AI Engineer at Freda, building LLM-powered agents for compliance workflows. Previously Machine Learning Research Engineer at Embark Studios, fine-tuning language models for interactive video game characters. Previous experiences include Machine Learning Developer at Substorm AB and a Master’s Degree in Autonomous Systems at KTH. Accomplishments include publishing a journal research article based on my master’s thesis at SCANIA.
August 2025 - Present
March 2022 - July 2025
November 2020 - February 2022
Jun 2020 - June 2020
 2018-2020 Double Master Degree in Autonomous SystemsDescription:Double Master Degree between the University of Trento, for the first year, and the Royal Institute of Technology (KTH), for the second year, focused on Artificial Intelligence, Robotics and Entrepreneurship. Topics addressed include: machine learning, deep learning, reinforcement learning, robotic design, robotic path planning and start-up business development. Highest possible exit grade (110 with honors out of 110). | ||
 2018-2020 Bachelor's Degree in Information and Business Organization EngineeringDescription:Bachelor Degree focused on Computer Science, with topics like Object Oriented Programming (in C++), database design and query with SQL, networking, Javascript development and Python development. A part of my study plan was also focused on business organization: I studied agile techniques for software development, process management and business strategy. Highest possible exit grade (110 with honors out of 110) |
Predicting the future trajectories of pedestrians is a challenging problem that has a range of application, from crowd surveillance to autonomous driving. In literature, methods to approach pedestrian trajectory prediction have evolved, transitioning from physics-based models to data-driven models based on recurrent neural networks. In this work, we propose a new approach to pedestrian trajectory prediction, with the introduction of a novel 2D convolutional model. This new model outperforms recurrent models, and it achieves state-of-the-art results on the ETH and TrajNet datasets. We also present an effective system to represent pedestrian positions and powerful data augmentation techniques, such as the addition of Gaussian noise and the use of random rotations, which can be applied to any model. As an additional exploratory analysis, we present experimental results on the inclusion of occupancy methods to model social information, which empirically show that these methods are ineffective in capturing social interaction.
Implementation of a deep learning detection model to detect pedestrians in images taken at night from the NightOwls dataset. The model was implemented with Python and the Keras API for TensorFlow and trained on the Google Cloud Platform. This project was for the course of Scientific Reading and Writing at KTH.
Implementation of a Generative Adversarial Network (GAN) that generates faces with specified features. The network was trained on the CelebA dataset and was implemented using Pytorch. It was the project for the course of deep learning at the University of Trento.
Plan a path for a wheeled robot: given an image taken from the top of a robot in an environment with obstacles and a goal, the objective of the project was to plan a path to reach the goal, avoiding the obstacles. The project was written in C++ with the image processing part done with OpenCV. Project for the course of Laboratory of Applied Robotics at the University of Trento.
Project for the advanced deep learning course at KTH where the task was to choose a paper from NIPS 2019 and reproduce it. The chosen paper was about transfer learning: the objective was to train a network from another already trained network (the “teacher”) without any of the data the teacher network was trained with. The name of the paper is: “Zero-shot Knowledge Transfer via Adversarial Belief Matching”, it was re-implemented using Python and Pytorch and trained on the Google Cloud Platform.
Implementation of a deep learning detection model to detect pedestrians in images taken at night from the NightOwls dataset. The model was implemented with Python and the Keras API for TensorFlow and trained on the Google Cloud Platform. This project was for the course of Scientific Reading and Writing at KTH.
