earned an M.Tech in Mechanical Engineering and a BE in Production Engineering from Nagpur University. His research focuses on Industry 4.0, Cloud Computing Adoption, Big Data Analytics, Sustainable Supply Chain Management, and Talent Management. He is listed among the top 2% of scientists globally by Stanford University (2020, 2021,2023, 2024) and has led several high-impact projects. Prof. Raut has published extensively in leading journals such as RSER, JCLP, JEMA, and Annals of Operations Research.

Gagan Narang earned a Master of Science (MS) in Informatics from the University of Delhi in 2022 and is currently an All But Dissertation (ABD) PhD candidate in Information Engineering at the Dipartimento di Ingegneria dell'Informazione, Università Politecnica delle Marche in Ancona, Italy. His research focuses on the adoption of emerging technologies to promote sustainable development and transform governance systems. Currently, he is leveraging Artificial Intelligence for data-driven water resource management, aiming to advance sustainable resource utilization and enhance decision-making frameworks.

Dr. Alessandro Galdelli is currently an Assistant Professor at the Department of Information Engineering (DII) of Università Politecnica delle Marche. He earned his B.Sc. degree in Computer and Automation Engineering in July 2012 from Università Politecnica delle Marche and completed his M.Sc. degree in the same field at the same institution in July 2017. In May 2021, he earned his Ph.D. in Information Engineering with a thesis titled "Applied Artificial Intelligence for Precision Fishing: Identification and Classification of Fishing Activities."His research focuses on time-series processing and computer vision applied to remote sensing, utilizing satellite images and AIS data. He specializes in machine learning and deep learning techniques, with applications in areas such as Industry 4.0, precision fishing, groundwater level prediction, and object detection using edge computing. Over the years, he has worked extensively on advanced time-series data processing, applying both supervised and unsupervised classification methods.