Find Academic Supervisors

• Computer Networks
• Machine Learning
• Reinforcement Learning
• +6 more

Alessio Sacco is a Fixed-term Assistant Professor at the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, Italy. He's affiliated with the NETGROUP - Computer Networks Group and actively contributes to research in computer networks, machine learning, and software-defined networking. Current academic position: Assistant Professor Research group: NETGROUP Key collaborations: Guido Marchetto, Flavio Esposito Research Interests focus on: Computer networks and communications Dynamic programming and reinforcement learning Machine learning applications in network management Software-defined networks Cybersecurity and privacy analysis Intelligent Internet of Things systems Recent Publications demonstrate expertise across: Network security verification (PathSafe) AI-driven agricultural optimization (FertilizeSmart) Cross-layer congestion control (Flecto) Reinforcement learning for computing continuum scheduling Explainable AI for network transparency (ClearNET) Multi-agent exploration systems (MARS) Scientific Contributions include: Commercial research on network security and anomaly detection ERC sector expertise in distributed systems and machine learning Teaching in computer engineering programs since 2019 Advising involves: Supervising Federico Rinaudi (ongoing) Co-supervising Doriana Monaco (ongoing)

• Probability
• Mathematical Statistics
• Entropy Measures
• +3 more

Francesco Buono is a Fixed-term Assistant Professor at the Department of Mathematical Sciences (DISMA) within Politecnico di Torino . His research focuses on probability theory, mathematical statistics, and entropy-based measures, with a particular emphasis on reliability engineering and stochastic modeling. He has published extensively on topics such as extropy, Kullback-Leibler divergence, and generalized entropy in applications to system reliability and ranked set sampling. His recent publications (2023–2025) highlight collaborations with researchers like Franco Pellerey, Jorge Navarro, and Maria Longobardi. Key trends include advancements in stochastic orders , predictive modeling , and information-theoretic measures applied to failure analysis and statistical inference. Notable journals include Entropy , Communications in Statistics , and Journal of Applied Probability .

• Edge AI
• Electronic Design Automation
• Federated Learning
• +3 more

Valentino Peluso is a Fixed-Term Assistant Professor at the Department of Control and Computer Engineering (DAUIN) of Politecnico di Torino. His research focuses on Edge AI , Electronic Design Automation , Federated Learning , and Low-Power Design , with applications in distributed systems and cyber-physical technologies. Associate Editor at IEEE Transactions on Circuits and Systems II (2025-) Organizer of workshops on smart grids and IoT at IEEE conferences Recipient of Politecnico di Torino's RIMINI SCHOOL 2025 badge for research funding participation His recent work explores homomorphic encryption for secure federated learning, DVFS side-channel attacks in edge inference, and energy-efficient pipeline optimization for embedded systems. Publications span IEEE Transactions, ICECS, ICCD, and VLSI-SoC conferences. Scientific Awards & Roles: RIMINI SCHOOL 2025 Certification Learning to Teach (L2T) badge Editorial contributions to IEEE journals Chair and co-chair roles in hardware/software co-design sessions He contributes to teaching in High-Level Synthesis , Machine Learning for IoT , and Efficient AI Computing at both PhD and MSc levels.

Domenico TortorellaView profile

Assistant Professor

• Machine Learning
• Graph Neural Networks
• Reservoir Computing
• +6 more

Domenico Tortorella is an Assistant Professor (RTD-A) in the Department of Computer Science at the University of Pisa, Italy. He earned a PhD in Computer Science (cum laude) in 2024, an MSc in Computer Science (cum laude) in 2020, and a BSc in Computer Engineering (cum laude) in 2017. His research focuses on graph neural networks , reservoir computing , and deep learning on graphs , particularly addressing challenges like heterophily, over-squashing, and structural encoding. He contributes to conferences such as ICANN, ESANN, and NeurIPS, and is part of the Computational Intelligence and Machine Learning (CIML) research group. Research Trends: His recent publications emphasize Graph topology encoding (e.g., Randomized Ising Models) Efficiency in graph kernels and neural networks Explainable AI for deep graph models Stability analysis in echo state networks Handling heterophilic graphs via reservoir computing Temporal resolution in recurrent architectures Grants & Service: He received funding from the Future AI Research (FAIR) project (2024–2025) and served on program committees for ICANN, NeurIPS workshops, and GbRPR. He was Vice-Chair of the IEEE Student Branch of Pisa (2023–2024) and holds memberships in IEEE, ACM, ENNS, and IAPR's CVPL.

• Algorithm fairness
• Explainable AI
• Trustworthy AI
• +7 more

Eliana Pastor is a Fixed-term Assistant Professor at the Department of Control and Computer Engineering (DAUIN) , Politecnico di Torino , and a member of the SmartData@PoliTO - Big Data and Data Science Laboratory . She teaches courses including Explainable and Trustworthy AI (Computer Engineering) and Business Intelligence for Big Data (Management Engineering) across academic years 2023-2025. Scientific branch: IINF-05/A - Information Processing Systems (Area 0009 - Industrial and Information Engineering) ERC sectors: Algorithms, Artificial Intelligence, Machine Learning, Software Engineering, Web Systems Her research focuses on Algorithm Fairness , Explainable AI , and Trustworthy AI , with applications in speech processing, computer vision, and ethical AI systems. She leads the commercial research project Root Cause Analysis in Mechatronic Systems via Pattern Recognition and Causal AI (2024-2025) and supervises PhD students Eleonora Poeta (Safe and Trustworthy AI) and Alkis Koudounas (Speech Foundation Models). Recent publications address fairness in speech models, video LLMs for zero-shot summarization, and Kolmogorov-Arnold Networks for language understanding Collaborates with DBDM - Database and Data Mining Group (DAUIN) on AI ethics and data science projects

Nicoletta NocetiView profile

Associate Professor

• Computer Science
• Robotics
• Machine Learning
• +7 more

Nicoletta Noceti is an Associate Professor at the Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS) of the University of Genoa. Her academic roles include teaching courses such as Computer Vision, Deep Learning, and Machine Learning across undergraduate and master's programs in Robotics Engineering and Computer Science. She also contributes to innovative education initiatives like the 'Smart rogaining' model for computer science orientation. Her research focuses on interdisciplinary areas spanning computer vision, human-centered artificial intelligence, and robotics. Key themes include human motion analysis, causal discovery, and the application of AI in healthcare and social interaction. Recent work emphasizes disentangled representations for microscopy images, scene-unbiased action recognition, and understanding engagement dynamics in virtual teams of older adults. She also explores uncertainty-aware systems for head pose estimation and kinematic primitives in action similarity judgments. Her research frequently intersects with robotics, cognitive science, and biomedical engineering, addressing challenges such as causal inference in nonlinear models and embodied interaction design for robots. These efforts aim to bridge computational methods with human perceptual and motor systems, with applications in assistive technologies and social robotics.

Fabio PrevidiView profile

Professor

• Control Systems
• Mechatronics
• Aerospace Engineering
• +5 more

Fabio Previdi is a Full Professor in Control Systems at the Department of Management, Computer and Industrial Engineering, University of Bergamo. His career spans roles from Research Assistant at the University of Glasgow (1999-2000) to Associate Professor (2006-2017) before assuming his current position. He holds a PhD in Control Systems from Politecnico di Milano (1999), MSc in Electronic Engineering (1993) and Physics (2008) from Politecnico di Milano and University of Milan. Research focuses on control systems, mechatronics, and aerospace applications. Key areas include electromechanical actuators, fault detection, system identification, and biomedical engineering. Over 150 peer-reviewed papers highlight contributions to topics like robust control, machine learning for industrial systems, and pandemic modeling. Publications (2023-2020) address cutting-edge topics such as jamming detection filters for aircraft actuators, agent-based pandemic simulations, and kernel-based system identification. Awards: Not explicitly mentioned but notable contributions recognized through extensive publication record.

• Nuclear Fusion Engineering
• Plasma Physics
• Edge Plasma Modeling
• +3 more

Haosheng Wu is a Fixed-term Assistant Professor at Politecnico di Torino's Department of Energy (DENERG), affiliated with the College of Electrical and Energy Engineering. His research focuses on edge plasma modeling in fusion reactors, divertor physics, and computational plasma simulations using codes like SOLPS-ITER. He collaborates on teaching advanced master's courses including Nuclear Fusion Reactor Physics and Engineering. Wu leads the NEMO Research Group and supervises PhD student Matteo Maria Robaldo in Energetics. His work addresses critical challenges in fusion energy such as plasma detachment, heat load management, and impurity control in tokamaks like ASDEX Upgrade and J-TEXT. Recent studies include multi-machine benchmarking of edge transport models and SPARC divertor optimization. Teaching roles: Course collaborator in Nuclear Fusion Reactor Physics/Engineering since 2022 Research Group: Co-lead of DENERG's NEMO Group Key Projects: SOLPS-ITER validation, DTT facility design, SPARC divertor modeling Publications emphasize edge plasma dynamics, code comparison studies, and experimental data integration. Current research explores helium enrichment effects and Bayesian inference techniques for particle transport analysis. His contributions advance both fundamental plasma physics and applied fusion engineering solutions.

Kaicheng YangView profile

Researcher

• Computational Social Science
• Social Bots
• Misinformation
• +4 more

Kaicheng Yang is a postdoctoral researcher at Northeastern University , with an upcoming transition to Assistant Professor in the Department of Computer Science at Binghamton University in Fall 2025. His work bridges computational social science, AI ethics, and platform governance. Education: Ph.D. in Informatics from Luddy School of Informatics, Computing, and Engineering , Indiana University Bloomington His research investigates malicious information manipulation through social bots, algorithmic biases, and generative AI misuse, developing countermeasures for trustworthy online ecosystems. Recent publications explore domain-sharing patterns , news source credibility , and AI-generated social media fraud across journals like Scientific Data , PNAS , and Journal of Online Trust and Safety . Awarded Best Paper - Honorable Mention at the International AAAI Conference on Web and Social Media, Yang also contributes to open-source tools such as the LLM Domain Classification dashboard and Botometer X. His work has received media coverage from Wired , Forbes , and MIT Technology Review .

• Gravitational Physics
• Numerical Relativity
• Quantum Gravity
• +4 more

Roberto De Pietri is an Associate Professor at the Department of Physics and Earth Sciences within the Faculty of Mathematical, Physical and Natural Sciences at the University of Parma, Italy. He has held this position since March 1, 2015, following 10 years as a Researcher (Assistant Professor) at the same institution. His research focuses on gravitational physics, numerical relativity, and quantum gravity, with particular emphasis on compact objects, gravitational wave phenomena, and computational astrophysics. De Pietri has made significant contributions to numerical simulations of relativistic systems, including neutron star oscillations, binary mergers, and gravitational wave emission. His work bridges theoretical frameworks with high-performance computing applications. Analysis of his recent publications reveals a strong focus on gravitational wave astronomy, particularly in catalog development (GWTC series), multi-messenger follow-up strategies, and computational infrastructure for real-time alert generation. His research spans from fundamental quantum gravity investigations to practical applications in gravitational wave detection pipelines. De Pietri has supervised five PhD students to completion and numerous master's theses, with research topics centered on numerical relativity, gravitational waveforms, and quantum gravity. He has secured substantial computational resources through PRACE projects, including 30 million core hours for neutron star merger simulations. His teaching portfolio includes graduate courses in gravitational physics and undergraduate instruction in general physics and computational methods. He has presented his research at major international conferences including the Marcel Grossmann Meetings and specialized workshops on numerical relativity.

Diego Reforgiato RecuperoView profile

Professor

• Artificial Intelligence
• Semantic Web
• Big Data
• +5 more

Diego Reforgiato Recupero serves as Full Professor at the University of Cagliari's Department of Mathematics and Computer Science since February 2022, previously holding an Associate Professor position from December 2015. He maintains dual affiliations as an associated researcher at CNR's Semantic Technology Laboratory (STLAB) and active roles in university governance including the Commission for start-up and spin-off. His research spans Artificial Intelligence, Semantic Web, and Human-Robot Interaction with concrete applications in healthcare robotics and financial AI. Key methodologies include knowledge graph extraction, semantic role labeling, and frame-based language analysis, consistently bridging theoretical NLP with real-world implementations through spin-off ventures. Recent publications reveal a concentrated trajectory in healthcare robotics since 2017, where semantic technologies enable geriatric assessment systems and social robots. This work integrates deep learning with knowledge representation to solve practical challenges in hospital settings, demonstrating strong continuity between his Semantic Web foundations and emerging AI applications. Computer World Horizon Award (2006) Marie Curie International Grant PROVIDEO (2008) Best Researcher Award 2012 Telecom Italia Working Capital Award (2012) 10+ Best Research Paper Awards Data mining/sentiment analysis patent (20100023311) He coordinates major European initiatives including PhilHumans (funding 8 Ph.D. students) and Dr. VCoach Marie Curie Fellowship, while managing approximately 40 FP7/H2020 projects through R2M Solution. His grant leadership spans ICT, energy-saving networks, and robotics with industrial partners like Philips Research. As director of the Human-Robot-Interaction Laboratory and co-director of both the Artificial Intelligence and Big Data Laboratory and Semantic Web Laboratory, he leads interdisciplinary teams developing commercializable technologies. His spin-offs—including VISIOSCIENTIAE for financial AI and B-UP for semantic software—demonstrate consistent translation of academic research into market applications.

• Medical Image Analysis
• Video Surveillance Systems
• Pattern Recognition
• +3 more

Lorenzo Putzu is a Researcher (Assistant Professor) at the University of Cagliari's Department of Computer Science. He holds a PhD in Computer Science from the same institution (2016). His work focuses on pattern recognition techniques applied to medical imaging and video surveillance, including automated diagnosis support systems, security applications, and synthetic data generation for AI training. Since 2017, he has been part of the PRA Lab, contributing to projects like the IMMAGINA initiative for trustworthy AI in surveillance. Education: Bachelor's Degree in Computer Science (2010) Master's Degree in Computer Science (2012) PhD in Computer Science (2016) Research Interests: Medical image analysis for automated diagnosis (e.g., blood cell classification, histopathology) Video surveillance systems using AI (e.g., person re-identification, crowd counting) Synthetic data generation for improving model robustness Federated learning in healthcare applications Adversarial defense mechanisms in computer vision Key Projects: Development of MIRIAMS for multiple sclerosis segmentation Advancing leukocyte classification methods for hematological analysis Pioneering work on federated learning for cell nuclei segmentation Grant & Advising Activities: No explicit grants or advising roles listed Labs/Teams: Active contributor to the PRA Lab, collaborating on interdisciplinary AI projects in healthcare and security.

• Geometric Deep Learning
• Compositional Learning
• Optimal Transport
• +4 more

Mircea Petrache is an Assistant Professor at the Department of Mathematics, Pontifical Catholic University of Chile. His research focuses on geometric deep learning, compositional learning, optimal transport, calculus of variations, and geometric measure theory, with applications to large point configurations and mathematical analysis. He holds a PhD in Mathematics from ETH Zürich (2013) and an MSc from Scuola Normale Superiore (2008). Education: PhD in Mathematics, ETH Zürich, 2013 MSc in Mathematics, Scuola Normale Superiore, 2008 Research Interests: Petrache explores the interplay between geometric principles and machine learning, particularly in equivariant networks and compositional systems. His work on optimal transport and calculus of variations addresses energy minimization and crystallization problems in materials science. Recent projects include symmetry-based structured matrices and topological pseudodistances for data analysis. Grants: Fondecyt Regular Grant (2021–2025) on rigidity and uniformity in large point configurations. Events & Organization: Co-organized workshops such as 'Theoretical and Mathematical Aspects of Deep Learning' (2022) and 'Calculus of Variations: A New Generation' (2024). Active in conferences including NeurIPS, ICIAM, and AIM workshops on discrete geometry.

• Performance Engineering
• Cloud Computing
• Machine Learning
• +3 more

Emilio Incerto is an Assistant Professor at the IMT School for Advanced Studies Lucca, Italy. His research focuses on performance engineering, cloud computing, and formal methods applied to systems optimization. He specializes in developing frameworks for performance prediction, autoscaling solutions for microservices, and integrating machine learning techniques into performance modeling. Key research areas include: Performance modeling and optimization of distributed systems Autoscaling algorithms for dynamic cloud workloads Formal verification of real-time software systems AI-driven performance control mechanisms He has organized international workshops such as AIPerf (Artificial Intelligence for Performance Modeling), showcasing leadership in bridging AI and performance engineering. His work frequently explores reproducibility in performance studies and quantitative evaluation of complex systems like flocking behaviors in biological simulations. Notable contributions include μOpt (an efficient autoscaler for microservices) and μP (a performance prediction framework). His research emphasizes practical implementation of theoretical models through frameworks like SystemC-based environments for HW/SW co-design.

• Bayesian Inference
• Spatial Statistics
• Biostatistics
• +5 more

Maura Mezzetti is a Full Professor of Statistics at the Department of Economics and Finance, Università degli Studi di Roma II (Tor Vergata), Rome, Italy. Her career spans institutions such as Harvard School of Public Health, Università Bocconi, and European Institute of Oncology. She holds a PhD in Methodological Statistics from Università di Trento (1997) and a Laurea in Mathematics from Università di Milano (1992). Current Position: Full Professor (2021–Present) Former Roles: Associate Professor (2010–2021), Assistant Professor (2004–2010, Tor Vergata; 2000–2005, Bocconi) Research Interests: She specializes in Bayesian statistical methods for spatially correlated data, hierarchical modeling, survival analysis, and biostatistics. Her work addresses public health challenges like cancer incidence modeling, socioeconomic disparities in disease burden, and psychometric/motor behavior analysis. She has developed Bayesian compartmental models for chemical exposure studies and contributed to epidemiological research on breast and colorectal cancer risk factors. Scientific Awards: Yamagiwa-Yoshida Fellowship (2014) ICRETT Fellowships (2012) Excellence in Research (2005) from Bocconi AIRC Fellowships (1998, 1993) Teaching Contributions: She has taught Statistics at undergraduate and graduate levels across Tor Vergata and Bocconi, including specialized courses in medicine, European economy, and data analysis. Her pedagogical efforts include authoring educational materials like "Statistica Descrittiva: Esercizi risolti e guida alla soluzione usando Excel" (2002).