Doctoral Candidate at University of Cologne , Germany
A final-year Ph.D. candidate at the University of Cologne and IE Business School, this researcher specializes in practical operations management and data science. Their work focuses on developing intelligent decision support systems using agent-based simulation , machine learning , and deep reinforcement learning. With expertise in smart mobility , energy systems , and sustainability, they bridge real-world problems with cutting-edge technology. Fluent in Python and other tools, they actively teach ML to graduate students and collaborate across academia and industry. Passionate about climate solutions 🌱, they aim to innovate for a greener and smarter world.
Professional Profile
Google Scholar Profile
Education & Experience
Holding a Ph.D. (2025 exp.) in Information Systems & Operations Management from the University of Cologne and currently a visiting scholar at IE Business School, Madrid , they also earned M.Sc. and B.Sc. degrees in Iran in Industrial and Mechanical Engineering respectively. With roles as researcher, lecturer , and tutor, their journey spans Europe and Asia. They’ve worked on EU-level energy and mobility research projects, simulations for EV fleets , and optimization algorithms. Teaching advanced analytics, leading grants, and collaborating with cities like Berlin and Paris , they blend deep technical skills with real-world impact.
Professional Development
This candidate continuously enhances their skill set through hands-on research, collaborative grant writing , and academic publishing . They lead cutting-edge projects using Python, TensorFlow, PyTorch, and simulation tools like Simpy and Pyomo . Their development includes teaching graduate-level machine learning courses , engaging in high-impact conferences like ICIS and AAMAS , and working with institutions like EWI. Industry collaborations include EV charging systems and sustainable logistics . Their commitment to sustainability, innovation, and smart city solutions positions them as a future leader in technology-driven operations management.
Research Focus
Their research centers on smart mobility , energy systems , and climate-conscious technologies 🌱. They design agent-based simulations and deep learning models to manage shared autonomous fleets and EV charging. Key areas include dynamic fleet pricing, ride-hailing, digital twins of mobility networks , and predictive analytics for load scheduling. They bridge theory and application by leveraging real-world data from European cities . Using advanced optimization (GA, MPC, RL) and simulation, their work contributes to more sustainable urban ecosystems. Their core mission is to build data-driven, adaptive platforms for smarter, greener cities.
Awards & Honors
Recognized for academic excellence and innovation , they ranked in the top 5% during their M.Sc. and top 10% in their B.Sc. programs . They earned a competitive research scholarship from the Institute of Energy Economics at the University of Cologne and co-led multiple successful EU research grant proposals . Their work has been presented at top-tier conferences like ICIS, ECIS, and WITS . They’ve also made an impact through teaching awards and invitations to speak on sustainability in mobility and energy systems . Their excellence extends to both academia and industry collaborations.
Publication Top Notes
1.Ahadi, R., Ketter, W., Collins, J., & Daina, N. (2023).
“Cooperative Learning for Smart Charging of Shared Autonomous Vehicle Fleets.”
Transportation Science, 57(3), 613–630.
Summary: This study presents a cooperative learning framework for optimizing electric vehicle (EV) charging across shared autonomous vehicle fleets. The model integrates real-time learning with coordination strategies to improve efficiency, grid stability, and user satisfaction.
2.Khalilzadeh, M., Neghabi, H., & Ahadi, R. (2023).
“An Application of Approximate Dynamic Programming in Multi-Period Multi-Product Advertising Budgeting.”
Journal of Industrial & Management Optimization, 19(1).
Summary: This paper develops an approximate dynamic programming approach to optimize advertising budgets over time for multiple products. It accounts for intertemporal trade-offs and uncertain returns, showcasing the method’s superiority to static approaches.
3.Yazdi, L., Ahadi, R., & Rezaee, B. (2019).
“Optimal Electric Vehicle Charging Station Placing with Integration of Renewable Energy.”
15th Iran International Industrial Engineering Conference (IIIEC), 47–51.
Summary: This conference paper investigates optimal site selection for EV charging stations using a multi-objective model that includes renewable energy generation and urban demand forecasting.
Conclusion
R. Ahadi exemplifies the qualities of a future-leading scholar with impactful, sustainable, and innovative contributions to operations management and intelligent systems. Their work directly contributes to the global challenge of building greener, smarter urban ecosystems—making them highly deserving of the Best Researcher Award.