Elena Maria Baralis is a Full Professor at the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin. She serves as Pro-Rector, member of the Board of Directors (without voting rights), member of the Academic Senate (without voting rights), and coordinator of the University's Permanent Observatory for Monitoring the Academic Sector. She chairs the Control and Computer Engineering Department and previously chaired the Computer Engineering School from October 2012 to October 2018. Her research interests focus on database systems and data mining, specifically explainable AI, bias detection in data analytics, and machine learning algorithms for big data. Her work spans various application domains including predictive maintenance, Industry 4.0, and healthcare. Recent publications demonstrate her expertise in speech processing, bias mitigation, and innovative neural network architectures like Kolmogorov-Arnold Networks. Her research output shows a clear trend toward addressing fairness and explainability in AI systems while exploring novel approaches to speech and language understanding. Professor Baralis has received significant recognition including becoming a Fellow of the Academy of Sciences of Turin in 2017. She has served as Editor-in-Chief for IEEE Internet of Things Journal (2016-2019) and Knowledge and Information Systems (2014-present). She actively mentors doctoral students including Claudio Savelli (researching Machine Unlearning), Eleonora Poeta, Giuseppe Gallipoli, Alkis Koudounas, and others. Her research is supported by numerous projects including AI4CTI (Artificial Intelligence for Cyber Threat Intelligence, 2025-2028), Smart manufacturing driven by Machine Learning in Industry 4.0 (2019-2020), and I-REACT (2016-2019).
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)
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.
Giuseppe Santucci is an Associate Professor at the Department of Computer, Control and Management Engineering Antonio Ruberti at Sapienza University of Rome. He teaches courses on Fundamentals of Computer Science, Software Engineering, and Visual Analytics. His office is located in Room B218 at Via Ariosto 25, Rome, and his contact email is santucci@diag.uniroma1.it. Dr. Santucci's research focuses on Visual Analytics, Information Visualization, Human-Computer Interaction, and Information Retrieval. His work spans theoretical aspects of visual query languages for semantic models to practical applications in visual analytics for cybersecurity, cryptocurrencies, and deep learning explainability. He has published over 130 articles in international journals and conferences, demonstrating his significant contributions to these fields. His recent publications show a strong trend toward applying visual analytics to increasingly complex domains including cybersecurity, cryptocurrencies, and explainable AI. The work demonstrates an evolution from theoretical foundations of visual query systems to practical applications that help users understand complex data and systems. His research bridges the gap between theoretical computer science and practical user-centered solutions. Dr. Santucci has received notable recognition including: IEEE VizSec 2018 Best Paper Award Human-Computer Interaction Cybersecurity Awards 2018 He actively mentors students through thesis projects focused on information visualization and visual analytics. His PROMISE project provides a framework for students to engage in cutting-edge research in information retrieval and visual analytics. He has supervised work on topics including visual evaluation techniques, visual mappings optimization, and user studies for Infovis systems. Dr. Santucci leads the A.WA.RE (Advanced Visualization & Visual Analytics REsearch) group at Sapienza University. This group conducts research on visual analytics tools for information retrieval evaluation, cybersecurity analysis, and deep learning explainability. Their work includes developing frameworks like CryptoComparator for cryptocurrency analysis and BUCEPHALUS for cybersecurity platform analysis.
Giacomo Fiumara is an Associate Professor at the University of Messina, Department of Mathematical and Computer Sciences, Physical Sciences and Earth Sciences. He holds academic rank since October 2021. Previously, he served as a Permanent Researcher (2008–2021) and secondary school teacher (1997–2008). He earned a Doctorate in Physics (1993) and a Degree in Physics (1989), both from the University of Messina. He is an associate member of the Accademia Peloritana dei Pericolanti and qualified as an associate professor in INF/01 and ING-INF/05 sectors. His research focuses on social network analysis, network science, data science, criminal networks, knowledge representation, bioinformatics, and computational modeling. He has supervised over 170 theses and advised PhD students in Mathematics and Computational Sciences. Key collaborations include work with Prof. Pasquale De Meo on criminal networks and complex systems, and international projects with institutions in the US, UK, China, and Australia. Teaching includes courses on Algorithms, Data Structures, Bioinformatics, and Machine Learning across Computer Science, Engineering, and Medical programs since 2000. He also contributed to international programs at Lviv Polytechnic, Birzeit University, Cluj-Napoca, and Murcia. His editorial roles include Associate Editor of IEEE Access and Academic Editor of Complexity. He holds a patent for predictive analysis of criminal organizations' social structures and has received FFABR research funding. Key awards include FFABR funding (2017) and recognition in the FFABR Unime 2020 II edition. He organized conferences like Crimenet 2014 and participated in high-profile events such as the 2022 Complex Networks conference in Palermo, presenting on quantum walks for criminal network analysis.
Andrea Santilli is a Research Scientist at Nous Research and holds a PhD in Computer Science from GLADIA at Sapienza University of Rome. His research focuses on large language models (LLMs), robustness, reliability, and multimodal learning. He previously worked at Apple MLR, Hugging Face’s BigScience, and Pi School. He earned his MSc and BSc in Computer Science from Tor Vergata University and Sapienza. Education: PhD in Computer Science, Sapienza University of Rome (2024) MSc in Computer Science, University of Roma Tor Vergata (2020) BSc in Computer Science, University of Roma Tor Vergata (2018) Research Interests: Santilli’s work spans LLM robustness , mechanistic interpretability , multimodal neural databases , and instruction-tuning . He introduced Parallel Jacobi Decoding and contributed to projects like BLOOM, Camoscio, and Fauno. His research bridges syntax-aware NLP, privacy-preserving LLMs, and cross-modal alignment. Publications: His work includes advancements in 3D-text latent space alignment (CVPR 2025), evolutionary merging (ICML 2025), and efficient decoding (ACL 2023). Over 15+ peer-reviewed papers span venues like ACL, CVPR, and ICLR. Awards: Received the Emanuele Pianta Award for his MSc thesis on continual language learning with syntax-based episodic memory. Grants & Projects: Winner of ‘Machine Learning Algorithms for Translation’ grant (2022), developing Parallel Decoding Co-PI for ‘Multimodal AI for 3D Analysis’ (2021) with Ecole Polytechnique Labs & Teams: Active in GLADIA (Sapienza), Apple MLR, and Hugging Face’s BigScience initiative. Core contributor to open-source projects like PromptSource and BLOOM.
Damiano Piovesan is Associate Professor in Bioinformatics (SSD BIO/10) at the Department of Biomedical Sciences , University of Padua , Italy. Since March 2022 he has held this rank, having previously served as Assistant Professor (2022) and PostDoc researcher (2019) in the same department. Education 2013 – PhD in Biotechnology, Pharmacology and Toxicology, University of Bologna 2009 – MSc in Bioinformatics, University of Bologna 2007 – BSc in Biotechnology, University of Bologna Research Focus Piovesan’s research integrates machine-learning approaches with structural bioinformatics to advance understanding of intrinsically disordered proteins (IDPs) and protein function prediction . He develops widely used resources such as MobiDB for disorder annotation, DisProt for functional curation of disordered regions, and RING for residue interaction networks. Additional interests include tandem repeat proteins , cancer-related IDP targets , and community benchmarking initiatives (CAFA, CAID, CAGI). Publication Trends His 2024–2025 output is dominated by updates to flagship databases ( InterPro , DisProt , MobiDB ), next-generation disorder predictors leveraging deep learning ( PredIDR , MobiDB-lite 4.0 ), and large-scale genomics challenges ( CAGI6 ). Across the decade, recurring themes include methodological advances in disorder prediction, creation of interoperable bioinformatics platforms, and rigorous benchmarking to ensure community-wide reliability. Scientific Awards No specific awards are listed in the provided materials. Advising & Grants No individual students or grant details are explicitly supplied; however, his leadership in multi-institutional consortia (e.g., InterPro, DisProt, CAFA) implies substantial supervisory and funding coordination roles. Labs & Teams Piovesan is affiliated with the BioComputingUP Lab ( https://biocomputingup.it/ ) at the University of Padua, a hub for computational biology and bioinformatics tool development.
Fabrizio Falchi is a researcher at the Artificial Intelligence for Media and Humanities (AIMH) Lab of the Institute of Information Science and Technologies (ISTI) within Italy's National Research Council (CNR). He also maintains an associate position at the Biorobotics Institute of Scuola Superiore Sant'Anna. His work focuses on developing advanced multimedia retrieval systems, with the VISIONE platform being his most notable contribution, which has won international competitions including the Video Browser Showdown in 2024 and placed second in 2023. Falchi's educational background includes: Ph.D. in Information Engineering from University of Pisa (Italy) Ph.D. in Informatics from Faculty of Informatics of Masaryk University of Brno (Czech Republic) M.B.A. from Scuola Superiore Sant'Anna in Pisa His research spans deep learning, convolutional neural networks, deep features extraction, similarity search algorithms, distributed indexing systems, multimedia information retrieval, computer vision applications, and peer-to-peer systems. Falchi has made significant contributions to fine-grained visual understanding, cross-modal retrieval (particularly image-text matching), and robustness of deep learning systems against adversarial attacks. His work demonstrates a strong focus on practical applications of these technologies, particularly in video retrieval systems and safety monitoring solutions. Analysis of Falchi's recent publications reveals a strong focus on video and image retrieval systems, with the VISIONE platform being central to his work. His research shows increasing emphasis on fine-grained understanding in computer vision, cross-modal retrieval, and addressing practical challenges like cross-resolution face recognition. Recent work demonstrates innovation in making these systems more efficient through techniques like knowledge distillation (ALADIN) and leveraging virtual worlds for training data. His publications consistently bridge theoretical advances with practical applications in surveillance, safety monitoring, and multimedia search. Falchi's work has received significant recognition: Best paper award at CBMI 2024 for 'Is ClLIP the main roadblock for fine-grained open-world perception?' VISIONE 2024 won the Video Browser Showdown competition in Amsterdam VISIONE obtained second place at Video Browser Showdown 2023 in Bergen Best Paper Award for 'Learning Safety Equipment Detection using Virtual Worlds' at CBMI 2019 Falchi collaborates extensively with researchers at ISTI-CNR, particularly within the AIMH Lab. His work on VISIONE involves collaboration with Giuseppe Amato, Paolo Bolettieri, Fabio Carrara, Claudio Gennaro, Nicola Messina, Lucia Vadicamo, and Claudio Vairo. As co-chair of Ital-IA 2023, the 3rd National Conference on Artificial Intelligence, he plays an active role in the academic community. He is a member of ACM (since 2012), the Computer Vision Foundation, the Italian Association for Computer Vision Pattern Recognition and Machine Learning (CVPL), and the CINI Lab on Artificial Intelligence and Intelligent Systems. Falchi is a key member of the Artificial Intelligence for Media and Humanities (AIMH) Lab at ISTI-CNR, where he leads research on video retrieval systems. The lab has developed the award-winning VISIONE platform, which combines multiple scientific results in content-based video retrieval. His team focuses on developing systems that enable users to search for target videos using textual prompts, drawing objects and colors, or images as query examples. The lab's work demonstrates strong interdisciplinary collaboration, bridging computer science with practical applications in media, safety monitoring, and urban environments.
Tania Cerquitelli is a Full Professor in the Department of Control and Computer Science (DAUIN) at Politecnico di Torino, where she leads research in data science, concept-drift management, and inclusive AI technologies. She is a member of SmartData@PoliTO, the GEDI Observatory for Gender Equality, and serves in leadership roles related to social affairs and community policies at the university level. She also acts as a scientific advisor for the partnership with Accenture. Her research interests span Data Science , Concept-Drift Management , Database Systems , Conversational Data Science , and Industry 4.0 . She applies AI and machine learning to industrial, societal, and ethical challenges, particularly in promoting inclusive communication and gender equality in research. The most recent publications highlight her work in explainable AI, concept drift detection, multimodal diagnostics, and AI for social good. Her research integrates machine learning, natural language processing, and computer vision to address real-world problems in manufacturing, healthcare, agriculture, and education. She is an Associate Editor for several prestigious journals including Expert Systems with Applications , Computer Networks , Future Generation Computer Systems , and Knowledge and Information Systems . She has served on the program committees of major conferences such as ECML PKDD, EDBT/ICDT, and ACM KDD, and has been a reviewer and selection committee member for ETH Zurich and EMPA. She actively supervises PhD students and teaches a wide range of courses including Data Science and Database Technologies, Business Intelligence for Big Data, and Gender and Diversity in Research. She is involved in multiple national and international research projects such as E-MIMIC, WEBFARE, and EnABLES, focusing on inclusive AI, smart data, and industrial applications. Her lab affiliations include the DBDM - Database and Data Mining Group (DAUIN) and the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory , where she contributes to advancing data science methodologies and their societal impact.
Filippo Ubertini is Professor of Civil and Environmental Engineering at the University of Perugia, Italy, where he coordinates the International Doctoral Programme in Civil & Environmental Engineering and represents the University inside the FABRE national bridge-research consortium. He leads the Structural Health Monitoring Laboratory ( SHM-Lab ) and is the primary contact for assignments linked to smart-infrastructure research. Education: While explicit degrees are not listed in the supplied text, his role as programme coordinator and full professor implies completion of a PhD and habilitation in Civil Engineering. Research focus: Ubertini’s work sits at the intersection of smart materials and data-driven infrastructure management . He develops self-sensing cementitious composites doped with carbon micro-fibers or graphene nano-platelets that can measure strain, cracking and moisture in real time, turning whole bridges and buildings into distributed sensors. Complementary research threads include low-cost acquisition electronics, UAV & InSAR remote sensing, Bayesian & adversarial machine-learning algorithms for damage detection, digital twins and life-cycle cost analysis of bridge networks. Publication trends (2024-2025): Roughly 30 peer-reviewed items per year concentrate on (i) AI-enhanced operational modal analysis and transfer-learning damage classification across bridge populations, (ii) experimental characterisation of 3D-printed and cast self-sensing concrete, (iii) full-scale validation on curved box-girder, masonry and railway bridges, and (iv) integration of satellite radar data with numerical collapse simulations to predict residual service life of landslide-affected viaducts. Scientific awards & recognition: No specific prizes or fellowships are mentioned in the provided text. Doctoral supervision & grants: The text does not enumerate individual students or funded projects; however, his coordination of an international PhD programme and numerous experimental campaigns imply sizeable supervisory and funding responsibilities. Laboratory & team: Ubertini heads the SHM-Lab at UniPg, maintaining facilities for material mixing, 3D concrete printing, electrical impedance tomography, UAV photogrammetry, and large-scale structural testing, while collaborating with the European FABRE consortium and multiple EU projects.
Maurizio Zamboni is a Full Professor at the Department of Electronics and Telecommunications (DET) at the Polytechnic University of Turin, where he also serves as Student Ombudsman. His academic career spans over three decades with continuous teaching and research contributions in electronics and computing fields. Professor Zamboni's research interests focus on cutting-edge areas including CMOS integrated circuits, computer architecture, quantum computing, semiconductor devices, and VLSI design. His work particularly emphasizes emerging nanotechnologies for digital microelectronic architectures and the design of high-performance or low-consumption processing systems. He has developed expertise in circuit architectures for probabilistic computing, logic-in-memory computing, magnetic devices, and quantum architectures. His recent publications (2021-2025) reveal a strong trend toward quantum computing applications, in-memory processing architectures, and novel approaches to overcoming the memory wall problem. These works span both theoretical algorithm development and practical hardware implementations, with significant focus on quantum annealing, FPGA-based quantum emulation, and memory-mapped processing architectures. Professor Zamboni has been actively supervising PhD students working on quantum computing algorithms, hardware AI accelerators for automotive applications, and quantum-related optimization approaches. He leads research within the VLSILAB Group at DET, focusing on the intersection of nanoelectronics, quantum computing, and advanced computer architectures. His work bridges theoretical computer science with practical electronic design, creating novel solutions for next-generation computing challenges.
Mauro Andreolini is a University Researcher at the Department of Physical, Computer and Mathematical Sciences, University of Modena and Reggio Emilia. He teaches Operating Systems and Secure Software Development courses within the Computer Science degree program. His research focuses on Cybersecurity , Network Security , Machine Learning in Security , and Cloud Computing . His recent publications analyze Data Privacy through geohashing and clustering, Adversarial Attacks in cybersecurity, and Moving Target Defense architectures. He has also contributed to frameworks for Automated Security Assessments using deductive reasoning and Realistic Botnet Detection benchmarks. Andreolini's work addresses Graph Neural Networks in intrusion detection, n-Gram Analysis for automotive network security, and Side-Channel Vulnerabilities in USB devices. He collaborates with researchers like Artioli, Ferretti, Marchetti, and Colajanni on projects spanning Adversarial Machine Learning , Secure Software Development , and Cloud-Based Monitoring .
Beppe Liotta is a Full Professor at the Department of Engineering, University of Perugia. He serves as Rector Delegate for ICT and Digital Agenda. His research spans network discovery, graph drawing, algorithm engineering, and computational geometry . Laurea in Electrical Engineering (1990), Ph.D. in Computer Engineering (1995), both from University of Rome 'La Sapienza' Post-doc at Brown University (1995-1996) Current teaching: Information Visualization and Database Management Systems Liotta has authored over 170 papers and led projects like VisFAN (financial crime detection), VHyXY (large graph visualization), COWA (web traffic analysis), and WhatsOnWeb (web clustering). His work focuses on hybrid visualizations and network robustness . Recent articles highlight his expertise in biological networks , financial activity networks , and one-to-many matched graph visualizations . He has contributed to journals like IEEE Transactions on Visualization and Computer Graphics and conferences including PacificVis and Graph Drawing . Liotta actively participates in scientific service, including editorial roles for the Journal of Graph Algorithms and Applications and program committees for IEEE PVIS 2019.
Claudia Andreini is an Associate Professor at the University of Florence, affiliated with the Magnetic Resonance Center and the Department of Chemistry. Her research focuses on computational biology, particularly metalloproteins and bioinformatics. She pioneered the use of bioinformatics to study metalloproteins and developed the MetalPDB database. She collaborates internationally and has presented at major conferences. Education: PhD in Chemistry (2006), University of Florence; Laurea in Chemistry (2002, cum laude), University of Florence. Research interests include metalloprotein structure-function relationships, metal-coordination databases, and predictive computational tools. Her work integrates structural bioinformatics, machine learning, and database mining to analyze metal sites in proteins. Recent publications highlight advancements in zinc-binding site prediction, metal-induced structural variability, and applications of deep learning in metalloprotein analysis. She has contributed to drug design studies involving ruthenium complexes and antibacterial agents. No scientific awards or grants are explicitly listed in the provided texts. She leads the MetalPDB project and collaborates on structural biology initiatives.
Paolo Garza is an Associate Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, where he also serves as Coordinator of the College of Computer, Film and Mechatronics Engineering. He is a member of the DBDM research group and the SmartData@PoliTO laboratory, and actively contributes to academic governance through roles in teaching coordination and PhD program committees. Education: Bachelor’s in Computer Engineering, Polytechnic University of Turin (2001) PhD in Computer and Systems Engineering, Polytechnic University of Turin (2005) His research centers on data science, big data analytics, data mining, and machine learning , with applications in emergency management, real-time communications, Earth observation, and cybersecurity. He has led and participated in numerous national and commercial research projects, including AI4CTI and NODES (PNRR), and has collaborated with industry partners like Cisco Systems. His work bridges theoretical algorithm development and practical deployment in critical systems. The recent publications reflect a strong trend toward multimodal AI, crisis informatics, and intelligent networking . Articles span computer vision for environmental monitoring (e.g., burned area detection, canopy estimation), ML for real-time communication quality, multimodal document understanding, and crisis response systems. His team leverages deep learning, transformers, and vision-language models across diverse domains. Scientific Service: Associate Editor, Knowledge and Information Systems (2024–) Associate Editor, Expert Systems with Applications (2022–) General Co-Chair, IEEE AICT Conferences (2022, 2023) He mentors several PhD students and leads funded research initiatives focused on AI for sustainable industry and cyber threat intelligence. His teaching includes graduate courses on big data processing, distributed architectures, and data science lab methods. He has also directed commercial training programs and research contracts in machine learning and cybersecurity. Research Labs & Teams: DBDM - Database and Data Mining Group (DAUIN) SmartData@PoliTO - Big Data and Data Science Laboratory LAB 5 - Research Laboratory (DAUIN)
