Ali Forootani

Ali Forootani is a Research Associate in the Department of Bioenergy at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany. His work focuses on integrating machine learning with environmental and energy systems, including bioenergy optimization, stochastic control, and reinforcement learning applications.

Education:

• Ph.D. in Information Technology & Automatic Control (2018), University of Sannio, Italy
• M.Sc. in Electrical Engineering (2011), SBU, Tehran, Iran
• B.Sc. in Electrical Engineering (2008), Azad University, Iran

Research Interests: Ali's research spans machine learning, reinforcement learning, approximate dynamic programming, and their applications in environmental modeling, energy systems, and bioenergy. His work emphasizes practical stabilization techniques, system identification, and AI-driven tools for sustainability.

Publications: His recent work includes advancements in federated learning, climate-aware neural networks, and physics-informed models for partial differential equations. He has contributed to impactful projects like the BENOPT bioenergy optimization model and the EE-Monitor tool for renewable energy assessment.

Awards: IEEE Senior Member recognition highlights his contributions to control systems and machine learning. He has secured multiple postdoctoral fellowships across top institutions in Italy, Ireland, and Germany.

Advising & Grants: Leads the Man0EUvRE EU project on net-zero energy systems, focusing on AI-driven scenario generation and stakeholder engagement. Collaborates on projects like InPositiv exploring renewable energy's biodiversity benefits.

Labs & Teams: Active in the UFZ Bioenergy team, developing tools like the Bio-Eng-LMM AI chatbot and the GS-PINN framework for PDE parameter estimation.