Find Academic Supervisors

• Sparse optimization
• Machine learning
• Convex and nonconvex optimization
• +7 more

Sophie Fosson is an Associate Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, Italy. She is a member of the System Identification & Control (SIC) research group and teaches courses such as Automatic Control and Modeling and Control of Cyberphysical Systems at both the master's and doctoral levels. Education: PhD in Mathematics for Industrial Technologies, Scuola Normale Superiore di Pisa Master’s in Mathematical Engineering, Politecnico di Torino Her research focuses on sparse optimization, machine learning, system identification, and control theory . She integrates mathematical modeling with practical applications in cyber-physical systems and neural networks. Her work emphasizes efficient and robust optimization techniques for deep learning and control systems. The recent publications highlight a strong trend in optimization of neural networks , particularly through sparse training, binary and ternary quantization, self-supervised learning, and fault tolerance . Her research bridges control theory with machine learning, applying mathematical rigor to modern AI challenges in safety-critical and embedded environments. Scientific Awards: No awards explicitly mentioned. She supervises several PhD students including Alice Re, Rosario Milazzo, and Simone Pirrera , and has been involved in research projects such as CRISP and collaborations with Centre Tecnològic de Telecomunicacions de Catalunya . She has held postdoctoral positions at DISMA and DET (Politecnico di Torino) and worked as a researcher at Istituto Superiore Mario Boella. Laboratories and Research Groups: System Identification & Control (SIC), DAUIN, Politecnico di Torin

Salvatore BarbaView profile

Professor

• Architectural Drawing
• Conservation and Restoration
• Remote Sensing
• +8 more

Salvatore Barba is a Full Professor in the Department of Civil Engineering at the University of Salerno, Italy, where he conducts research and teaching in architectural surveying, cultural heritage documentation, and digital reconstruction. He also serves as a Contract Professor at the Faculty of Architecture, University of Basilicata, Matera. His work bridges engineering, architecture, and heritage science, focusing on advanced digital methodologies. Research interests: His expertise lies in photogrammetry , remote sensing , 3D laser scanning , GIS-BIM integration , and augmented reality for cultural heritage. He investigates degradation diagnostics, low-cost survey tools, and micro-photogrammetry, emphasizing interdisciplinary and methodologically rigorous approaches to digital documentation. Publications and technology: His recent articles highlight the application of UAVs, wearable SLAM scanners, and handheld microscopes in heritage contexts. He validates instruments like the Shining 3D EinScan-Pro and develops protocols for high-accuracy documentation. His work consistently underscores the importance of expert interpretation over purely automated processes in surveying. Scientific Awards: No specific awards mentioned in the provided text. Advising and grants: While specific students and grants are not listed, he collaborates on international projects, participates in technology transfer agreements (e.g., with Dino-Lite), and contributes to methodological advancements in heritage science. His role as a faculty member implies advising graduate students in civil engineering and architectural documentation. Labs and teams: He is involved in research activities related to digital heritage documentation, likely within laboratory facilities at the University of Salerno. His collaborative work with researchers across Europe and on diverse heritage sites indicates active participation in interdisciplinary research teams focused on advanced surveying and conservation technologies.

• Chaos theory
• Compressed sensing
• Electromagnetic interference
• +14 more

Fabio Pareschi is an Associate Professor at the Department of Electronics and Telecommunications (DET), Politecnico di Torino, where he conducts research in circuit architectures, embedded systems, and signal processing with applications in security, AI, and power electronics. He is affiliated with the VLSILAB research group and leads multiple high-impact research projects. Research Interests: Chaos theory and true random number generation for cryptographic applications Compressed sensing for secure and efficient signal acquisition EMI reduction techniques in DC-DC power converters Tiny machine learning and low-power embedded systems Circuit design for IoT and biomedical applications The recent articles highlight a strong trend in integrating compressed sensing with encryption, leveraging chaos-based randomness for security, and optimizing power electronics for EMI reduction. His work bridges theoretical foundations with practical hardware implementations in microelectronics and embedded systems. Scientific Awards: Best Student Paper Award (IEEE, 2005) Best Paper Award (IEEE, 2005) IEEE PRIME Gold Leaf Certificate (2019) BioCAS Transactions Best Paper Award (2019) Best Student Paper Award at EMCCompo (IEEE, 2019) Advising and Grants: He supervises multiple PhD students in the Electrical, Electronics, and Communications Engineering program. He is the Scientific Director of the CESOIA project (Non-EU International Research, 2025–2028) on low-complexity AI models, and leads the ECS4DRES project (EU-funded, 2024–2027) on resilient energy systems. He also heads a commercial research project on high-performance DC-DC converters (2022–2025). His editorial roles include Associate Editor for IEEE Transactions on Circuits and Systems and guest editorships in multiple IEEE journals. Labs and Teams: He is a key member of the VLSILAB Group (DET), which focuses on VLSI systems, embedded signal processing, and secure hardware design.

Michael ShekelyanView profile

Lecturer

• Data Management
• Database Systems
• Differential Privacy
• +5 more

Dr. Michael Shekelyan is a Lecturer (Assistant Professor) in Computer Science at Queen Mary University of London (2023–present), part of the School of Electronic Engineering and Computer Science. He holds a PhD in Computer Science from the Free University of Bozen-Bolzano (2018) and a Diploma in Media Informatics from Ludwig Maximilian University of Munich (2014). Previously, he worked as a Research Associate at King's College London (2021–2023) and a Research Fellow at the University of Warwick (2018–2021). His academic journey includes roles as a Meta-Reviewer for NeurIPS and ICDT Proceedings Chair (2024), with extensive service as a reviewer for top conferences like ICML, NeurIPS, ICLR, and journals such as TKDE and IEEE T-IFS. Research Interests: Focuses on algorithms and data structures for managing large/sensitive datasets, including differential privacy, random sampling (e.g., Hidden Shuffle Method), and multidimensional data summarization (e.g., DigitHist). His work bridges theoretical guarantees with practical applications in data management and machine learning. Notable contributions include privacy-preserving top-k selection, efficient sampling over joins, and error-bounded data summaries. Publications: Over 15 peer-reviewed articles in top venues including SIGMOD, ICDE, EDBT, AISTATS, and PVLDB. Key works include 'Streaming Weighted Sampling over Join Queries' (EDBT'23), 'Sequential Random Sampling Revisited' (AISTATS'21), and 'Sparse Prefix Sums' (Information Systems'19, which won an ADBIS award). Grants/Awards: Received the ADBIS'17 Award for Sparse Prefix Sums. Active in organizing conferences and mentoring early-career researchers through reviewing roles. Current projects include a PhD studentship on privacy-preserving algorithms for medical data sharing. Labs/Teams: Leads the Privacy-Preserving Algorithms initiative at Queen Mary, collaborating with international research networks in database systems and data privacy.

• Structural Health Monitoring
• Earthquake Engineering
• Satellite Data Integration
• +7 more

Stefania Coccimiglio is a Research Fellow and Research Assistant at the Department of Structural, Geotechnical and Building Engineering (DISEG) at Politecnico di Torino. She also serves as an external lecturer and teaching assistant in the Master's Degree Course in Earthquake Engineering. Her work is centered on structural health monitoring, seismic monitoring, and the integration of satellite data for civil and heritage infrastructure assessment. Her research interests include Structural Health Monitoring , Earthquake Engineering , Satellite Data Integration , Soil-Structure Interaction , Modal Analysis , and Built Heritage Conservation . She applies advanced data fusion techniques and remote sensing to monitor the dynamic behavior of civil structures, particularly monumental and historical buildings. Her recent publications (2021–2025) demonstrate a strong focus on using interferometric satellite data (e.g., Copernicus), in situ measurements, and machine learning for structural monitoring. She frequently publishes in journals such as Scientific Reports , Geosciences , and Bulletin of Earthquake Engineering , and presents at key conferences including the European Workshop on Structural Health Monitoring and the International Brick and Block Masonry Conference. No scientific awards are explicitly mentioned in the provided texts. She has collaborated extensively with researchers such as Rosario Ceravolo, Gaetano Miraglia, Giorgia Coletta, and Erica Lenticchia. Although no formal advisees or grants are listed, her role as a recent doctoral graduate and active researcher suggests ongoing contributions to research teams and projects. She does not appear to lead a lab but is an integral member of research groups focused on structural dynamics and monitoring at PoliTO.

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 .

Matteo LapucciView profile

Research Fellow

• Optimization
• Machine Learning
• Statistics
• +4 more

Matteo Lapucci is a Research Fellow (RTD/A) at the Department of Information Engineering, University of Florence, where he conducts advanced research in mathematical optimization and its interdisciplinary applications. His position situates him within the university's engineering research ecosystem, focusing on algorithmic innovation and theoretical development. His research program centers on complex optimization frameworks including: Non-linear Optimization Constrained Optimization Multi-objective Optimization Sparse Optimization with direct applications in machine learning model training, statistical inference, and large-scale data analysis. His work bridges theoretical mathematics with practical computational challenges in artificial intelligence, emphasizing efficiency and scalability for real-world implementation. Dr. Lapucci maintains active scholarly visibility through his Google Scholar profile and ORCID identifier (0000-0002-2488-5486), with physical presence at Via di Santa Marta, 3 – 50139 Florence, Italy. His research trajectory demonstrates consistent specialization in optimization theory with growing impact in data science applications.

• Antenna Theory and Design
• Electromagnetic Fields
• Quantum Electromagnetics
• +7 more

Nicola Anselmi is a Researcher (RTD-A) at the University of Trento, affiliated with the Department of Civil, Environmental and Mechanical Engineering. He also contributes to the Department of Physics through teaching and research. His work focuses on advanced antenna array design, quantum computing applications in electromagnetics, and modular phased array architectures. Key research areas include tolerance analysis of reconfigurable systems, compressive sensing techniques for antenna characterization, and optimization strategies for next-generation wireless communication systems. He teaches courses such as Antenna Theory and Synthesis Methods (Civil Engineering) and Quantum Electromagnetics (Physics), emphasizing theoretical foundations and practical software applications. His research leverages quantum computing for solving complex electromagnetic problems and explores novel array configurations for low Earth orbit (LEO) satellite communications and urban wireless networks. Anselmi’s recent work highlights advancements in interval arithmetic for robust array tolerance analysis, Bayesian compressive sensing for microwave imaging, and self-replicating tiling techniques for modular array design. His publications span IEEE journals and conferences, addressing topics like electromagnetic environment optimization, sparse array synthesis, and AI-driven antenna optimization. Though no scientific awards are explicitly mentioned, his contributions reflect cutting-edge innovations in electromagnetics and quantum engineering. His research also involves collaborations with the ELEDIA Research Center, focusing on task-oriented reflectarrays and system-by-design methodologies for multi-scale applications. Current interests include overcoming electromagnetic challenges in smart cities and next-generation radar systems.

Pia AddabboView profile

Lecturer

• Remote Sensing
• Geophysics
• Environmental Monitoring
• +3 more

Pia Addabbo is a Lecturer at the Department of Engineering , University of Sannio , with expertise in remote sensing and satellite data analysis. Her work focuses on GNSS reflectometry, wind speed estimation, and hyperspectral imaging applications. Department: Engineering Academic Rank: Lecturer Research Interests: Addabbo's research spans Remote Sensing , Geophysics , Environmental Monitoring , and Signal Processing . She has contributed to: GNSS reflectometry for ocean wind speed retrieval UAV systems in photovoltaic plant maintenance Phase altimetry techniques for surface height measurement Sparse learning algorithms in radar profiling Publication Trends (2015–2024): 15 articles highlight her focus on satellite-based environmental monitoring (CYGNSS, Sentinel-1), microwave reflectometry , and machine learning applications in remote sensing. Key collaborations include Silvia Ullo, Maurizio Di Bisceglie, and Carmela Galdi.

Christian MicheloniView profile

Associate Professor

• Pattern Recognition
• Active Vision
• Neural Networks
• +3 more

Christian Micheloni is an Associate Professor at the University of Udine's Department of Mathematics and Computer Science. He holds a Laurea (cum Laude) and Ph.D. in Computer Science from the University of Udine (2002 and 2006 respectively). His research focuses on Active Vision, Pattern Recognition, and Resource Aware Networks. He has been involved in European projects like MUSAS (2008-2010) and HAMLet (2006-2008), and collaborates with international research centers and industry. Key research areas include person re-identification using neural networks, trajectory analysis, and camera network optimization. He has received the Best Paper Award at the International Conference on Distributed Smart Cameras (2014) and serves as an editor for the IEEE Journal of Selected Topics in Signal Processing. His work emphasizes real-time surveillance systems and adaptive sensor networks to manage computational and power constraints. Micheloni teaches courses like Artificial Vision and advises post-doctoral researchers. He maintains an active lab (AVires Lab) and offers post-doctoral positions. His publications span journals like IEEE Transactions on Pattern Analysis and Machine Intelligence, and conferences such as CVPR and ICCV.

Walter BresciaView profile

Researcher

• Control Strategies for Autonomous Vehicles
• Reinforcement Learning
• Machine Learning
• +3 more

Walter Brescia is a Researcher at the Department of Electrical and Information Engineering (DEI) at Politecnico di Bari , Italy. He is a member of the Control Engineering and Computer Science Laboratory (C3Lab), focusing on interdisciplinary research at the intersection of robotics, machine learning, and network-aware systems. Research Interests include: Control strategies for industrial autonomous vehicles Reinforcement learning and machine learning for robotics Network-aware perception systems mmWave radar processing Multimodal perception datasets Real-time teleoperation systems Recent Publications span robotics control, sensor fusion, and networked systems, with a focus on practical applications in autonomous navigation and immersive teleoperation. His work has been presented at top-tier conferences like IFAC WC, IEEE MED, and ACM MMSys. Awards : Best Demo Paper, ACM Multimedia Systems (MMSys) 2025 Collaborations include active partnerships with researchers in robotics and networking domains.

Bruno CarpentieriView profile

Associate Professor

• Numerical Linear Algebra
• High-Performance Computing
• Computational Electromagnetics
• +5 more

Bruno Carpentieri is an Associate Professor in Applied Mathematics at the Faculty of Computer Science , Free University of Bozen-Bolzano , Italy. His work focuses on Numerical Linear Algebra , High-Performance Computing , and Preconditioning Techniques for large-scale scientific problems. Education : Laurea in Applied Mathematics (1997, University of Bari Aldo Moro); PhD in Computer Science (National Polytechnic Institute Toulouse). Research Interests : Sparse linear systems, Krylov subspace methods, computational electromagnetics, PageRank problems, and fractional calculus applications in nonlinear engineering. His publications (1998–2025) emphasize efficient solvers for multi-shifted systems, parallel computing, and preconditioning strategies. Recent works (2025) explore fractional-order methods and multilayer network centrality . He has collaborated on ITER tokamak simulations (F4E/MIUR grants) and biomedical finite element models . Grants : F4E-2008-OPE-06-06-11 (ITER analysis); MIUR PRIN 2010SPS9B3. Key Contributions : VBARMS preconditioner (2014), hybrid block GMRES variants (2018), and chaos-enhanced fractional solvers (2025).

Marcello RestelliView profile

Professor

• Reinforcement Learning
• Machine Learning
• Artificial Intelligence
• +3 more

Marcello Restelli is a Professor at the Department of Electronics, Information, and Bioengineering (DEIB) of Politecnico di Milano, affiliated with the School of Industrial and Information Engineering. His research focuses on Reinforcement Learning (RL), Machine Learning, Robotics, and Control Systems. He explores challenges in real-world RL applications, multi-agent systems, and optimization under uncertainty. Key contributions include developing scalable algorithms for sparse-reward environments, POMDPs, and safe robotics. He leads projects like the EU-funded I3LUNG initiative, integrating AI for personalized lung cancer care. His work bridges theory and practice, addressing topics such as exploration strategies, regret minimization, and causal feature selection. He has contributed to frameworks like Gym4ReaL for benchmarking real-world RL systems and Storehouse for warehouse optimization. Recent efforts emphasize energy systems, financial decision-making, and explainability in extreme event prediction. His publications highlight advancements in inverse RL, online learning, and transfer learning. Collaborations span academia and industry, reflecting his impact on both theoretical and applied AI domains.

Giuliano GrossiView profile

Associate Professor

• Sparse representation models
• Signal processing
• Face recognition
• +5 more

Giuliano Grossi is an Associate Professor at the Department of Computer Science, University of Milan. His research focuses on sparse representation models, signal processing, and GPU algorithms with applications in biomedical engineering and affective computing. He leads projects such as 'Stairway to Elders' for elderly wellbeing and contributes to tools like pyVHR for remote health monitoring. Grossi has supervised PhD students in areas including high-performance computing and stochastic models. Notable awards include Best Paper recognitions in 2017 and 2020. His work spans interdisciplinary fields from bioinformatics networks to facial recognition systems. Education: PhD in Computer Science (1999), University of Milan Positions: Assistant Professor (2001-2022), Associate Professor (2022–present) Research Interests: Sparse recovery methods, parallel algorithms, emotion recognition via multimodal analysis, and graph-based models for learning. Projects include EU T-NOVA for network virtualization and emotion interpretation frameworks funded by MIUR. Scientific Contributions: Over 100 publications in journals like IEEE Transactions and conference proceedings. Key contributions include GPU-accelerated algorithms, network biology analysis tools (UNIPred-Web), and frameworks for photoplethysmography (pyVHR).

Gabriel E. HineView profile

Researcher

• Biometrics
• Signal Processing
• Information Security
• +13 more

Gabriel E. Hine is a Postdoc researcher at the Biometric Systems and Multimedia Forensics Lab within the Department of Engineering at Roma Tre University, Italy. He is actively engaged in research at the intersection of signal processing, biometrics, and information security, and contributes to teaching in advanced signal processing and biometric systems. Bachelor’s Degree in Electronic Engineering, Roma Tre University (2013, cum laude) Master’s Degree in Information and Communication Technology Engineering, Roma Tre University (2015, cum laude) PhD in Applied Electronics, Roma Tre University (2018) Research collaboration at Telefonica I+D, Barcelona (2016) Research involvement in EU H2020 ENCASE and PRIN 2011/2012 projects His research focuses on biometric cryptosystems, template protection, and privacy-preserving biometric recognition, with significant contributions to fingerprint, palmprint, EEG, and signature-based systems. He has also conducted influential work on online social media dynamics, particularly analyzing 4chan’s /pol/ forum. The analysis of his recent publications reveals a strong trend in developing secure and unlinkable biometric templates using deep learning, sparse signal representations, and zero-leakage frameworks. His work spans both theoretical formulation and experimental validation on real-world data, particularly in finger-vein and palm-vein recognition. He also explores novel biometric modalities such as in-air 3D signatures and resting-state EEG. Best Paper Runner-Up, ICWSM-17 (2017) Selected as 'Best of Arxiv' paper Widely covered in Nature , MIT Technology Review , The Independent , Vice , and Italian media Hine has served as a lecturer and teaching assistant for courses including 'Biometric Systems' and 'Signal Theory' at Roma Tre University. He has led a didactic laboratory on biometric systems, guiding students in implementing and analyzing biometric recognition solutions. His research is supported by major grants such as EU H2020 ENCASE and PRIN 2011/2012. He collaborates extensively with Prof. Patrizio Campisi and other researchers in the BioMedia4n6 lab. He is a core member of the BioMedia4n6 research group, which specializes in biometrics, data hiding, and blind deconvolution, applying advanced signal processing techniques to multimedia forensics and security challenges.