Find Academic Supervisors

• Civil Engineering
• Structural Engineering
• Machine Learning in Structural Engineering
• +4 more

Giuseppe Carlo Marano is a Full Professor at the Department of Structural, Building and Geotechnical Engineering at Politecnico di Torino. He is also a component of the SISCON Interdepartmental Center for Infrastructure Safety. With expertise in civil and structural engineering, his work focuses on machine learning applications, seismic risk reduction, and sustainable structural optimization. Education Graduated cum laude in Structural Engineering from Polytechnic University of Bari PhD in Structural Engineering from University of Florence (2000) Research Interests Marano's research spans structural optimization, seismic engineering, and machine learning applications in civil infrastructure. He develops advanced computational models for: Seismic retrofitting of existing structures Optimization of steel and masonry structures Recycled materials in concrete production AI-driven structural health monitoring Multiobjective design methodologies Publication Trends His recent work emphasizes: Machine learning for concrete mix design and damage assessment Optimization of gridshells and arch structures Seismic isolation systems and vibration control Sustainable construction practices with recycled materials Multiobjective genetic algorithms for structural design Scientific Recognitions National Scientific Qualification - First Band (2013, MIUR Italy) Certificate of Appreciation for Outstanding Lecture (2012, China) Academic Contributions As an educator, he teaches: Consolidamento Strutturale (Structural Consolidation) Dinamica delle Vibrazioni Random (Random Vibration Dynamics) Progettazione Generativa (Generative Design) He also leads Challenge@PoliTo initiatives and contributes to national infrastructure safety regulations. Research Projects ADAPT4CE - Adaptive Digital Systems for Circular Economy (2025-2028) AI-ENVISERS - AI for Seismic Retrofit Environmental Impact (2023-2025) ADDOPTML - Additive Manufacturing Optimization (2021-2025)

• Software Engineering
• Deep Learning
• Human-Computer Interaction
• +7 more

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.

• Maternal Fetal Medicine
• Ultrasound
• Preeclampsia
• +3 more

Giuseppe Rizzo is a Full Professor at the Department of Maternal and Child Health and Urological Sciences, Sapienza University of Rome. His academic career focuses on Maternal Fetal Medicine, with expertise in ultrasound applications, fetal growth restriction, preeclampsia, and congenital anomalies. Research Interests : Delivery optimization, prematurity prediction, ultrasound diagnostics, and placental anomalies. Recent Publications : Over 15 high-impact studies in 2024-2025 on topics like umbilical cord abnormalities, cerebroplacental ratios, and gestational diabetes complications. Clinical Expertise : Prenatal counseling, Doppler ultrasound, and labor management protocols. Collaborations : Active in multicenter trials across Italy and Europe, with a focus on fetal neurosonology and high-fidelity obstetric simulation.

Luca DemetrioView profile

Assistant Professor

• Adversarial Machine Learning
• Malware Detection
• Cybersecurity
• +3 more

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.

• Behavioral modeling
• Electromagnetic compatibility
• Macromodeling
• +7 more

Stefano Grivet-Talocia is a Full Professor at the Department of Electronics and Telecommunications at the Polytechnic University of Turin, where he also serves as Director of the Doctoral School and President of the Doctoral School Council. He is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, the University Committee for Research, Technology Transfer and Services to the Territory, and the Commission for the Promotion of Library, Archive and Museum Heritage. His academic career spans over two decades at Politecnico di Torino, where he has established himself as a leading researcher in electromagnetic modeling and signal integrity. Grivet-Talocia earned his Laurea degree (summa cum laude) in Electronic Engineering in 1994 and his Ph.D. in Electronic and Communication Engineering in 1998, both from the Polytechnic University of Turin. Between 1994 and 1996, he conducted research at NASA/Goddard Space Flight Center in Greenbelt, Maryland. His educational background laid the foundation for his expertise in electromagnetic modeling, wavelet analysis, and signal processing. His research focuses on behavioral modeling, electromagnetic compatibility, macromodeling, model order reduction, numerical modeling, passivity, power integrity, signal integrity, transmission lines, and wavelets . Grivet-Talocia is particularly renowned for his work on passive macromodeling of interconnect structures, development of the TOPLine technique for transmission line simulation, and pioneering contributions to passivity enforcement algorithms. He has co-authored the first book entirely dedicated to Macromodeling (2016) and developed innovative approaches to waveform relaxation and wavelet-based signal processing. His recent publications (2024-2025) demonstrate continued leadership in model order reduction, with significant contributions to data-driven modeling of linear and nonlinear systems, power integrity analysis, and electromagnetic compatibility. His work spans both theoretical advances in numerical methods and practical applications in circuit design, with strong industry relevance particularly for semiconductor and electronic design automation companies. IEEE Fellow (2018-present) Three Intel SRS Grants (2022-2024) Three IBM SUR Grant Awards (2007-2009) Best Associate Editor Award - IEEE Transactions on Components, Packaging and Manufacturing Technology (2020) Multiple Best Conference Paper Awards (2006-2020) URSI Young Scientist Awards (1999) Ranked among the "top 2% worldwide researchers" (Stanford) since 2019 Grivet-Talocia actively supervises doctoral students including Michele Cusano, Sara Paknezhad Panahi, Antonio Carlucci, and Kun Zhao. He has secured numerous research grants from competitive national calls (PRIN) and commercial contracts with industry partners including Intel, IBM, Nokia, Hitachi, Infineon, and Cadence. His technology transfer activities include co-founding the spin-off IdemWorks (2007-2016), which was acquired by CST in 2016. He also developed the autoCircuits web service for automated circuit problem generation, widely used in electrical engineering education. He leads the EMC Group (Electromagnetic Compatibility) at DET and has been instrumental in establishing the Compact Dynamical Modeling research area. His work has practical applications in high-speed electronics design, with algorithms embedded in commercial tools like IBM PowerSPICE. Grivet-Talocia maintains strong industry connections through his research projects and serves as Associate Editor for IEEE Transactions on Components, Packaging and Manufacturing Technology.

• Structural Health Monitoring
• Seismic Analysis
• Bayesian Calibration
• +14 more

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.

Luciano LavagnoView profile

Professor

• Field Programmable Gate Arrays (FPGA)
• High-Level Synthesis
• Indoor Positioning
• +10 more

Luciano Lavagno is a Full Professor at the Department of Electronics and Telecommunications, Polytechnic University of Turin, with over two decades of academic and research contributions. His work bridges hardware acceleration, low-power electronics, and intelligent system design. Research Focus: Hardware-accelerated machine learning, high-level synthesis (HLS) for FPGA/ASIC, heterogeneous CPU/GPU/FPGA platforms Key Projects: SPACE (predictable acceleration), REBECCA (secure AI acceleration), HPC-National Center (quantum computing), and oral history preservation via "Ti racconto una storia" initiative His recent publications analyze CNN inference optimization, subgraph isomorphism, and superword-level parallelism exploitation. Lavagno supervises multiple PhD students working on FPGA acceleration, neural network hardware, and embedded systems. As Principal Investigator for national and EU-funded projects (PRIN, JTI-ECSEL, PNRR), he drives advancements in sustainable computing infrastructure. His patented technologies include MIx&Latch timing methodology, capacitive sensing innovations, and 5G acceleration frameworks.

Maurizio MUZZUPAPPAView profile

Professor

• Industrial Engineering Design
• Augmented Reality (AR) Applications
• Product Development Methodologies
• +5 more

Maurizio MUZZUPAPPA is a Full Professor at the Department of Mechanical, Energy and Management Engineering (University of Calabria) since 2018. His roles include Rector's Delegate for Technology Transfer, Academic Delegate for Education at DIMEG, and Head of the Physical Prototyping Laboratory at the MaTeRiA Center (UNICAL-CNISM collaboration). He supervises the Unical Racing Team in Formula SAE competitions and co-founded three university spin-offs: 3DResearch, Tech4Sea, and Q-BOT. As Scientific Director of projects like TECH4YOU (climate change adaptation technologies) and GROWN IN THE BLUE (Mediterranean reef conservation), he integrates research in industrial design, augmented reality, and underwater cultural heritage. He has authored over 200 publications (h-index 27) and holds 10 patents. His teaching includes Tools and Methods for Industrial Design and Formula SAE LAB . His research focuses on: Industrial design methodologies with parametric and sustainable approaches 3D prototyping and additive manufacturing User-Centered Design for product ergonomics Virtual/Augmented Reality applications in engineering and cultural heritage Underwater robotics and artifact restoration Recent publications highlight trends in AR for industrial maintenance, generative design tools, and mechatronic solutions for underwater heritage. He has supervised over 300 theses and 10 Ph.D. students while leading technology transfer initiatives.

• Digital Circuits
• Digital Signal Processing
• Machine Learning
• +10 more

Maurizio Martina is a Full Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino. He is a member of the Interdepartmental Center PEIC - Power Electronics Innovation Center and serves as an Associate Editor for the IEEE Transactions on Circuits and Systems I (2018-2023). His research focuses include: Digital circuits and signal processing Machine learning hardware architectures RISC-V extensions and post-quantum cryptography VLSI design for edge computing and IoT Recent publications emphasize cryptographic hardware implementations (CHIMERA, Keccak co-processors), RISC-V integration methodologies, and privacy-preserving neural network frameworks. His work spans VLSI architectures for video processing, bio-inspired electronics, and error correcting codes, with applications in cybersecurity, robotics, and biomedical systems. Scientific Recognition : Premio Nazionale Innovazione (2013) Premio dei Premi (2014) He supervises 12 PhD students across cycles 35-40 in Electrical, Electronics and Communications Engineering, including: Valeria Piscopo (2024-in progress) Alessandra Dolmeta (2022-in progress) Luigi Giuffrida (2022-in progress) Walid Walid (2019-2023) As part of the VLSILAB Group , his research explores hardware accelerators for machine learning, post-quantum cryptography on RISC-V, and bio-inspired embedded systems. Teaching activities include courses on Integrated Systems Architecture and Hardware & Wireless Security at Politecnico di Torino and Università di Pavia.

• Software Engineering
• Artificial Intelligence
• AI Trustworthiness Assessment (SE4AI)
• +5 more

Gias Uddin is an Associate Professor at York University's Lassonde School of Engineering and an Adjunct Professor at the University of Calgary . His research bridges Software Engineering (SE) and Artificial Intelligence (AI) , focusing on AI Trustworthiness Assessment (SE4AI) and AI-Driven Productivity Tools (AI4SE) . PhD in Software Engineering & AI, McGill University (2018) MSc in Software Engineering, Queen’s University (2008) BSc in Computer Science & Engineering, Bangladesh University of Engineering and Technology (2004) His research explores: Metamorphic Relations for LLM Hallucination Detection AI-Enhanced Software Documentation Foundational Models for Runtime System Modernization Developer-Centric AI Tooling Recent article trends show expertise in LLM Trustworthiness , Low-Code Platforms , and IoT Developer Communities . Awards include Distinguished Paper at FSE 2025 , multiple IBM Champion recognitions, and York Research Award . He leads the Data Intensive Software Analytics (DISA) Lab and mentors PhD students in SE-AI Intersections .

• Artificial intelligence
• Biomedical signal processing
• Human movement analysis
• +12 more

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.

Rocco OLIVETOView profile

Professor

• Software Engineering
• Artificial Intelligence
• Machine Learning
• +7 more

Prof. Rocco OLIVETO is a Full Professor at the University of Molise, affiliated with the School of Biosciences and Territory. His research spans software engineering, artificial intelligence, cybersecurity, and healthcare technology. He focuses on empirical studies of developer practices, AI-driven code analysis, vulnerability detection in smart contracts, and human-centric computing. His work also addresses challenges in game development, mobile app optimization, and wearable health monitoring systems. Notable research areas include code readability assessment, machine learning applications in healthcare diagnostics, and the effectiveness of AI tools like GitHub Copilot. He has contributed to projects like QualAI (continuous quality improvement for AI systems) and 2Vita-B (cognitive and physical rehabilitation systems). His empirical studies often bridge academic research with real-world developer workflows, emphasizing practical applicability. Prof. Oliveto's recent work explores topics such as automated gameplay analysis for game debugging, detection of engagement issues in video games, and robust methods for identifying security vulnerabilities. He has also investigated Dockerfile quality, developer frustration metrics, and the ethical implications of AI in administrative document simplification.

Marco PiconeView profile

Associate Professor

• Distributed Systems
• Internet of Things
• Edge Computing
• +3 more

Marco Picone is an Associate Professor at the Department of Sciences and Methods for Engineering (DISMI) of the University of Modena and Reggio Emilia. He leads the Distributed and Pervasive Intelligence (DIPI) Group and holds a PhD in Information Technology from the University of Parma. His postdoctoral research at the University of Parma (2012-2015) and a visiting scholar position at the University of Cambridge (2011) further enriched his expertise. He is nationally qualified as an Associate Professor by MIUR (2020). Education: PhD in Information Technology, University of Parma (Italy) M.Sc. (cum Laude) in Computer Engineering, University of Parma Visiting Researcher, NetOS Group, University of Cambridge (UK) His research focuses on Distributed Systems , IoT , Edge Computing , and Digital Twins , emphasizing their applications in smart industries and cities. He has supervised postdocs (e.g., Matteo Martinelli) and PhD students (e.g., Enrico Rossini) on topics like Digital Twin Continuum and Edge-Cloud Systems. Teaching spans courses on Intelligent IoT , Distributed IoT Software Architectures , and Edge Computing , with a focus on lab-based learning and industry-relevant projects. He actively contributes to open-source frameworks like the White Label Digital Twin (WLDT) and collaborates on initiatives such as the Web of Digital Twins (WoDT). Research collaborations include projects on smart city data fusion, livestock waste management, and Industry 5.0 human-centric systems. His work bridges theoretical advancements with practical implementations in cyber-physical environments.

Andrea MorichettaView profile

Associate Professor

• Blockchain Technology
• Smart Contracts
• Business Process Management
• +4 more

Andrea Morichetta is an Associate Professor at the University of Camerino (UNICAM). His research focuses on blockchain technology, smart contracts, and business process management. He explores the integration of blockchain with choreography-based systems, model-driven engineering, and process mining. His work addresses challenges in smart contract testing, distributed systems coordination, and auditability in decentralized applications. Key research areas include: Blockchain-based execution frameworks for BPMN choreographies Mutation testing strategies for Solidity smart contracts Decentralized identity systems using blockchain Event log analysis for Ethereum applications His publications highlight advancements in: Smart contract security and testing methodologies Choreography-driven architectures for IoT and MLOps Formal analysis of business process collaborations He contributes to international conferences and workshops on blockchain, enterprise modeling, and business informatics research. His work bridges theoretical foundations with practical implementations in distributed systems and process automation.

Michele GermaniView profile

Professor

• Industrial Engineering
• Ergonomics
• Sustainability
• +7 more

Prof. Michele Germani is a Full Professor at the Polytechnic University of Marche, Department of Industrial Engineering and Mathematical Sciences. His research focuses on ergonomics, sustainability, and human-centered design, with particular emphasis on industrial automation, wearable technologies, and circular economy principles. He leads projects on ergonomic risk assessment, environmental impact modeling, and smart manufacturing systems. Academic Background: Holds a position in the Faculty of Engineering, contributing to both teaching and research. His work integrates machine learning, sensor-based systems, and virtual reality to address challenges in manufacturing, healthcare, and sustainable design. Research Interests: Prof. Germani explores topics such as design for disassembly, eco-design tools, and the application of advanced technologies (e.g., exoskeletons, cobots) to enhance workplace safety and efficiency. His studies often bridge engineering and environmental science, aiming to reduce carbon footprints through innovative design methodologies. Key Projects: Development of decision support systems for fall risk detection, sensor-based ergonomic evaluation tools, and frameworks for circular economy implementation. He also investigates the impact of Industry 4.0 technologies on workforce dynamics and operator training via mixed reality simulations. Grants & Collaborations: Engaged in interdisciplinary collaborations focusing on sustainable manufacturing, healthcare technology, and smart systems. His research has led to tools like Durabot for durability analysis and Greenbuild for green building design. Labs & Teams: Oversees labs developing eco-design strategies, human-robot collaboration systems, and ergonomic assessment protocols, with a focus on real-world industrial applications.