Since January 2022, Luca Pasa has held the position of Assistant Professor at the Department of Mathematics, University of Padova. His main research interests lie in the field of Machine Learning, including Deep Learning, Computational Neuroscience, and Automatic Speech Recognition. Currently, his research activity is focused on the application of Deep Learning methods on structured domains (sequences, trees, and graphs).

Luca Pasa has co-authored numerous research papers published in international peer-reviewed journals and conference proceedings. Additionally, he has served as a Program Committee member for several Machine Learning conferences and has actively organized special sessions at various international Machine Learning conferences. Currently, he is an Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems (TNNLS). He is also a member of the IEEE Computational Intelligence Society, the IEEE Task Force on Deep Learning, and the Italian Association for Artificial Intelligence (AIxIA).

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Her research activity focuses on identifying possible biological markers for the early diagnosis of Parkinson’s disease, specifically blood and CSF biomarkers, and their possible correlations with clinical and neuropsychological profiles. Moreover, Marta has been studying the pathological deposition of different proteins (e.g. alpha-synuclein, tau) in specific tissues (e.g. skin and gastrointestinal system), and the concomitant immunological changes occurring in the tissue. These projects, together with the evaluation of non-invasive techniques such as Optical Coherence Tomography, aim to identify specific patterns supporting the differential diagnosis of different conditions, including Parkinson’s disease and atypical parkinsonisms such as Multiple System Atrophy, as well as and other neurodegenerative disorders (e.g. Alzheimer’s disease).

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He earned a Ph.D. in Bioengineering under the supervision of Prof. Alessandra Bertoldo with a thesis entitled “Quantitative neuroimaging of perfusion with Arterial Spin Labelling: deconvolution and physiology-based models”. He was a post-doctoral fellow at the Department of Information Engineering, at the Padova Neuroscience Center of the University of Padova and at the Department of Neuroscience, Medicine and movement of the University of Verona. He is currently a Research Fellow at the Department of Information Engineering at the University of Padova.

His research interests mainly include artificial intelligence applied to MRI, to study perfusion, brain clearance and related disorders exploiting both medical images and biomedical signals.

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His research activity focuses on the discovery and validation of central and peripheral neuropathological biomarkers for neurodegenerative diseases, particularly Parkinson’s Disease. Other research interests include clinical neuroanatomy of the human brainstem, nervous control of respiratory centers, infectious and inflammatory pathology of the brain and its relationship with neurodegeneration.

In the last 5 years Aron Emmi has published 50+ peer-reviewed articles in international journals in the field of neuroscience, neuropathology and human anatomy and presented his research activity as a speaker at over 60 national and international conferences. He serves as ad-hoc reviewer for several high-impact scientific journals (Nature NPJ Parkinson’s Disease, Movement Disorders, Journal of Neurology, Neurosurgery and Psychiatry, Neurobiology of Disease). He have co-authored 4 academic textbooks for students and specialists on neuroanatomy and microscopic anatomy.

Aron Emmi is a member of several national and international scientific organizations, including the Royal College of Pathologists, the European Confederation of Neuropathological Societies, and the italian group for neuromorphology.

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He obtained a M.Sc. in Biomedical Engineering from Politecnico di Milano, Italy, in 2017, and he received a PhD in Information Engineering from the University of Padova, Italy, in 2021. He then pursued two years of postdoctoral research at the Intelligent Autonomous System laboratory, the University of Padova, Italy. He is currently Assistant Professor at the Department of Information Engineering of the University of Padova, since 2023. He spent 10 months as “visiting student” at the Eidgenössische Technische Hochschule (ETH) in Zurich, and 7 months as “visiting researcher” at the Campus Biotech (École Polytechnique Fédérale de Lausanne, EPFL) in Gèneve, in 2016 and 2019 respectively.

The research activity of Stefano Tortora has been strongly characterized by a commitment to the emerging field of Neurorobotics. In particular, he has focused on its interdisciplinary engineering and neuroscientific aspects. His research has been passionately dedicated to improving the interaction between users and robotic devices by creating a new paradigm in human-machine interfacing (HMI) fusing multimodal information coming from wearable sensors and robot’s sensors. During the Ph.D. activity, Stefano Tortora worked on the development of hybrid human-machine interfaces (h-HMI) to decode movement intention from wearable sensors. In 2020, Stefano Tortora was among the first researchers in developing recurrent neural networks for gait decoding from electroencephalography data. He proposed and implemented novel probabilistic approaches combining brain and muscular activity to ensure a more reliable control of robotic devices.

Currently, his work concerns the integration of these h-HMI with shared-autonomy algorithms for the development of intelligent assistive devices aimed at enhancing the mobility of people with motor impairments. On these topics, Stefano Tortora is currently involved as research fellow in the PNRR PE8 “Age-IT – Ageing Well in an Ageing Society” project (https://ageit.eu/wp/), responsible for the analysis of multimodal EEG-EMG data for the control of a lower limb exoskeleton and other assistive robotic devices via machine learning and deep learning. Additionally, his project on the development of an intelligent lower limb exoskeleton has been recently founded within the PNRR PE1 “FAIR – Future Artificial Intelligence Research” initiative (https://fondazione-fair.it/cascade-calls/).

Stefano Tortora is Associate Editor for the European Journal on Artificial Intelligence, Guest Associated Editor and Review Editor for the journal “Frontiers in Neurorobotics”, and Guest Associated Editor for the journal “MDPI Applied Sciences” in the section on “Robotics and Automation”. He is also Publication Chair for the IEEE European Conference on Mobile Robots 2025 (https://ecmr2025.dei.unipd.it/). Since 2021, he is in charge of the course “Sistemi di elaborazione 1” at the Department of Statistical Sciences, and of the course of “Python Programming for Data Science and Engineering” within the Doctoral School of Information Engineering (University of Padova) and the National Doctorate in Robotics and Intelligent Machines (DRIM).

Stefano Tortora is one of the Team Managers of the WHi Team and Whi Students Team (University of Padova), participating at the Cybathlon BCI race since 2019. With his teams, Stefano Tortora won 2 gold medals, 1 silver medal and 1 bronze medal in the various Cybathlon editions, and received the BCI Jury Award for the most promising technology at the last Cybathlon 2024 in Zurich.

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She received from M.I.U.R (Italian Ministry of Education, University and Research) the National Scientific Habilitation as Associate Professor in General Psychology, Psychobiology and Psychometrics (11/E1).

She got a Master degree in Evolution of Animal and Human Behaviour (cum laude) at the University of Torino in 2009. She obtained a Ph.D in Psychological Sciences at the University of Padova (2014) working on the mechanisms underlying non-verbal numerical abilities using fish as model species. She worked as post-doctoral fellow at the Department of General Psychology, University of Padova (2014-2018) and then she moved to London where she worked as Marie Curie research fellow at Queen Mary University of London (2018-2020). From 2020 to 2021 she was a Stars-Grant post-doctoral fellow at the University of Padova (Department of Biomedical Science).

Her research interest focuses on the evolution of cognitive abilities in animals, with specific reference to the non-verbal numerical systems, visual perception and the impact of brain lateralization on cognitive functions, by combining behaviour with in vivo imaging.

She attracted more than 300k  of research grants by successfully participating in competitive calls at national and international level. In 2017 she won a Marie-Curie Individual Fellowship and in 2019 she was granted by the University of Padua with the STARS Starting Grant.

She has authored more than 50 peer-reviewed articles in international scientific journals and 5 book chapters. She has organized 2 symposia and she has been invited as a speaker in national and international conferences and research institutes.

She regularly acts as a reviewer for international peer-review journals in the field of comparative psychology and she is Guest Editors of special issues in peer-review scientific journals.

Google scholar: https://scholar.google.com/citations?hl=it&user=pSBUG4QAAAAJ&view_op=list_works&sortby=pubdate

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Upon completing a Ph.D in quantum physics at University of Turin, my research interests switched towards data analysis for neuroscience and I joined the Statistical and Biological Physics sector of the International School for Advanced Studies (SISSA), Trieste, where I worked in Prof. Alessandro Laio’s group from 2015 to 2018. My research activity within Laio’s group focused on advanced clustering techniques and their application to the study of dynamically changing brain connectivity. I deepended my focus on neuroscience during my stay at the Timone Neuroscience Institute (CNRS) in Marseille (2018-2021), where I joined the BraiNets group led by Andrea Brovelli. In Marseille I worked on the characterization of directional connections in brain networks, and their disruption in stroke.

My main research interest is understanding functional connections and in the brain, and their relation with the underlying dynamics. In my research I use a wide variety of tools including dimensionality reduction, information theory, and model inference.

I am author of 18 publications, of which 11 as a leading author. I lecture on statistics, data analysis, inference and information theory to physics undergraduate students and Neuroscience PhD students.

Personal web page: https://micheleallegra.github.io/

Google scholar: https://scholar.google.com/citations?user=pwlWv54AAAAJ&hl=it&oi=ao

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She received her master’s degree in Psychology path Neuroscience (2009) and her Ph.D. in Psychological Sciences and Education (2013) at the University of Trento. As a Ph.D. student, she visited the Family and Child Neuroscience Laboratory at the University of Denver (CO, US). She was a post-doctoral fellow at the National Institute of Child Health and Human Development, National Institute of Health (NICHD -NIH, Bethesda, MD, US; 2014-2015) and Nanyang Technological University (Singapore, SG; 2016-2017).

Scientific Production
She is an author of 22 peer-reviewed articles in international scientific journals (most of them in high-quality journals including Scientific Report Nature, Proceeding of National Academy of Sciences, Developmental Review, NeuroImage and Social neuroscience), 1 book and 4 book chapters. She served as an ad-hoc reviewer for Behavioural Brain Research, Brain Imaging and Behavior, Brain Research, Cognitive, Affective, & Behavioral Neuroscience, Developmental Psychobiology, Parenting: Science and Practice, Perceptual & Motor Skills Journal, PLOS ONE, Psychological Reports, Social Cognitive and Affective Neuroscience, and Social Neuroscience. She was a Guest Editor for Infant Behavior and Development Journal (Edited by Esposito, Bornstein & Rigo, 2020).

Grant and Award
She is a PI of a Research Project funded by BIRD182991-2019 (15K EUR), and a Co-PI of several Research Projects funded by PRIN 2017 (around 119K EUR), Academic Research Fund (AcRF) Tier 1 Grant 2016 (circa 67K EUR), BIRD195080-2020; (28K EUR), Padova Neuroscience Center, PCN (5K EUR). She received the Intramural Research Training Award (IRTA) as Postdoctoral Visiting Fellow (NIH, 2014).

Research Interest and Collaborations
Her research interest focuses on the psychobiological basis of parenting and intersubjectivity. Through an ecological and interdisciplinary perspective, she investigates how caregivers’ response is modulated by the interplay between individual and clinical factors of parents (e.g., temperament, mood) and biological changes occurring during the early post-partum period. In connection with these studies; her research also investigates the effect of situational context in which parents respond to infant needs. She uses an integrated approach that spans from psychological measures to observational, behavioral, and neuroimaging studies (fMRI). National and international research collaborations: Alessandra Simonelli (University of Padova), Pilyoung Kim (University of Denver), Xiaoxia Du (East China Normal University), Marc H. Bornstein, (NIH), Gianluca Esposito (Nanyang Technological University, University of Trento), Paola Venuti (University of Trento). Since 2012, she is a member of the Special Community Membership of the University of Denver (Dr. Kim).

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I got my master degree in Mathematics and my PhD in Health Planning Sciences at the University of Padova in 2016 and 2021, respectively.

In my master thesis and during my PhD, I worked in the field of Magnetic Particle Imaging (MPI), which is a novel tracer-based medical imaging technique, studying effective approximation schemes suitable for reconstructing signals along Lissajous curves, which are typical sampling trajectories in a MPI scanner. Moreover, I studied and applied kernel-based machine learning techniques in the context of rare diseases diagnosis and in the classification of patient-derived xenografts.

After the PhD, I started collaborating with the MIDA group of the University of Genova in the field of space weather forecasting, and I analysed and applied deep learning methods. Then, I won a one year postdoctoral grant sponsored by the Istituto Nazionale di Alta Matematica (INdAM). The research project linked to my current position concerns p-Laplacians on hypergraphs and related machine learning approaches.

My research interest is mainly in approximation theory, kernel-based approximation and machine learning, deep learning, medical imaging and space weather forecasting. I am part of the research groups CAA (Constructive Approximation and Applications) between the Universities of Padova and Verona, Rete Italiana di Approssimazione (RITA) and Methods for Image and Data Analysis (MIDA).

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He graduated in Medicine and Surgery in 2012 and completed his residency in Neurology in 2018 at the University of Padova. He completed his PhD in Neuroscience in 2022 at Padova Neuroscience Center, with a thesis on brain disconnection in focal lesions (stroke, gliomas).

Main interests:

• Impact of brain tumors and cancer treatments on clinical outcomes (cognitive and neurological impairment, survival) mediated by structural and functional disconnection.
• Neurological complications of cancer.
• Transthyretin amyloidosis and peripheral neuropathies.

Scopus: https://www.scopus.com/authid/detail.uri?authorId=56359388100

Google scholar: https://scholar.google.it/citations?user=WwsDAwsAAAAJ&hl=it

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