our team
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Hakan's research focuses on computer vision and machine learning with a emphasis on weakly/unsupervised learning, multi-task/domain/modality learning, and 3D understanding.
Elliot’s research interests include efficient machine learning, neural architecture search, and weakly supervised learning in the context of computer vision.
I received my academic education at the University of Catania, where I achieved my PhD degree in 2017. Since 2010, I collaborated with the PeRCeiVe Lab, where I started my research activity in the areas of computer vision and machine learning. Since 2020, I am an assistant professor at the University of Catania, where I carry out my teaching and scientific activity at the Department of Electrical, Electronic and Computer Engineering. My current research interests lie in the areas of biosignal analysis for medicine, continual learning in videos and reinforcement learning for robot navigation.
Henry's research focuses on using theoretically-grounded machine learning to train, understand, and reliably leverage foundation models.
I received my academic education at the University of Catania, where I achieved my PhD degree in 2023. Since January 2024, I'm an Assistant Professor at the Department of Electrical, Electronic, and Computer Engineering at the same institution. I am a member of the PeRCeiVe Lab research group, where I started my research activities in the areas of artificial intelligence, machine learning, and computer vision, with a particular focus on medical image analysis, federated learning, and explainable AI. My current research interests include developing AI-driven solutions for medical imaging, integrating federated learning frameworks for privacy-preserving data analysis, and applying explainable AI techniques to enhance transparency in machine learning models
Matteo Pennisi is an Assistant Professor at the University of Catania, working within the Perceive Lab at the Department of Electrical, Electronic and Computer Engineering (DIEEI). He holds a Ph.D. in Artificial Intelligence and is actively engaged in national and international research collaborations. His research interests primarily include Federated Learning, Generative Models, and Continual Learning, with a particular focus on developing privacy-preserving and bio-inspired AI systems. More recently, his work has also expanded to Explainable AI and Federated Unlearning, with the goal of enhancing transparency and user control in decentralized learning environments.
Changjian's research explores topics in 3D shape creation and analysis, sketch-based modeling, shape reconstruction, and medical image processing and modeling.
Hakan's research focuses on computer vision and machine learning with a emphasis on weakly/unsupervised learning, multi-task/domain/modality learning, and 3D understanding.
Elliot’s research interests include efficient machine learning, neural architecture search, and weakly supervised learning in the context of computer vision.
I specialize in clinical psychology, with a broad range of interests spanning from neuroscience to psychoanalysis. By examining the mechanisms that regulate cognition, behavior, and emotions in humans, my goal is to contribute to the activities of "PeRCeiVe Lab" aimed at developing artificial intelligence models inspired by biological processes.
Morteza earned his Master's degree in Electrical Engineering and joined PerceiveLab in 2021 to pursue his PhD in Systems, Energy, Computer, and Telecommunication Engineering. His research interests include computer vision (saliency detection and prediction, video understanding), Human-Computer Interaction and Intelligent Driving.
I am a PhD student at the University of Catania and a member of the PeRCeiVe Lab. My research focuses on leveraging deep learning models to explore how the brain perceives the world. Specifically, I study encoding and decoding models of the visual cortex, aiming to replicate nature’s design to advance AI and uncover the mysteries of the human and mouse brain. Outside the lab, I am deeply passionate about music, and I express this as a pianist and choir director.
I'm a PhD student at the University of Catania. My research activity focuses on audio and textual applications aimed at improving human-machine interaction. Additionally, I have also explored the explainability of visual models using text data.
I received my Bachelor's and Master's degrees in Computer Engineering from the University of Catania, Italy. Since 2022, I have been enrolled in the Italian National PhD Programme in Artificial Intelligence, carrying out my activities at the University of Catania, where I’m also a member of the PeRCeiVe Lab research group. My research primarily focuses on bio-inspired machine learning, continual learning, and computer vision to understand the underlying dynamics of neural activity through brain imaging data.
I'm a National PhD student in Artificial Intelligence (Health and Life Science) at the University Campus Bio-Medico of Rome with affiliation in University of Catania and member of PeRCeiVe Lab. My research interests are in the areas of Image/Video Classification, Pattern Recognition, Object Detection, Medical Imaging. I collaborated with Istituto Nazioale di Geofisica e Vulcanologia (Catania), AOU Città della Salute e della Scienza (Turin), Max Planck Institute for Intelligent Systems (Tübingen), STMicroelectronics R&D Power and Discrete (Catania).
My research focuses on applying modern Deep Learning in Cybersecurity, developing advanced techniques to support companies and public administrations in software security certification. Specifically, I work on implementing LLM-based vulnerability detectors and preventing adversarial attacks on LLMs.
I' m a PhD student at the University of Catania at Perceive Lab. My research investigates hippocampal memory and imagination mechanisms, translating them into computational frameworks to enhance continual learning and test-time adaptation in deep neural models. The aim is to mitigate catastrophic forgetting and improve domain adaptability by integrating memory consolidation and generative capabilities inspired by cognitive science.
I graduated in Computer Engineering, earning both my Bachelor's and Master's degrees from the University of Catania. I am currently a PhD student at the same university, affiliated with the PeRCeiVe Lab and the National Institute of Geophysics and Volcanology (INGV). My research focuses on the application of artificial intelligence in geophysics, using deep learning techniques on fiber optic data to enhance seismic monitoring and improve the accuracy of seismic data interpretation.
I graduated in Data Science from the University of Catania and am currently a PhD student in Artificial Intelligence at the University Campus Bio-Medico of Rome, with an affiliation at the University of Catania, where I am also a member of the PeRCeiVe Lab. My research focuses on AI techniques for astrophysical and medical imaging, such as segmentation and object detection. As part of my research activities, I collaborate with the National Institute for Astrophysics (INAF).
I'm Marco Finocchiaro, a Computer Engineering graduate from the University of Catania. I'm currently pursuing a PhD in Artificial Intelligence, with a focus on decoding neural patterns from brain imaging data, including fMRI and EEG.
I am a PhD student conducting research in deep learning applied to radio astronomy, funded by INAF – Astronomical Observatory of Catania. My initial work focused on applying self-supervised methods to radio-astronomical data. Currently, I am working on data curation to build a meaningful dataset for training a foundational model for radio sources. This research is driven by the upcoming influx of high-resolution data from the Square Kilometer Array (SKA)
Data Scientist at STMicroelectronics. My research interests include traditional machine learning and Bayesian networks, to optimize design, manufacturing, and processes in the semiconductor industry
Rutger Hendrix is a Ph.D. candidate following the National Italian PhD program organized by UCBM, working under the guidance of Prof. Concetto Spampinato, Dr. Federica Proietto Salanitri, and Prof. Ulas Bagci. His research focuses on Federated Explainable AI, where he aims to integrate visual interpretability and uncertainty measures into the Federated Learning framework. His goal is to bridge the gap between AI and clinical applications, helping physicians trust AI-based systems in their practice. Rutger became a medical engineer at Eindhoven University of Technology, where he specialized in medical image analysis and laid the foundations for his current research directions.
I am a PhD researcher at the FAIR Spoke 10 project at Perceive Lab, specializing in neuro-inspired approaches for incremental learning. My work focuses on designing adaptive neural architectures that enhance security, efficiency, and dynamic task adaptation in decentralized AI. By addressing challenges like catastrophic forgetting, I aim to improve sequential task learning while upholding ethical AI principles and responsible data governances
My PhD research focuses on the interpretability of human brain signals using functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data. Specifically focusing on the visual part of the brain that is stimulated by visual stimuli. The goal is to classify the visual stimuli seen from the acquired fMRI and EEG.
I’m a master student, studying Medical Engineering at Eindhoven University of Technology, in the Netherlands. I’m currently specializing in Medical Image Analysis. For my three-month internship, I visited Perceive Lab in Catania. I investigated the use of eye gaze data with SAM for segmentation in medical images of polyps, prostate and pancreas