Find Academic Supervisors

• Computer Vision
• Natural Language Processing
• Artificial Intelligence
• +7 more

Federico D'Asaro is a researcher and PhD student at Politecnico di Torino, affiliated with the Department of Control and Computer Science (DAUIN) and the Computer Graphics & Vision Group (CGVG). He works as an external lecturer and teaching assistant for the Applied Data Science Project course in the Data Science and Engineering program. His research focuses on Vision-Language Models (VLMs), particularly addressing the Modality Gap in multimodal feature spaces and their applications in downstream tasks like semantic segmentation and speech emotion recognition. Education: Master's degree in Data Science and Engineering (2021), currently pursuing PhD in Computer and Systems Engineering (39th cycle, 2023-2026). His research at the intersection of Natural Language Processing and Computer Vision investigates how reducing the Modality Gap improves crossmodal performance. Recent work applies Large Speech Models (LSMs) to cross-lingual emotion recognition and non-verbal vocalization tasks. He collaborates with researchers like Andrea Bottino, Giuseppe Rizzo, and Juan José Márquez Villacis on projects involving multimodal deep learning and feature extraction. The trends in his publications highlight expertise in multimodal learning (Vision-Language Models, speech-text alignment), deep learning for segmentation and emotion recognition, and crossmodal adaptation in speech processing. His 2025 work focuses on contrastive alignment and non-verbal vocalization, while 2024 studies explore transfer learning of speech models across languages. Teaching Contributions External lecturer for Applied Data Science Project (2025/26) Course collaborator for Applied Data Science Project (2024/25) Federico is part of the Computer Graphics & Vision Group (CGVG) , contributing to interdisciplinary projects that bridge Computer Vision , Natural Language Processing , and Speech Emotion Recognition . His work emphasizes practical applications of multimodal models in real-world scenarios.

Debora NozzaView profile

Assistant Professor

• Natural Language Processing
• Hate Speech Detection
• Ethics in NLP
• +4 more

Debora Nozza is an Assistant Professor in Computing Sciences at Bocconi University , where she leads research on ethics in Natural Language Processing with a focus on hate speech detection and algorithmic bias in multilingual contexts. She was awarded a €1.5 million ERC Starting Grant (2023) for her project PERSONAE , exploring personalized and subjective approaches to NLP. Previously, she secured a €120,000 Fondazione Cariplo Grant (2019) for the MONICA project analyzing Italian responses to the pandemic. Education: PhD in Computer Science (2018), University of Milano Bicocca Her research spans hate speech detection , gender bias in language models , and computational social science . She organizes key workshops like the Workshop on Online Abuse and Harms (WOAH) and Gender Bias in NLP (GeBNLP) at ACL conferences, and co-led shared tasks including HODI (Homotransphobia Detection in Italian) and HatEval (hate speech against immigrants). Recent publications address actionable bias metrics , gender pronoun handling in translation , and large-scale stereotype amplification via text-to-image generation . Her work combines model interpretability , cross-lingual frameworks , and social impact analysis to tackle systemic biases. Scientific Awards: ERC Starting Grant (2023) Fondazione Cariplo Grant (2019) She actively participates in international conferences, including keynotes at ECCV 2024 and WASSA 2024 , and collaborates with groups like MilaNLP and ACL workshops .

Marco GrangettoView profile

Professor

• Computer Vision
• Image Processing
• Artificial Intelligence
• +5 more

Marco Grangetto serves as Full Professor in the Department of Computer Science at the University of Turin, coordinating research in image processing and computer vision. His expertise spans wavelets, image/video coding, data compression, error resilient video coding, and biomedical image processing, with significant contributions to ISO JPEG2000 standardization and editorial roles in IEEE Transactions on Multimedia. His educational background includes a PhD in Electrical and Communications Engineering (2003) and MSc in Telecommunications Engineering (1999), both from Politecnico di Torino. His research integrates Artificial Intelligence and Deep Learning with medical imaging and fundamental compression theory , producing innovations in neural network pruning, capsule networks, and entropy-based models. Recent work focuses on Covid-19 diagnosis from chest X-rays and efficient 3D scene modeling. His publication trends reveal dual trajectories: applied medical AI (Covid-19 diagnostics, lung cancer segmentation) and theoretical advances (learned compression, contrastive learning, bias mitigation). This bridges clinical validation with information-theoretic foundations, particularly in resource-constrained environments. Scientific recognition includes: Premio Optime by Unione Industriale di Torino (2000) Fulbright Grant for research at UC San Diego (2001) He maintains leadership through IEEE editorial positions, ISO standardization participation, and MPAI membership. His research group develops medical datasets (UniToChest, UniToPatho) while advancing neural network efficiency for clinical deployment. Current projects focus on entropy minimization techniques and unbiased representation learning for healthcare applications.

• AI and Law
• Privacy and Data Protection
• Algorithmic Fairness
• +3 more

Francesca Lagioia is Associate Professor of Legal Informatics, AI and Law, and Ethics for AI at the University of Bologna's Department of Legal Sciences, with a secondary appointment as Part-time Professor at the European University Institute. Her research examines legal frameworks for artificial intelligence systems including algorithmic fairness, explainability, data protection, and liability. Primary research domains span: computational law applications, AI's impact on consumer rights and democratic processes, copyright implications of AI-generated content, and normative modeling in socio-technical systems. She leads the PRIN2022 project 'EQUAL' focused on countering algorithmic discrimination. Recognition includes the Italian National Scientific Qualification as Associate Professor (2020) and previous Max Weber Postdoctoral Fellowship.

Martin ScheininView profile

Part-Time Lecturer

• International Human Rights Law
• Public International Law
• Comparative Constitutional Law
• +6 more

Martin Scheinin is a Part-time Professor in the Department of Law at the European University Institute (EUI), where he previously served as full-time Professor of International Law and Human Rights (2008–2020) and Dean of Graduate Studies (2016–2019). He is currently also the British Academy Global Professor at the University of Oxford and a member of the Scientific Committee of the EU Fundamental Rights Agency. He collaborates with the PluriCourts Centre at the University of Oslo on the development of a future World Court of Human Rights. PhD in Law, University of Helsinki (1991) Professor of Constitutional Law, University of Helsinki (1993–1998) Professor of Constitutional and International Law, Åbo Akademi University, Finland (1998–2008) His research focuses on international human rights law, public international law, and comparative constitutional law, with special expertise in human rights in counter-terrorism, digital surveillance, AI, indigenous rights, and climate change. He advocates for a human rights-based approach to global governance and security. His recent scholarly output centers on the intersection of human rights law with emerging technologies and global crises. Themes include digital discrimination, AI and freedom of expression, climate litigation involving indigenous communities, and pandemic response frameworks grounded in human rights. His work bridges doctrinal legal analysis with policy-oriented research. Notable scientific contributions include his role in the landmark Daniel Billy et al. v. Australia case before the UN Human Rights Committee, affirming states’ human rights obligations in climate change contexts. He has also led research on surveillance ethics, counter-terrorism sanctions, and digital rights. UN Human Rights Committee Member (1997–2004) UN Special Rapporteur on human rights and counter-terrorism (2005–2011) British Academy Global Professor (2020–2024) Contributor to Just Security , Verfassungsblog , EJIL: Talk! , Opinio Juris Martin Scheinin has supervised over 30 PhD students at EUI, covering topics such as international criminal law, human rights adjudication, digital rights, and climate justice. His research is supported by institutions including the British Academy and the PluriCourts Centre. He leads the project 'Addressing the Digital Realm through the Grammar of Human Rights Law' (2020–2024). He is actively involved in interdisciplinary research teams focusing on AI and human rights, transnational legal governance, and climate justice. His work with the PluriCourts Centre and the Bonavero Institute of Human Rights at Oxford underscores his engagement with global legal reform initiatives.

Luca SagliettiView profile

Assistant Professor

• Machine Learning
• Statistical Physics
• Supervised Learning
• +10 more

Luca Saglietti is an Assistant Professor in the Department of Computing Sciences at Bocconi University, Milan. He holds a PhD from the Polytechnic of Turin (2018) and has conducted postdoctoral research at Microsoft Research (Cambridge, MA), ENS (Paris), and EPFL (Lausanne). His work bridges Machine Learning and Statistical Physics, focusing on theoretical models to understand learning algorithms. Research Interests Supervised/semi-supervised learning paradigms Transfer learning, continual learning, distillation Multi-learner systems (adversarial learning, multi-headed networks) Curriculum learning and fairness in ML His recent publications explore large deviations in ML models, curriculum learning theory, and regularization via distillation. Teaching includes Foundations of Physics I (BAI program) and Computer Programming (BEMACS program).

Federica RiccaView profile

Associate Professor

• Combinatorial Optimization
• Network Optimization
• Portfolio Selection
• +5 more

Federica Ricca is an Associate Professor (SECS-S/06) at the Department of Methods and Models for Economy, Territory and Finance (MEMOTEF) at Sapienza University of Rome. She teaches courses including Project Management, Financial Optimization and Asset Management, and Quantitative Portfolio Selection for Management at the Faculty of Economics. Her academic career includes: Associate Professor at MEMOTEF Department since 2015 Researcher (MAT/09) at the Department of Statistical Sciences, University of Rome 'La Sapienza' from December 30, 2008 to September 30, 2015 Federica Ricca's research focuses on applied mathematics, combinatorial optimization, and network optimization . Her work has significant applications in electoral system mathematics (particularly biproportional seat allocation and electoral districting), service location problems and customer center assignment, and classification problems. She has developed innovative approaches to portfolio selection problems using combinatorial optimization and financial network analysis. Her recent publications demonstrate a strong focus on the intersection of mathematical optimization with practical applications in finance and political systems. She has made significant contributions to portfolio optimization through network approaches, political districting with attention to minority representation, and combinatorial optimization for financial decision-making. Her work bridges theoretical mathematical concepts with real-world applications in finance and electoral systems. Dr. Ricca is actively involved in research projects including: Mathematical Models for Portfolio Selection Problems: Innovative Combinatorial Optimization and Financial Network Analysis Approaches Quantitative models and efficient procedures for territorial partition and aggregation problems with applications in the public and private services sector She maintains regular office hours for student consultations and follows institutional protocols for student communications, requiring students to use their institutional email addresses when contacting her.

• Computer Science
• Artificial Intelligence
• Reinforcement Learning
• +6 more

Eric Eaton is a prominent researcher in Computer Science, specializing in Artificial Intelligence, Reinforcement Learning, and Federated Learning. His work bridges theoretical advancements with practical applications in healthcare, robotics, and educational technology, as evidenced by his collaborations with institutions like the Vector Institute and co-authors such as Marcel Hussing and Amir-massoud Farahmand. Research Focus: Lifelong Learning, Object-Centric Representation, and Algorithmic Fairness Key Contributions: ELLA algorithm, Distributed Continual Learning frameworks, and AI integration in surgical video analysis His recent publications address critical challenges in high update ratio reinforcement learning, federated learning for surgical data, and ethical considerations in algorithmic fairness. These works highlight his interdisciplinary approach, combining AI with healthcare and education. Eaton's leadership in projects like FORLA and Slot-BERT demonstrates innovation in unsupervised learning and temporal coherence. His involvement in the CS2023 curriculum design underscores his commitment to advancing computer science education.

• Computer Networks
• Edge Computing
• Internet of Things (IoT)
• +5 more

Paolo Bellavista is a Full Professor of Distributed and Mobile Systems at the Department of Computer Science - Science and Engineering, Alma Mater Studiorum – University of Bologna. His research focuses on edge computing, federated learning, IoT, digital twins, and blockchain applications. With over 140 journal articles and 200+ conference papers, his work appears in top venues such as ACM Computing Surveys and IEEE Transactions. He serves as Editor-in-Chief of the MDPI Computers Journal and holds editorial roles in IEEE Communications Surveys&Tutorials and ACM Computing Surveys. His contributions span network optimization, cybersecurity, and smart cities, with notable achievements in citation metrics (h-index 46 on Google Scholar). Research interests include distributed systems, vehicular networks, AI-driven security, and QoS-aware edge infrastructures. He has organized major conferences like IEEE Mobile Cloud and ACM ICDCN. Current projects explore federated learning unlearning, digital twin entanglement, and blockchain-based data spaces. His work bridges theoretical foundations with real-world deployments in smart manufacturing and healthcare. Editorial Roles : MDPI Computers (Editor-in-Chief), IEEE Communications Surveys&Tutorials (Associate Editor), ACM Computing Surveys (Editor) Conference Leadership : Technical Program Chair for IEEE Mobile Cloud 2015, General Co-Chair for SCNS 2018 Key Contributions : Over 140 journal papers, 15 special issues as guest editor, and 200+ conference publications His lab develops middleware for edge-cloud continuum applications and collaborates internationally on projects like InAbled Cities. Office hours are held weekly at the University of Bologna’s DISI department.

Antonela TommaselView profile

Adjunct Professor

• Social Computing
• Recommender Systems
• Machine Learning
• +5 more

Dr. Antonela Tommasel is a Researcher at CONICET and an Adjunct Professor at National University of the Center of the Province of Buenos Aires (UNICEN) , working at the intersection of social computing, machine learning, and misinformation mitigation. Her work combines computational techniques to address challenges in social media analysis, bias detection, and recommender system design. PhD in Computer Sciences (2017), UNICEN Bachelor in Software Engineering (2012), UNICEN Her research focuses on recommender systems with applications to social media trustworthiness , including projects like Faking It! (fake news dataset creation) and OHARS (harm-aware recommendation frameworks). Key themes include misinformation spread analysis , gender bias detection , and machine learning applications for social good. She has led 11+ years of teaching in courses like Advanced Java Programming, Recommender Systems, and Natural Language Processing for AI specializations. Her technical certifications include NVIDIA Deep Learning Institute programs on GPU-accelerated data science and Coursera specializations in machine learning and visualization. Currently based at ISISTAN Research Institute in Tandil, Argentina, she advocates for responsible AI through tools like graphoW (graph-of-words library) and Sen4Smells (technical debt prioritization). Her work emphasizes bridging social media analysis with practical implementation frameworks.

Fosca GiannottiView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Big Data
• +6 more

Fosca Giannotti is a Researcher at the Institute of Information Science and Technologies (ISTI-CNR), focusing on the intersection of artificial intelligence, big data, and human-centered computing. Her work emphasizes the societal implications of AI, including algorithmic bias, data-driven discrimination, and the ethical challenges of machine learning systems. Specializes in explainable AI and human-in-the-loop methodologies Investigates the societal impact of AI, particularly in shaping social interactions and economic behaviors Contributes to projects addressing fairness, transparency, and accountability in AI systems Giannotti's research explores how AI algorithms, trained on human-generated data, can inadvertently reinforce societal inequalities while striving to create technologies that offer context-aware, understandable decisions for diverse users.

• High Performance Computing
• Grid Computing
• Cloud Computing
• +7 more

Giovanni Aloisio is a Full Professor of Information Processing Systems at the Department of Innovation Engineering, University of Salento, Italy. He is a leading figure at the Euro-Mediterranean Center on Climate Change (CMCC), where he serves as Director of the Supercomputing Center and Director of the Scientific Computing and Operations (SCO) Division. He is also a member of CMCC’s Governance bodies, including the Strategic Council and Executive Committee. His research focuses on High Performance Computing (HPC) , grid and cloud computing , and distributed data management . He has played a pivotal role in major European and international initiatives such as the European Grid Forum (Egrid), the EU FP7 IS-ENES projects, the European Exascale Software Initiative (EESI), and the International Exascale Software Project (IESP). His work bridges advanced computing infrastructure with Earth system modeling and climate science. Recent publications highlight a strong trend toward integrating HPC simulations , big data analytics , and machine learning in climate science. His team develops tools like PyOphidia for scalable data analytics and explores AI-driven methods for weather and climate modeling, pandemic forecasting, and environmental data downscaling. The research spans interdisciplinary domains including climate modeling, epidemiology, and environmental health. He has authored over 100 peer-reviewed journal articles. His leadership in EU-funded projects and international working groups, such as the ENES HPC Task Force and the WCES Working Group, reflects his significant contributions to the scientific community. He leads the HPC laboratory at the University of Salento and oversees advanced computing operations at CMCC. His work supports large-scale scientific workflows, climate data spaces within the European Open Science Cloud, and the convergence of HPC, AI, and data analytics.

Emilio RussoView profile

Professor

• Mathematical Finance
• Actuarial Mathematics
• Option Pricing Theory
• +3 more

Emilio Russo is a Full Professor in the Department of Economics, Statistics and Finance at the University of Calabria, Italy, where he has maintained continuous academic appointments since 2003. His career progression includes teaching assistant positions (2003-2007), Researcher (2007-2015), Associate Professor (2015-2024), and Full Professor (2024-present), with consistent teaching responsibilities in Financial Mathematics and Mathematical Methods for Economics across undergraduate and graduate programs in Economics, Business Administration, and Mathematics. He holds a Degree in Statistics and Actuarial Science (cum laude, 2003) from the University of Calabria, an MPhil from Brunel University (2005), and a PhD in Computational Methods for Economic and Financial Forecasts and Decisions from the University of Bergamo (2006). His research focuses on mathematical finance and actuarial mathematics, particularly option pricing theory, interest rate models, regime-switching frameworks, and insurance policy valuation under stochastic environments. Analysis of his recent publications reveals a dominant trend in developing lattice-based numerical methods for pricing complex financial derivatives and insurance products under multiple risk factors, including fractional Brownian motion, stochastic volatility, and regime-switching dynamics. His work frequently addresses path-dependent features in variable annuities, surrender options, and the impact of exogenous factors on policyholder behavior, with strong emphasis on computational efficiency and model flexibility. Professor Russo serves as Principal Investigator for the PRIN 2022 project "Building resilience to emerging risks in financial and insurance markets" and Task Leader for PNRR 2022's "Age-It" project (Spoke 6, Work Package 5). He actively contributes to academic discourse as a reviewer for Quantitative Finance, Journal of Computational and Applied Mathematics, Scandinavian Actuarial Journal, and other leading publications. He is a core member of the University of Calabria's research group "Metodi quantitativi per l'economia, la finanza ed il management," which specializes in quantitative decision-making models for finance, actuarial science, industrial economics, and logistics using mathematical programming, econometric techniques, and statistical methods.

Gianluca Maria Carmelo EspositoView profile

Professor

• Administrative Law
• Public Law
• Tax Law
• +5 more

Gianluca Maria Carmelo Esposito serves as a Full Professor of Administrative Law and Public Law Institutions at Sapienza University of Rome, where he teaches courses including Public Law Institutions (L40) and Administrative Law. He is affiliated with the Department of Social and Economic Sciences within the Faculty of Political Sciences. Previously, he held the position of Full Professor of Administrative Law at the University of Salerno. Professor Esposito's research focuses on Administrative Law, Public Law, Tax Law, and Public Contracts, with particular expertise in public procurement regulations, administrative procedures, and the relationship between public administration and private entities. His scholarly work demonstrates a strong emphasis on the practical application of legal frameworks to contemporary administrative challenges, including digital governance, environmental protection, and business regulation. His recent publications reveal a trend toward examining the intersection of traditional administrative law with emerging issues such as algorithmic decision-making, taxpayer rights in comparative perspective, and the evolving nature of public programming. His scholarly output includes numerous monographs, journal articles in high-impact Italian legal journals (Fascia A), and significant editorial work including the comprehensive 'Public Contracts Code. Commentary on doctrine and jurisprudence' (2017). Professor Esposito has held numerous significant institutional positions that complement his academic work, including Director General for Small and Medium Enterprises at the Ministry of Economic Development (2011-2013), Director General for Business Incentives (2009-2011), and Head of Department for Competitive Policies at the Ministry of Agriculture (2013-2014). He has also served on various national commissions including the National Commission for Environmental Impact Assessment (AIA) and as a legal consultant to the Council of Ministers. His academic leadership extends to directing doctoral programs, serving on examination committees, and leading research initiatives. Professor Esposito has also been active in professional development, teaching at the Higher School of Public Administration and various specialized legal training programs.

Marco RondinaView profile

Lecturer

• Data Science
• Computer Vision
• Artificial Intelligence
• +8 more

Marco Rondina is a Lecturer at the Polytechnic University of Turin , specifically within the Department of Control and Computer Science (DAUIN) . He is also a PhD student in Control and Computer Engineering , expected to graduate in 2026. His work bridges Data Ethics , Responsible AI , and Software Engineering . PhD Student: 2022–2025 Guest Researcher: Weizenbaum Institute, TU Berlin (2025) Marco's research focuses on data fairness to promote responsible AI development, addressing algorithmic bias , dataset documentation , and ethical data practices . His publications analyze gender stereotypes in LLMs, fairness in car insurance pricing, and biases in facial analysis systems for marginalized groups like people with Down syndrome. His recent articles emphasize neural-symbolic AI , data quality , and empirical evaluation frameworks for ethical compliance. He has received the Quality Award Template 2025 and actively contributes to teaching as a Teaching Assistant in courses like Business Information Systems and Data Ethics and Data Protection . Scientific Awards Quality Award Template 2025 Marco collaborates with the Internet Media Group (IMG) at DAUIN and participates in initiatives like the Nexa Center for Internet and Society . His outreach includes talks on AI ethics, algorithmic audits, and data discrimination risks.