Marco Zanotti | Data Science | Best Researcher Award

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Mr. Marco Zanotti | Data Science | Best Researcher Award

Mr. Marco Zanotti | University of Milan-Bicocca | Italy

Marco Zanotti is an accomplished Machine Learning Engineer and Data Scientist specializing in time series analysis, forecasting, anomaly detection, and econometrics. With extensive experience across sectors such as e-commerce fashion, tourism, aviation, and digital services, he has played a key role in designing and improving advanced forecasting systems that drive data-informed business decisions. He currently serves as a Data Scientist and Forecasting Specialist at Wanan Luxury (Rome, Italy), following previous positions at Blogmeter, T-Voice, and Uvet Amex GBT, where he contributed to predictive modeling, machine learning optimization, and process automation. In academia, Marco is an Adjunct Professor at the University of Milan and other leading Italian universities, where he teaches modern time series forecasting, machine learning, programming, and statistics at both Bachelor’s and Master’s levels. He holds a Ph.D. in Statistics from the University of Milano-Bicocca, a Post-graduate Diploma in Data Science for Economics, Business and Finance, and an M.Sc. in Economics and Finance from the University of Milan. Fluent in Italian, English, and French, Marco is proficient in R, Python, SQL, Git, Shiny, and Google Cloud Platform. A member of the International Institute of Forecasters, he is passionate about bridging the gap between academic research and industrial applications, mentoring young data scientists, and advancing the science of predictive analytics.

Profile: Orcid

Featured Publications

Zanotti, M. (2025, December). On the retraining frequency of global models in retail demand forecasting. Machine Learning with Applications. https://doi.org/10.1016/j.mlwa.2025.100769

Zanotti, M., & Mazzucchelli, L. (2021). dispositionEffect [Research protocol or software].

Saleem Ramadan | Data Science | Best Researcher Award

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Assoc. Prof. Dr. Saleem Ramadan | Data Science | Best Researcher Award

Assoc. Prof. Dr. Saleem Ramadan | Al Hussein Technical University | Jordan

Dr. Saleem Z. Ramadan is an accomplished Data Analyst and Business Analyst with a strong interdisciplinary background in industrial engineering, systems optimization, and data science. With academic and consulting experience across the U.S. and Jordan, he has applied data-driven decision-making, predictive analytics, and optimization modeling to complex problems in healthcare, manufacturing, and finance. Dr. Ramadan holds a Ph.D. in Systems Engineering from Ohio University and has served as Acting Chair and Associate Professor at Al Hussein Technical University, leading research and teaching initiatives integrating machine learning and operations analytics. He has developed impactful analytics solutions—ranging from hydroponic resource optimization and radiology workflow improvement to financial risk dashboards—using tools such as Python, SQL, Power BI, Tableau, and Minitab. His consulting work with Healthcare Operations & Performance Excellence (HOPE) led to measurable improvements in hospital performance through Six Sigma and process control techniques. A Certified Analytics Professional (CAP) and Microsoft Power BI Data Analyst Associate, Dr. Ramadan has authored 20 peer-reviewed publications, accumulating 236 citations from 230 documents with an h-index of 6. His recent works focus on machine learning–driven optimization, surgical scheduling prediction, and additive manufacturing parameter tuning. Dr. Ramadan’s combination of technical proficiency, academic leadership, and applied research impact uniquely positions him at the intersection of analytics innovation and business performance excellence.

Profiles:  Scopus | Google Scholar | LinkedIn

Featured Publications

Ramadan, S., Abushams, M., Al-Dahidi, S., & Odeh, I. (2025). A data-driven approach for predicting remaining intra-surgical time and enhancing operating room efficiency. Journal of Industrial Engineering and Management.

Ramadan, S., Abushams, M., Al-Dahidi, S., & Odeh, I. (2024). Optimizing tensile strength and energy consumption for FDM through mixed-integer nonlinear multi-objective optimization and design of experiments. Heliyon.