جست‌وجوی استاد و پژوهشگر

Gian Antonio Sustoمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Predictive Maintenance
• +۶ مورد دیگر

Gian Antonio Susto is an Associate Professor at the Department of Information Engineering , University of Padova . With a Ph.D. in Information Technology and post-doctoral experience at National University of Ireland, Maynooth, he leads research in Machine Learning , Semiconductor Manufacturing , and Industrial IoT . His work bridges Anomaly Detection , Continual Learning , and Algorithmic Fairness with applications in Hydroelectric Power Plants , Particle Accelerators , and Smart Mobility . B.Sc. and M.Sc. in Controls Engineering, University of Padova (cum laude) Ph.D. in Information Technology, University of Padova (2013) Post-Doc at National University of Ireland, Maynooth (2012-2013) Assistant Professor at University of Padova (2013-2021) His research focuses on Explainable AI , Virtual Metrology , and Deep Learning for manufacturing and infrastructure monitoring. Recent projects include the AIMS5.0 (AI for Manufacturing Sustainability) and MICS (Circular Economy in Italy) initiatives. His publications span Engineering Applications of Artificial Intelligence , IEEE Transactions , and Information Processing & Management , with 15+ recent papers on topics like Fault Diagnosis , Continual Learning , and Fair Ranking . Key scientific awards include: IEEE CCTA Best Student Paper Award (2021) IP&M 2020 Ph.D Paper Award Best Industry Paper Award, European Workshop on Advanced Control and Diagnosis (ACD 2019) He has supervised Ph.D. students on projects involving Particle Accelerators , Plant Behavior Modeling , and Explainable AI , with alumni now at institutions like Max Planck Institute , IBM , and Scripps Research . Current teaching includes Reinforcement Learning and Explainable Machine Learning at graduate and Ph.D. levels.

• Software Engineering
• Deep Learning
• Human-Computer Interaction
• +۷ مورد دیگر

Prof. Dr. Chunyang Chen is a full professor in the Department of Computer Science at the Technical University of Munich (TUM) in Heilbronn, Germany, and an adjunct professor at Monash University, Australia. He is a core member of the Munich Data Science Institute, board member of Heilbronn Data Science Center, and fellow at Fortiss. His research spans Software Engineering, Deep Learning, and Human-Computer Interaction (HCI), focusing on AI/ML, NLP, and program analysis applications. PhD from Nanyang Technological University (NTU), Singapore His research explores three primary domains: AI-Driven Mobile App Development : Focused on requirement elicitation, UI design, code generation, and GUI testing using LLMs, NLP, and lightweight program analysis. Software Repository Mining : Involves library/API migration, collaborative editing, query reformulation, and thesaurus construction. Deep Learning Security : Examines robustness and security in mobile apps, including on-device model attacks, backdoor attacks, phishing attacks, and defense mechanisms. His recent publications highlight trends in LLM applications for software testing, bug replay, accessibility, and dataset construction. Awards include Best Paper Honorable Mention at CHI 2024, DECRA, ACM SIGSOFT Early Career Award, multiple Distinguished Paper Awards, and the Chinese Government Award for Outstanding Self-financed Students Abroad. PhD students: Shengcheng Yu, Yanqi Su, Mark Huasong Meng, Wenchao Gu, Zhen Tao, Kai Huang, Sidong Feng, Yujin Huang, Jian Gu Master/Bachelor students: Dustin Minh Duc Vu, Jason Siu, Ruiqi Dong, Minh Nguyen, An-Chieh Chang, Chao Wu, Yujin Huang, Ishan Joshi, Xinyan Li Chunyang Chen has supervised numerous students across institutions including TUM, Monash University, Chinese Academy of Sciences, ANU, and others. His teaching portfolio includes courses on Generative AI, software engineering, and programming foundations. He actively contributes to academic service as a guest editor, program committee member, and reviewer for top-tier conferences and journals.

• Entrepreneurship
• Tourism Management
• Sustainable Business Models
• +۲ مورد دیگر

Dr. Silvia Baiocco serves as Assistant Professor at University of Rome Tor Vergata, teaching entrepreneurship, tourism management, and marketing courses across Bachelor and Master programs including 'Creation of Enterprises and Entrepreneurship' (Master), 'Fundamentals of Service Management' (Bachelor), and 'Tourism and Cultural Management for Sustainability' (Bachelor). Her institutional affiliation centers on Business Economics (sector ECON-07/A) with research rooted in co-evolutionary theory. Her research critically examines sustainable business model innovation through three interconnected lenses: (1) tourism-destination co-evolution in historic villages and Alberghi Diffusi, (2) university-industry knowledge exchange for sustainable spin-offs via PNICube Observatory frameworks, and (3) technology integration in smart tourism through AI-driven destination management. She emphasizes context-specific adaptation in both high-income (Italy) and low/middle-income settings (Ghana), with strong focus on social impact and heritage preservation. Analysis of her 2023-2025 publications reveals accelerating focus on digital tourism transformation (AI applications, smart city integration) and resilience-building in accommodation firms. Her work consistently applies co-evolutionary frameworks to decode organizational adaptation, particularly in sustainable entrepreneurship contexts. The PNICube Observatory reports highlight her policy-relevant contributions to university research valorization. As educator, Dr. Baiocco actively shapes future business leaders through courses spanning startup creation to sustainable destination management. Her research trajectory indicates deepening engagement with technology-mediated sustainability solutions and cross-sectoral innovation ecosystems, particularly through ongoing PNICube Observatory initiatives.

Luca Demetrioمشاهده پروفایل

استادیار

• Adversarial Machine Learning
• Malware Detection
• Cybersecurity
• +۳ مورد دیگر

Luca Demetrio is an Assistant Professor at the University of Genoa, Italy, specializing in adversarial machine learning and cybersecurity. Previously, he was a Post-doctoral Researcher at the PRA Lab within the Department of Electrical and Electronic Engineering at the University of Cagliari. He holds bachelor's (2015), master's (2017), and Ph.D. (2021) degrees from the University of Genova, with his doctoral thesis focusing on formalizing evasion attacks against security detectors. His research emphasizes enhancing the robustness of machine learning models against adversarial attacks, particularly targeting malware detectors, SQL injection defenses, and Windows security systems. He leads the development of SecML Malware, a Python library for generating adversarial Windows malware, and contributes to the SecML framework. His work has been published in top-tier journals like ACM TOPS and IEEE TIFS. Key research interests include adversarial example generation, malware analysis, and cybersecurity defense mechanisms. He has explored query-efficient attacks on phishing detectors, certified adversarial robustness via randomized smoothing, and robust synthetic data-driven threat detection. His recent studies (2023–2025) address challenges in hardening machine learning models against evasion attacks, adversarial SQL injection countermeasures, and securing autonomous driving systems from adversarial reinforcement learning attacks.

Antonino Vallesiمشاهده پروفایل

استاد

• Neuroscience
• Cognitive Psychology
• Neuropsychology
• +۳ مورد دیگر

Antonino Vallesi is a Full Professor in Neuropsychology and Cognitive Neuroscience at the University of Padua. He holds a master's degree in Psychology (University of Padua, 2003) and a PhD in Neuroscience (SISSA, Trieste, 2007). He has held roles as Assistant and Associate Professor at SISSA and the University of Padua before achieving his current rank. His research focuses on executive functions, cognitive aging, and temporal processing, employing neuroimaging, EEG, and experimental psychology methods. He has supervised over 10 PhD students, 13 postdocs, and 65 trainees. Education: PhD in Neuroscience, SISSA, Trieste (2007) Master's in Psychology, University of Padua (2003) Research Interests: The anatomo-functional organization of executive functions, cognitive aging, temporal processing, and neuropsychological methodologies. His work explores these areas through advanced techniques like EEG, neuroimaging, and neuromodulation. Awards: Bertelson Award (2011) Outstanding Young Person Award (2011) SIPF Prize (2017) ERC Starting Grant (2013) Advising & Grants: Supervisor of over 10 PhD students and 13 postdocs. Secured significant funding including an ERC grant. Involved in grant reviewing for EU programs (e.g., Horizon 2020) and international agencies. Labs & Teams: Leads the Executive Function Lab at the University of Padua, focusing on cognitive neuroscience and clinical applications.

• Structural Health Monitoring
• Seismic Analysis
• Bayesian Calibration
• +۱۴ مورد دیگر

Gaetano Miraglia is a Fixed-term Assistant Professor in the Department of Structural, Building and Geotechnical Engineering (DISEG) at Politecnico di Torino, where he conducts research in structural health monitoring, seismic analysis, and computational modeling. He is a member of the Interdepartmental Center R3C – Responsible Risk Resilience Centre, contributing to interdisciplinary efforts in risk mitigation and infrastructure resilience. His work spans both theoretical and applied domains, with strong emphasis on heritage preservation and sustainable urban development. His research interests include Bayesian calibration of nonlinear models, hybrid simulation, peridynamics, masonry structures, and the integration of satellite interferometric (InSAR) data with in-situ measurements for structural monitoring. He applies advanced computational and machine learning techniques to improve the accuracy and reliability of structural assessments, particularly in historical and monumental buildings. His work supports UN Sustainable Development Goals 9, 11, and 13. His recent publications demonstrate a consistent focus on data fusion, digital twinning, domain adaptation, and real-time damage detection. He frequently collaborates with researchers such as Rosario Ceravolo and Erica Lenticchia, publishing in high-impact journals like Computer-Aided Civil and Infrastructure Engineering , Structures , and Scientific Reports , as well as at major conferences including EWSHM, SAHC, and EVACES. His research is applied in projects such as the monitoring of the Vicoforte Sanctuary and the development of the CAMELOT and HY-LEARN toolboxes. Research Projects: MONITORAGGIO VICOFORTE (2024–2026) – Member of Research Group CAMELOT – PoC Transition (2023–2024) – Member of Research Group HY-LEARN – Model Calibration via Hybrid Simulation and ML (2022–2024) – Scientific Manager (PNRR Mission 4) He teaches in various programs, including as a course collaborator in PhD, Master’s, and Bachelor’s level courses such as Earthquake Engineering , Structural Consolidation , and Seismic Risk of Cultural Heritage . He is also an inventor on national and international patents and software related to the CAMELOT toolbox, highlighting the translational impact of his research. He has no listed scientific awards or formal advisees in the provided text.

• Information Retrieval
• Crowdsourcing
• Human-in-the-loop AI
• +۵ مورد دیگر

Dr. Gianluca Demartini is a leading researcher in Human-in-the-loop AI Systems with significant contributions to Crowdsourcing , Information Retrieval , and Generative AI applications. His work bridges Machine Learning and Human-Computer Interaction , focusing on Bias Management , Fact-Checking , and Ethical AI . Major Affiliations : L3S Research Center, ScienceWISE platform, and collaborations with institutions like University of Queensland and University of Padua Over 15 years, his research has explored Crowdsourcing Quality Control (Mechanical Cheat 2012), Entity Ranking (2008-2013), and Semantic Search . Recent work (2024-2026) focuses on Generative AI Impacts in domains like Media Literacy , Data Curation , and Visual Analytics . Scientific Recognition : Best Paper Award (Top 1.4%) at ICTIR 2023 Best Short Paper Award (Top 0.6%) at ECIR 2020 Honorable Mention (Top 2%) at CSCW 2020 Best Demo Award at ISWC 2011 3rd Best Paper at LA-WEB 2008 His 15 most recent publications (2024-2026) demonstrate expertise in LLM-based Content Moderation , Immersive Data Visualization , and Trustworthy AI Systems . He has pioneered methods for Bias Detection in Wikipedia (2013), Entity Ranking (2008-2013), and Human-AI Collaboration frameworks. His work consistently addresses ethical challenges in AI for Social Good and Responsible Data Science .

Germain Gauthierمشاهده پروفایل

استادیار

• Political Economy
• Public Economics
• Econometrics
• +۷ مورد دیگر

Germain Gauthier is an Assistant Professor at the Department of Social and Political Sciences, Bocconi University. His work bridges economics and political science with a focus on political economy, public economics, and machine learning methods. He develops AI tools for social scientists, particularly for analyzing unstructured data like texts and images. Ph.D. in Economics, Ecole Polytechnique (2018–2023) Postdoctoral Researcher, ETH Zürich (2023) M.Sc. in Analysis and Policy in Economics, Paris School of Economics (2017–2018) M.Sc. in Quantitative Economics, HEC Paris (2013–2017) His research spans two main areas: applied studies on digital technologies' societal consequences (e.g., X's algorithms, #MeToo's impact) and methodological innovations in machine learning for social science data. Recent publications include work on protest dynamics, inequality, and narrative extraction from texts. Key trends in his publications include: Machine learning applications to political and economic analysis Text mining for social science narratives Empirical studies on protest movements and digital governance Algorithmic bias in labor markets Economic policy evaluation using big data He has received significant funding: Swiss National Sciences Foundation Grant (300K CHF, 2023–2026) Swiss National Sciences Foundation Grant (100K CHF, 2020) Laboratoire d’Excellence Ph.D. Grant (2018–2023) He teaches courses on Public Finance (Bocconi) and Text as Data for Social Sciences (LMU München), and has developed open-source software packages like relatio and DeepLatent for text analysis and latent variable modeling.

Damiano Piovesanمشاهده پروفایل

دانشیار

• Protein function prediction
• Intrinsically disordered proteins (IDPs)
• Machine learning in bioinformatics
• +۵ مورد دیگر

Damiano Piovesan is Associate Professor in Bioinformatics (SSD BIO/10) at the Department of Biomedical Sciences , University of Padua , Italy. Since March 2022 he has held this rank, having previously served as Assistant Professor (2022) and PostDoc researcher (2019) in the same department. Education 2013 – PhD in Biotechnology, Pharmacology and Toxicology, University of Bologna 2009 – MSc in Bioinformatics, University of Bologna 2007 – BSc in Biotechnology, University of Bologna Research Focus Piovesan’s research integrates machine-learning approaches with structural bioinformatics to advance understanding of intrinsically disordered proteins (IDPs) and protein function prediction . He develops widely used resources such as MobiDB for disorder annotation, DisProt for functional curation of disordered regions, and RING for residue interaction networks. Additional interests include tandem repeat proteins , cancer-related IDP targets , and community benchmarking initiatives (CAFA, CAID, CAGI). Publication Trends His 2024–2025 output is dominated by updates to flagship databases ( InterPro , DisProt , MobiDB ), next-generation disorder predictors leveraging deep learning ( PredIDR , MobiDB-lite 4.0 ), and large-scale genomics challenges ( CAGI6 ). Across the decade, recurring themes include methodological advances in disorder prediction, creation of interoperable bioinformatics platforms, and rigorous benchmarking to ensure community-wide reliability. Scientific Awards No specific awards are listed in the provided materials. Advising & Grants No individual students or grant details are explicitly supplied; however, his leadership in multi-institutional consortia (e.g., InterPro, DisProt, CAFA) implies substantial supervisory and funding coordination roles. Labs & Teams Piovesan is affiliated with the BioComputingUP Lab ( https://biocomputingup.it/ ) at the University of Padua, a hub for computational biology and bioinformatics tool development.

Gabriella Casalinoمشاهده پروفایل

استادیار

• Computational Intelligence
• eHealth
• Data Stream Mining
• +۵ مورد دیگر

Gabriella Casalino is an Assistant Professor at the University of Bari Aldo Moro, Department of Computer Science, and a key researcher at CILAB - Computational Intelligence Lab. Her work focuses on Computational Intelligence methods for interpretable data analysis, particularly in eHealth, Data Stream Mining, and eXplainable Artificial Intelligence (XAI) within medical and educational domains. She has contributed to innovative approaches in smartphone-based health monitoring, fuzzy logic applications, and remote vital sign detection via photoplethysmography. Education : Ph.D. in Computer Science, with advanced training at institutions like Universitat de Girona and Université de Mons. Research Trends : Recent publications highlight applications of evolving granular computing, neuro-fuzzy systems, and explainable AI in hypertension prediction, bipolar disorder monitoring, and educational data analysis. Key subfields include remote health monitoring, medical data streams, and hybrid AI models. Grants : Research funded by AIRC (Italian Cancer Research Foundation), focusing on computational methods for healthcare challenges. Labs & Collaborations : Active in CILAB, collaborating on projects involving mHealth solutions, cardiovascular risk assessment, and intelligent educational systems.

• Machine Learning
• Data Mining
• Bioinformatics
• +۱۱ مورد دیگر

Andrea Passerini is a Full Professor in the Department of Information Engineering and Computer Science at the University of Trento, Italy, where he also serves as Coordinator of the PhD programme in Information Engineering and Computer Science (Ministerial Decree 45/2013). His academic footprint spans multiple departments including Mathematics, Sociology, Cellular Biology, and Industrial Engineering, reflecting deep interdisciplinary engagement across computational sciences and life sciences. His research centers on Machine Learning and Data Mining with specialized expertise in Neuro-Symbolic AI , Probabilistic Reasoning , and Statistical Relational Learning . He pioneers methods for graph-based learning, medical AI applications, and explainable systems, with significant contributions to bioinformatics (particularly RNA-protein interactions) and healthcare diagnostics. His work bridges theoretical rigor with practical implementations in critical domains. Analysis of his 2025 publications reveals dominant trends in neuro-symbolic integration for graph data, human-AI collaboration in medical decision-making, and robust recommender systems. His research increasingly focuses on interpretable AI for high-stakes applications like surgical planning and physician support, while advancing foundational techniques in graph neural networks and concept-based modeling. As PhD programme Coordinator, Professor Passerini mentors doctoral candidates across AI and computer science disciplines. His collaborative network extends to medical researchers at CIBIO (Cellular, Computational and Integrative Biology department) and industrial partners, though specific lab structures aren't documented in available materials. Current projects emphasize medical AI validation, temporal network modeling, and LLM integration with structured reasoning frameworks.

Federico Becattiniمشاهده پروفایل

استادیار

• Computer Vision
• Deep Learning
• Artificial Intelligence
• +۷ مورد دیگر

Federico Becattini is a Tenure-Track Assistant Professor at the Department of Information Engineering and Mathematics (DIISM), University of Siena, Italy. He is an active member of the Siena Artificial Intelligence Lab (SAILab), where he contributes to cutting-edge research in computer vision, deep learning, and artificial intelligence. His work spans multiple interdisciplinary domains, including autonomous driving, human behavior understanding, cultural heritage, neuromorphic vision, and fashion recommendation. His research interests center on memory-based neural networks , which he has applied in numerous publications at top-tier venues such as CVPR, ECCV, IEEE TPAMI, and ACM TOMM. He has also delivered tutorials on this topic at international conferences including ICIAP 2022 and ACM MM 2022, and taught a Ph.D. course at the University of Florence. His recent work is aligned with the Collectionless AI paradigm, which emphasizes continual learning and interaction with dynamic environments. The recent publications highlight a strong trend in human-centric AI , focusing on understanding people through multimodal analysis of face, body, and clothing, as well as generating 3D virtual avatars. There is also a clear emphasis on memory-augmented architectures for temporal reasoning, explainability, and adaptive learning. His editorial role as Associate Editor of the International Journal of Multimedia Information Retrieval further underscores his standing in the research community. Associate Editor, International Journal of Multimedia Information Retrieval (IJMIR) Organizer, Workshop on Facial and Body Expressions (ICPR2020) Co-organizer, T-CAP Workshop (ICIAP2021, ICPR2022) Co-organizer, MCFR Workshop (ACM MM 2022) Co-organizer, WCPA Workshop and Challenge (ECCV 2022) Federico Becattini actively advises students and researchers within SAILab, particularly in the context of Ph.D. theses and research projects related to Collectionless AI and memory-based models. While specific grants are not mentioned, his extensive publication record and leadership in workshops and editorial roles suggest involvement in funded research initiatives. He collaborates with both academic and international research communities, serving as a reviewer for top-tier conferences and journals. He is a core member of the SAILab research group, which is pioneering the Collectionless AI initiative—a framework for continual learning over time, interacting with humans and agents without relying on pre-built static datasets. This lab serves as a hub for innovation in adaptive and sustainable AI systems.

• Artificial intelligence
• Biomedical signal processing
• Human movement analysis
• +۱۲ مورد دیگر

Marco Ghislieri is an Assistant Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino, Italy. He is a member of the Interdepartmental Center PolitoBIOMed Lab and teaches in the Biomedical Engineering program, including courses like Neuroengineering and Design of Programmable Biomedical Devices . His research spans Artificial Intelligence, Biomedical Signal Processing, Neuroscience, and Rehabilitation Engineering . PhD in Bioengineering and Medical-Surgical Sciences (2017-2021) at Politecnico di Torino Thesis: Muscle Synergy Assessment during Cyclic and Non-Cyclic Movements His research focuses on muscle synergy analysis in Parkinson’s Disease (PD) patients post- Deep Brain Stimulation (DBS) , AI-driven gait analysis for fall prevention, and wearable sensor applications for stress-cognitive decline monitoring. He leads the S-CoDe and OMNIA-PARK projects, and contributes to PRIN as a team member. Recent publications highlight advancements in machine learning for intraoperative DBS targeting , statistical gait analysis , and neurorehabilitation tools . He serves as Associate Editor for Scientific Reports and Applied Bionics and Biomechanics , and Guest Editor for Frontiers in Neural Circuits . Awards include the Carlo J. De Luca Award (2022) , GNB Doctoral Award (2022) , and the Best Poster Award at M. Grattarola Summer School (2022) . He supervises Fabrizio Sciscenti (PhD candidate) and collaborates on neuroengineering and biomedical device design courses. His work addresses Goal 3 (Good Health) and Goal 4 (Quality Education) of the UN SDGs.

Emma Colamarinoمشاهده پروفایل

پژوهشگر

• Electroencephalographic (EEG) Signal Processing
• Electromyographic (EMG) Signal Processing
• Brain-Computer Interfaces (BCI)
• +۴ مورد دیگر

Emma Colamarino is a Researcher at the Department of Computer, Control and Management Engineering "Antonio Ruberti" of Sapienza University of Rome. She holds an M.Sc. in Biomedical Engineering (2014, cum laude) and a Ph.D. in Bioengineering (2019). Since 2015, she has been a research collaborator at the Neuroelectrical Imaging and Brain-Computer Interfaces Lab of IRCCS Fondazione Santa Lucia in Rome and served as a Visiting Ph.D. student at Imperial College London (2018). From 2019 to March 2023, she was a Post-Doctoral Fellow at Sapienza University. Her research focuses on Advanced electroencephalographic (EEG) and electromyographic (EMG) signal processing Brain-Computer Interface (BCI) protocols for cerebral function recovery Machine learning in neurorehabilitation Hybrid BCIs integrating cortico-muscular networks Recent publications address stroke rehabilitation, BCI design, spectral graph theory, and EMG-EEG integration. Her work spans biomedical data analysis, neuroengineering, and rehabilitation technology validation. Scientific awards include multiple grants from Sapienza University and the Italian Ministry of Health, a Student Award at the 7th International BCI Meeting (2018), and recognition as a Subject Expert (2019). She has supervised/co-supervised 18 MD theses across Biomedical, Management, and Robotics Engineering disciplines.

Federico Nutarelliمشاهده پروفایل

استادیار

• International Trade
• Health Economics
• Machine Learning
• +۳ مورد دیگر

Federico Nutarelli is an Assistant Professor of Economics at the IMT School for Advanced Studies Lucca, Italy. His research focuses on the intersection of machine learning and economic analysis, particularly in international trade, health economics, and industrial organization. He holds a Ph.D. in Economics from IMT Lucca, and previously conducted postdoctoral research at Bocconi University. In 2024, he was a Visiting Scholar at MIT Sloan School of Management. Key research interests include causal machine learning methods to analyze heterogeneous firm responses to economic shocks, pharmaceutical market pricing strategies, and structural demand models. His work bridges methodological rigor with applied relevance, contributing to health economics, trade dynamics, and innovation policy. Recent publications (2020–2025) explore topics such as matrix completion for world trade analysis, machine learning applications in economic complexity, and modeling innovation ecosystems. His work often employs advanced statistical techniques like Shapley values and reinforced Bernoulli processes. No scientific awards were explicitly mentioned in the provided texts. Federico collaborates with institutions like Bocconi University and MIT Sloan, reflecting his interdisciplinary network in economics and data science.