Omid Isfahani Alamdari

Omid Isfahani Alamdari holds a PhD in Computer Science from the University of Pisa in Italy. Prior to pursuing his PhD, he earned a Master of Science in Computer Engineering - Software with a focus on Distributed Systems from the Iran University of Science and Technology and a Bachelor of Science in Computer Engineering - Software from Urmia University, both of which are in Iran. After completing his PhD, Omid worked as a post-doctoral researcher for two and a half years, focusing on mobility data analytics and contributing to various projects funded by the European Union.

Omid’s research centres on mobility data analytics with an emphasis on big data and data-intensive techniques for processing massive trajectory datasets. His work includes developing efficient trajectory analysis methods and advanced indexing techniques for scalable data management.

One of his current projects explores sustainable mobility solutions, particularly in carpooling and electric vehicle adoption, by analyzing individual mobility networks. Another of his projects focuses on event prediction, combining user history with real-time data to develop explainable decision-making techniques. Omid’s additional research interests include time series analysis and the application of generative artificial intelligence (AI), especially in mobility and data analytics challenges.

Alongside his research, Omid is passionate about teaching, having delivered computer science and data analytics courses that bridge theoretical concepts and real-world applications.

Courses taught

Agile Software Development, Python for Data Analytics, SQL Databases, Data Warehousing and Visualization, Marketing Analytics, Data Analytics Case Study (1, 2, 3)

Areas of academic interest

Mobility data analytics (electric vehicles, applications in sustainable transportation), big data, machine learning, predictive modelling, generative AI

Areas of specialization

Trajectory and spatio-temporal data analysis and indexing, simulation of transportation and mobility (electric vehicles), graph embedding, in-memory data processing

Publications

1. Omid Isfahani Alamdari, Mirco Nanni, Agnese Bonavita. “On the pursuit of Graph Embedding Strategies for Individual Mobility Networks.”, IEEE BigData 2023.
2. Mirco Nanni, Omid Isfahani Alamdari, Agnese Bonavita, Paolo Cintia. “From fossil fuel to electricity: studying the impact of EVs on the daily mobility life of users.”, IEEE Transactions on Intelligent Transportation Systems, 2023.
3. Pierpaolo Resce, Lukas Vorwerk, Zhiwei Han, Giuliano Cornacchia, Omid Isfahani Alamdari, Mirco Nanni, Luca Pappalardo, Daniel Weimer, and Yuanting Liu. “Connected Vehicle Simulation Framework for Parking Occupancy Prediction (Demo Paper).”, SIGSPATIAL 2022.
4. Mirco Nanni, Riccardo Guidotti, Agnese Bonavita, Omid Isfahani Alamdari. "City indicators for geographical transfer learning: an application to crash prediction." Geoinformatica, 26 (2022) pp. 581–612.
5. Omid Isfahani Alamdari, Mirco Nanni, Roberto Trasarti and Dino Pedreschi. "Towards In-Memory Sub-Trajectory Similarity Search." EDBT/ICDT Workshops 2020.
6. Mohammad Reza Abbasifard, Hassan Naderi, Omid Isfahani Alamdari, “Efficient Indexing for Past and Current Position of Moving Objects on Road Networks”, IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 9 (2018), pp. 2789 - 2800.