Research engineer at Honda Research Institute Europe and PhD candidate in electronics engineering at Sapienza University of Rome, defending in October 2026. Specialised in deep reinforcement learning for sequential decision making under uncertainty, in continuous and discrete action spaces and with multi-objective, constrained and multi-agent formulations. Applied to battery dispatch and energy management systems in the PhD, and to multi-agent coordination under shared resource constraints at Honda. Earlier work in computer vision and robotics at EPFL, including deep stereo networks for 360-degree depth estimation and a real-time person detection and tracking pipeline deployed on a mobile robot.
Available from November 2026. LinkedIn · GitHub · Publications · Projects
Experience#
- Trained multi-agent deep reinforcement learning policies for electric vehicle charging under a shared power budget; reached a wider Pareto front on efficiency and fairness than the group’s evolutionary (genetic programming) schedulers.
- Replaced hand-tuned reward weights with an explicit fairness constraint in actor-critic policy gradient training, giving an interpretable and verifiable fairness guarantee between agents for trustworthy deployment.
- Built the simulation environment, cost model and evaluation pipeline for benchmarking against optimisation-based and evolutionary baselines under the group’s published protocol.
- Wrote a reinforcement learning environment and trained agents in it to simulate the long-term energy transition of the Netherlands at national scale.
- Set up multi-year investment and retrofitting choices under uncertainty as a sequential decision problem, covering renewable integration and system planning.
- Trained deep reinforcement learning agents for battery dispatch in a real-time energy management system (EMS) of a renewable energy community, in both continuous and discrete action spaces; achieved lower operating cost than forecasting-based and optimisation baselines.
- Reduced battery degradation at comparable operating cost by writing multi-objective rewards that trade the two against each other; published as a degradation-aware RL framework (IJCCI 2025) and an enhanced SAC algorithm (IJCNN 2025, IEEE).
- Built the simulation environment around an extended battery equivalent-circuit model, giving the battery management system (BMS) side of the EMS a physical basis (IJCNN 2024, IEEE).
- Compared seasonal and non-seasonal forecasting methods for energy demand time series in smart grids and selected the forecaster used by the EMS (IJCCI 2023).
- Modified the architecture of existing deep stereo networks so they work on unrectified 360-degree camera images, after first reproducing the published benchmark results on the original datasets.
- Built a data collection rig from a lidar and two 360-degree cameras, recorded a new dataset, debugged the lidar extraction, and released the cleaned dataset in a standard format for the group.
- Ran a person detection and tracking pipeline on a Loomo mobile robot, with all inference on the onboard computer, so the robot followed a chosen person in real time.
Education#
- Thesis: Smart Control of Renewable Energy
- Thesis: Depth Estimation with Stereo 360-Degree Cameras (EPFL VITA Lab) · project page
Skills#
Reinforcement learning: deep RL in continuous and discrete action spaces, multi-objective and constrained RL, multi-agent RL, actor-critic and policy gradient methods, reward design, simulation environment design
Computer vision and perception: stereo depth estimation, 360-degree and omnidirectional imaging, object detection and tracking, lidar and camera data collection and calibration, dataset construction and cleaning, real-time inference on embedded hardware
Machine learning: deep learning, time series forecasting, PyTorch, TensorFlow
Optimisation and control: multi-objective and constrained optimisation, linear and mixed-integer programming, modelling and simulation of dynamic systems
Energy systems: energy management systems, battery management systems, battery dispatch, degradation modelling, renewable energy communities, smart grids
Programming and tools: Python, C/C++, MATLAB/Simulink, Git, Linux, bash, ROS
Robotics and embedded: NVIDIA Jetson, Raspberry Pi, Arduino, FPGA digital design (VHDL)
Languages: English (fluent), Persian (native), Italian (A2), French (A2)
Publications#
- Game-Theoretic Fair Allocation in MPC-Based Management of Renewable Energy CommunitiesAccepted for publication, 2026
- Forward–Forward Learning for Imbalanced Tabular Predictive Maintenance on a Real-World Smart-Grid Fault DatasetManuscript, 2026
- Intelligent Energy Management for Renewable Energy Communities and Microgrids: Models, Algorithms, and Practical ConstraintsStochastic Planning and Modeling for Energy Systems, Elsevier, pp. 491–532, 2026
- Degradation-Aware Energy Management in Residential Microgrids: A Reinforcement Learning Framework17th International Joint Conference on Computational Intelligence (IJCCI 2025), Springer CCIS, pp. 538–557, 2025
- Multi-Objective Battery Dispatching using an Enhanced SAC AlgorithmInternational Joint Conference on Neural Networks (IJCNN 2025), IEEE, pp. 1–8, 2025
- Seasonality Effect Exploration for Energy Demand Forecasting in Smart GridsIJCCI 2023 extended version, Computational Intelligence, Studies in Computational Intelligence, Springer, pp. 211–223, 2025
- An Extended Battery Equivalent Circuit Model for an Energy Community Real Time EMSInternational Joint Conference on Neural Networks (IJCNN 2024), IEEE, pp. 1–9, 2024
- A Comparison Between Seasonal and Non-Seasonal Forecasting Techniques for Energy Demand Time Series in Smart Grids15th International Joint Conference on Computational Intelligence (IJCCI 2023), SciTePress, pp. 459–467, 2023
Full CV with contact details available on request via LinkedIn.