Find Academic Supervisors

Battista BiggioView profile

Professor

• Machine Learning Security
• Adversarial Machine Learning
• Cybersecurity
• +4 more

Battista Biggio is a Full Professor at the University of Cagliari, Italy, affiliated with the Department of Electrical and Electronic Engineering under the Faculty of Engineering and Architecture. His research focuses on machine learning security, adversarial attacks, and cybersecurity. He co-founded the cybersecurity firm Pluribus One and has pioneered foundational work in poisoning attacks and adversarial robustness. Education: MSc (2006), PhD (2010). He holds editorial roles as Associate Editor-in-Chief for Elsevier's Pattern Recognition Journal and serves on IEEE TNNLS and IEEE CIM editorial boards. His awards include the 2022 ICML Test of Time Award and the 2021 Pattern Recognition Medal. He chairs IAPR TC1 and organizes conferences like S+SSPR and AISec. Research interests span adversarial machine learning, malware detection, and secure AI systems. He leads initiatives such as the sAIfer Lab and co-develops the SecML-Torch library. Teaching includes courses on Machine Learning Security and Industrial Software Development. Notable contributions include seminal papers like 'Poisoning Attacks against Support Vector Machines' and 'Wild Patterns.' He manages over 10 research projects and advises on AI security for industrial applications. His work bridges academic research with practical cybersecurity solutions.

Daniele IelminiView profile

Professor

• Non-volatile memory technologies
• Resistive switching memory (RRAM)
• Phase change memory (PCM)
• +5 more

Daniele Ielmini is a Professor at the Department of Electronics, Information and Bioengineering at Politecnico di Milano, Italy, where he leads research in non-volatile memory technologies and neuromorphic computing. He received his Laurea (with merit) and Ph.D. in Nuclear Engineering from Politecnico di Milano in 1995 and 2000, respectively, and has held visiting positions at Intel Corporation (2006), Stanford University (2006), and the University of Illinois at Urbana-Champaign (2010). His research focuses on the modeling and characterization of non-volatile memories, including nanocrystal memory, charge trap memory, phase change memory (PCM), resistive switching memory (RRAM), and spin-transfer torque magnetic memory (STT-MRAM). He has co-edited the book 'Resistive switching – from fundamental redox-processes to device applications' and published over 300 papers with more than 10,000 citations and an H-index of 69 (Scopus, September 2023). Prof. Ielmini's recent publications demonstrate a strong trend toward in-memory computing and neuromorphic applications, with particular emphasis on closed-loop analog computing architectures, reservoir computing with 2D materials, and hardware security implementations using emerging memory technologies. His work bridges fundamental device physics with practical computing applications, especially for energy-efficient AI acceleration. Intel Outstanding Researcher Award (2013) ERC Consolidator Grant (2014) IEEE-EDS Paul Rappaport Award (2015) Fellow of the IEEE Prof. Ielmini leads multiple ERC-funded projects including SHANNON (Secure Hardware with Advanced Nonvolatile memories), NEURO2D (neuromorphic systems based on reservoir computing in MoS2), and ANIMATE (closed-loop in-memory computing). His research group includes post-doctoral researchers, PhD students, and M.Sc. students working on various aspects of emerging memory technologies and their applications. He serves as Associate Editor for IEEE Trans. Nanotechnology and Semiconductor Science and Technology (IOP), and has served in several Technical Subcommittees of international conferences including IEEE-IEDM, IEEE-IRPS, and IEEE-ISCAS. His laboratory at Politecnico di Milano is equipped with advanced semiconductor device testing equipment including probe-stations, semiconductor parameter analyzers, high-speed waveform generators, and other specialized instruments for nano-electronic research. The lab collaborates with major semiconductor companies including Micron Technology Inc. and STMicroelectronics, as well as participating in national and international research projects.

• Artificial intelligence
• Data compression
• Deep learning
• +15 more

Enrico Magli is a Full Professor at the Department of Electronics and Telecommunications (DET) at Polytechnic University of Turin, Italy. He serves as Director of the Image Processing and Learning group and Coordinator of the 'ICT for Smart Societies' M.Sc. degree program. Additionally, he is a committee member of the PhD program in Electrical, Electronic and Communications Engineering and a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. Professor Magli's research focuses on applying machine learning and deep learning methods to satellite imaging, with applications to onboard processing and image analysis on the ground. His work spans deep learning for image and video analysis, image and video compression, compressive sensing, satellite imaging, and graph signal processing. He has published over 90 journal papers with 5900+ citations and an h-index of 40 on Google Scholar. His recent publications demonstrate a strong focus on developing deep learning architectures for satellite image processing, particularly for onboard applications. His research addresses challenges in hyperspectral image compression, super-resolution, change detection, and efficient neural network architectures suitable for resource-constrained satellite environments. There's also significant work on secure authentication systems using deep learning techniques and neural network optimization for edge devices. Elevated to IEEE Fellow (2017) 'for contributions to compression and communication of remotely sensed imagery' IEEE Geoscience and Remote Sensing Society 2011 Transactions Prize Paper Award IEEE Multimedia 2019 Best Paper Award Best Paper Awards at IEEE ICIP (2015, 2019) ERC Starting grant (consolidator type) and ERC Proof-of-Concept Grant recipient Multiple Best Paper Awards Francesco Carassa (2011, 2013, 2014) Professor Magli actively supervises numerous PhD students working on cutting-edge topics in deep learning for satellite imaging, image processing, and secure authentication systems. His research is supported by significant grants including ERC projects and multiple commercial contracts with space agencies and technology companies. He leads the Image Processing and Learning (IPL) Group at Politecnico di Torino, which focuses on developing innovative solutions for satellite image analysis and compression.

Luca SterponeView profile

Professor

• Reconfigurable computing
• Computer-aided design algorithms
• Fault tolerance and reliability
• +7 more

Luca Sterpone is a Full Professor at the Department of Control and Computer Science (DAUIN), Politecnico di Torino. He serves as Head of the Control and Computer Engineering Department (2023-2027), coordinates the Aerospace and Safety Computing Lab, and is a member of the Academic Senate and Power Electronics Innovation Center (PEIC). His research spans reconfigurable computing, fault tolerance, and radiation effects analysis in electronic systems. Professor since 2021 Department Head (DAUIN) since 2023 Coordinates international collaborations with ESA, AMD Xilinx, NVIDIA, and Thales Alenia Space Develops radiation-hardened FPGA tools (SETA, VERI-Place, PyXEL) 2007 EDAA Outstanding Dissertation Award and 2005 IEEE Best Paper Award Research Focus : Designing radiation-tolerant systems for aerospace, including fault-tolerant AI accelerators, FPGA reliability, and software-based error mitigation. He investigates soft error propagation in nanoscale circuits and develops tools for radiation sensitivity analysis in VLSI. His work integrates hardware-software co-design for mission-critical applications. Awards : EDAA Outstanding Dissertation Award (2007) IEEE European Test Symposium Best Paper (2005) SMACD Best EDA Tool Award (2018) ARC Best Paper candidate (2018) Teaching : He leads courses in Reconfigurable Computing (PhD level), GPU Programming , and Operating Systems . He has formal responsibility for teaching roles across 9 bachelor's and 7 master's years, and mentors multiple PhD students. Collaborations : Coordinates with the European Space Agency (ESA), University of Bielefeld, Universidad de Sevilla, and industrial partners like AMD Xilinx, NVIDIA, and General Motors. He leads projects such as RESCHIP4EU, VEGAS, and TERRAC for radiation-hardened computing solutions.

Sebastiano VASCONView profile

Associate Professor

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +4 more

Sebastiano Vascon is an Associate Professor at Ca' Foscari University of Venice's Department of Environmental Sciences, Computer Science and Statistics (DAIS), and affiliated with the European Center for Living Technology. He earned his PhD in 2016 from the Italian Institute of Technology and University of Genoa, focusing on evolutionary game theory in pattern analysis and computer vision. His postdoctoral work spanned institutions like the Technical University of Munich and ETH Zurich, where he specialized in Active Learning and multi-object tracking. His research merges AI with interdisciplinary challenges, including climate change, environmental science, and cultural heritage preservation. Key areas include graph neural networks, computer vision, and game-theoretic models. He leads projects like RePAIR (AI for cultural heritage reassembly) and EasyWalk (AI-driven mobility solutions), and contributes to initiatives like MEMEX (digital storytelling). Teaching spans courses in Deep Learning, Machine Learning for Environmental Applications, and AI in Cultural Management. Research projects include: RePAIR: AI-driven 3D puzzle solving for artifact reconstruction EasyWalk: Socially-aware navigation systems MEMEX: AI for inclusive digital storytelling Climate modeling with IceBoost framework Publications highlight innovations in trajectory forecasting, environmental risk assessment, and graph-based methods. He actively reviews for top conferences (CVPR, ECCV) and journals.

Dipanwita ThakurView profile

Assistant Professor

• Green Artificial Intelligence
• Federated Learning
• Smart Healthcare
• +4 more

Dr. Dipanwita Thakur serves as Assistant Professor at the Department of Computer Engineering, Modeling, Electronics and Systems (DIMES) at the University of Calabria, Italy since July 2023. She is an active member of the European Cooperation in Science & Technology (COST Action CA22104) focusing on cybersecurity and serves in the IEEE Future Networks Working Group for Artificial Intelligence/Machine Learning. Previously, she held a 15-year Assistant Professor position at Banasthali University, Rajasthan, and has industry experience at TechMahindra and C-DAC. Education: Ph.D. in Smart Healthcare from West Bengal University of Technology, Kolkata M.Tech. in Software Engineering from Banasthali Vidyapith MCA from NIELIT, Government of India B.Sc. from University of Calcutta Her research pioneers Green Artificial Intelligence with emphasis on energy-efficient federated learning and smart healthcare applications. She develops privacy-preserving human activity recognition systems using multimodal data fusion, focusing on performance evaluation and environmental sustainability. Her work bridges theoretical machine learning with practical healthcare solutions, optimizing AI systems for reduced carbon footprint while maintaining clinical efficacy through hardware-algorithm co-design and quantization techniques. Recent publications reveal a strong trajectory toward sustainable AI, with increasing focus on energy-aware federated learning frameworks, multimodal medical segmentation, and non-IID data handling. Her work consistently addresses the critical balance between model accuracy, convergence speed, and energy consumption across edge devices, with growing emphasis on quantization techniques and hardware-algorithm co-design for real-world deployment. Scientific Awards: Elevated to IEEE Senior Member (2024) Dr. B.C. Roy Memorial Scholarship for outstanding 10th Board results (1992) Student Science Seminar Award by West Bengal Government (1990) Dr. Thakur actively mentors students as evidenced by her congratulations to advisee Farwa for paper acceptances. She serves as Associate Editor for Information Fusion (Elsevier) and IEEE Sensors Journal, and holds editorial roles at Scientific Reports. Her research is advanced through COST Action CA22104 and IEEE working groups, though specific grant details aren't listed in the source material. She has organized key workshops including Green-Aware AI 2024 and Green Federated Learning at IJCNN 2025. She leads research within the MONAI community on data quality and federated learning, and contributes to IEEE IoT and Future Networks initiatives. Her work with the COST Action CA22104 Behavioral Next Generation in Wireless Networks connects cybersecurity with sustainable AI development, while her Missouri S&T visiting scholar position focuses on energy optimization for federated learning systems.

• Digital Circuits and Systems
• Energy-Efficient Circuits and Systems
• Embedded Systems
• +7 more

Massimo Poncino is a Full Professor at the Department of Control and Computer Science (DAUIN) within the Faculty of Engineering at Politecnico di Torino. He serves as Scientific Advisor for the STMicroelectronics partnership and coordinates basic engineering subjects. A Senior Member of IEEE since 2012 and Fellow since 2012, he has served on editorial boards for IEEE Transactions on Computer-Aided Design, IEEE Design & Test of Computers, and ACM Transactions on Design Automation. Education: Laurea in Electronic Engineering (1989) and PhD in Computer and Systems Engineering (1993) from Politecnico di Torino Academic Career: Visiting Scientist University of Colorado (1993-1994), Researcher at Politecnico di Torino (1995-2001), Associate Professor at University of Verona (2001-2004), Full Professor at Politecnico di Torino (2006-present) His research focuses on energy-efficient digital systems , including design automation of SoCs, hardware-aware AI, battery management, cyber-physical systems, and embedded systems. Recent publications highlight advancements in digital twins for batteries , low-power neural network deployment , and IoT privacy . Scientific Awards: Recognition of Service Award - ACM (2013) Certificate of Appreciation - IEEE Circuits and Systems Society (2006, 2008, 2009) IEEE Fellow (2012-) Research Involvement: EU H2020, VI/VII Framework Programs evaluator Scientific Director for projects: Approxim@ction, EMBAI, DISLO-MAN, DAMASCO Member of EDA research group Teaching: Course director for Energy Management for IoT (2019-2025) Lecturer for Computer Science courses (2003-2025)

Marco BuzzelliView profile

Assistant Professor

• Signal Processing
• Image Processing
• Video Processing
• +3 more

Marco Buzzelli is an Assistant Professor at the Department of Informatics, Systems and Communication (DISCo) at the University of Milan-Bicocca, where he also obtained his PhD in Computer Science in 2019. His academic career is centered around cutting-edge research in signal, image, and video processing with a specialized focus on color imaging and machine learning applications. Dr. Buzzelli's research interests span multiple interconnected domains within computer vision and image processing. He has established himself as a leading researcher in color constancy, with numerous publications exploring illuminant estimation, white balance algorithms, and perceptual aspects of color imaging. His work extends to video restoration, particularly addressing challenges in low-light conditions and HEVC-compressed video processing. Additional research areas include hyperspectral imaging applications for historical document analysis, food authentication technologies, and neural architecture search for various computer vision tasks. His publication record demonstrates a clear evolution from foundational work in logo recognition and saliency detection toward increasingly sophisticated approaches to color science and video processing. Recent work shows strong emphasis on uncertainty estimation in color constancy, Bayesian optimization for night photography, and multimodal approaches combining spectral information with traditional RGB imaging. His research often bridges theoretical advances with practical applications across diverse domains including cultural heritage preservation, food safety, and computational photography. As an active ELLIS member, Dr. Buzzelli maintains significant European collaborations with institutions including Universitat Autònoma de Barcelona, Universidade Nova de Lisboa, Université Jean Monnet, and Universidad de Granada. His research group participates in major challenges such as the NTIRE series on night photography rendering and spectral recovery, contributing both methodological innovations and comprehensive surveys of the field. His laboratory work focuses on developing practical imaging solutions with real-world applications, particularly evident in projects addressing food authentication, historical document analysis, and vision-based monitoring systems. The integration of traditional image processing techniques with modern deep learning approaches characterizes his methodological approach across multiple research domains.

• Machine Learning
• Signal Processing
• Telecommunications
• +3 more

Andrea Migliorati is a Fixed-term Assistant Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino. He specializes in telecommunications and industrial/information engineering (IINF-03/A - Area 0009), with a focus on machine learning applications in signal processing and neural network optimization. Academic Role: Assistant Professor Department: Electronics and Telecommunications Research Branch: Telecommunications (IINF-03/A) University: Politecnico di Torino Migliorati's research explores neural network sparsification, quantization techniques, and robust training methods for secure systems. His work bridges telecommunications engineering with modern machine learning, targeting applications in wearable biometrics and edge computing. Key trends include: Advancing sparse trainable neural networks through concave regularization Developing ternary/binary quantization approaches for model compression Enhancing gait identification systems for wearable devices Improving adversarial robustness via Gaussian class-conditional training

Marta CatilloView profile

Researcher

• Cybersecurity
• Machine Learning
• Intrusion Detection Systems
• +3 more

Marta Catillo is a Researcher at the Department of Engineering (DING) of the University of Sannio (UNISANNIO) . She specializes in Cybersecurity , with focus on Machine Learning applications for intrusion detection , IoT security , and cloud auto-scaling mechanisms . Her research addresses challenges in Denial of Service (DoS) mitigation , anomaly detection , and deep learning architectures for security. Teaching: [803004] PROGRAMMING 1 for Electronic and Biomedical Engineering students (2025 cohort) Contact: Office hours: Thursdays 3-5 PM, Room 23, Bosco Lucarelli Palace Research trends: From 2019-2025 publications, her work spans adversarial attack resistance , collective anomaly detection , outlier-aware architectures , and empirical analysis of defense mechanisms , with recurring collaborations with Antonio Pecchia , Umberto Villano , and Massimiliano Rak . Key methodologies include deep autoencoders , hybrid detection systems , and measurement-based security evaluation . Technical Contributions: Developed the ZED-IDS framework for zero-day threat detection, MultiCIDS for multivariate time series intrusion detection, and DEFEDGE for edge-cloud security testing.

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.

Roberto MenichettiView profile

Researcher

• Theoretical Physics
• Computational Physics
• Statistical Mechanics
• +7 more

Roberto Menichetti is a theoretical physicist at the University of Trento , affiliated with the Department of Physics and the VARIAMOLS research group . His work integrates computational physics, statistical mechanics, and machine learning to model biomolecules and soft matter systems. Prior roles include postdoctoral research at the Max Planck Institute for Polymer Research (2016-2018) and academic training at the University of Rome 'La Sapienza'. PhD in Physics (La Sapienza, 2016) BSc/MSc in Physics (La Sapienza, 2012) His research spans: Coarse-Graining Methods for polymers, colloids, and biomolecules High-throughput Molecular Dynamics Simulations in drug-membrane permeability studies Integration of Machine Learning with physical models for multi-scale analysis Applications in Biophysics (e.g., chromatin condensates, protein dynamics) Article trends highlight his focus on computational frameworks like EXCOGITO and theoretical investigations of non-equilibrium systems and allosteric regulation . He actively develops tools for systematic model reduction and conformational sampling in biomolecular systems. Contact: roberto.menichetti@unitn.it

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +3 more

Simone Bianco is an Associate Professor at the Department of Informatics, Systems and Communication (DISCo) of the University of Milano-Bicocca, Italy. His academic and research contributions span computer vision, artificial intelligence, machine learning, and optimization algorithms applied to multimodal and multimedia systems. His educational background includes a PhD in Computer Science (2010) and BSc/MSc degrees in Mathematics (2003/2006), both from the University of Milano-Bicocca. Bianco’s research focuses on color constancy, deep learning for video restoration, neural architecture search, and computational color imaging, with a strong emphasis on practical applications like biometric recognition, medical imaging, and environmental monitoring. The 15 most recent articles (2025–2020) highlight trends in computer vision, including uncertainty estimation in color constancy, portable material appearance modeling, temporal consistency in low-light videos, and advanced deep learning architectures for image and video processing. His work often integrates photogrammetry, sensor technology, and multimodal data analysis. Scientific accolades include recognition on Stanford University’s World Ranking Scientists List for achievements in artificial intelligence and image processing. Bianco also serves as R&D Manager for the University of Milano-Bicocca spin-off Imaging and Vision Solutions and contributes to international conferences and workshops.

Gian Luca MarcialisView profile

Associate Professor

• Biometrics
• Presentation Attack Detection
• Facial Recognition
• +4 more

Gian Luca Marcialis is an Associate Professor at the University of Cagliari, leading the Biometric Unit of the Pattern Recognition and Applications Laboratory (PRA Lab) within the Department of Electrical and Electronic Engineering. He holds an M.S. and Ph.D. in Electronic Engineering and Computer Science from the University of Cagliari (2000 and 2004). His research focuses on biometric security, including fingerprint presentation attack detection, facial deepfake detection, and crowd anomaly analysis. Key areas include multimodal biometric systems, liveness detection in authentication, and the ethical implications of AI in forensics. Marzialis has contributed to initiatives like the LivDet competitions and BullyBuster project against cyberbullying. His work integrates machine learning with real-world applications in surveillance, healthcare, and public safety, emphasizing both technical innovation and societal impact.

Marc MezardView profile

Professor

• Statistical Physics
• Machine Learning
• Information Theory
• +5 more

Marc Mezard is a Professor of Theoretical Physics at Bocconi University, where he leads the newly established Department of Computational Sciences. Previously, he served as Research Director at CNRS in Paris and held roles at Université Paris Sud. He earned his PhD in Physics from École Normale Supérieure in Paris in 1984. His research focuses on statistical physics of disordered systems, with applications to machine learning, information theory, computer science, and biophysics. His work bridges theoretical physics and interdisciplinary fields, including neural networks and deep learning, where he explores the impact of data structure on learning strategies. He teaches undergraduate courses in statistical and quantum physics and a doctoral course on complex systems. Key research themes include emergent phenomena in complex systems, with contributions to spin glass theory, compressed sensing, and algorithmic solutions for random satisfiability problems. His publications span foundational topics in statistical mechanics and modern applications in data science. Marc Mezard has collaborated extensively with institutions and researchers globally, contributing to the theoretical foundations of computational and physical sciences. His academic leadership includes directing École Normale Supérieure from 2012 to 2022, fostering interdisciplinary research initiatives.