جستجوی استاد راهنمای دانشگاهی

• Data Mining
• Database Systems
• Explainable AI
• +۵ مورد دیگر

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).

Michele Zorziمشاهده پروفایل

استاد

• Wireless Communications
• Networking
• mmWave Communications
• +۵ مورد دیگر

Michele Zorzi is a Professor of Telecommunications at the School of Engineering, University of Padova, Italy, where he has held a faculty position since 2003. He leads the SIGNET (Signal processing and Networking) Research Group, focusing on cutting-edge wireless networking challenges including mmWave communications and underwater networks. His extensive publication record exceeds 600 papers in top-tier journals and conferences, reflecting significant contributions to the field through both theoretical and experimental work. He received his Laurea Degree (1990) and Ph.D. (1994) in Electrical Engineering from the University of Padova. Prior academic appointments include Politecnico di Milano (1993-1996), University of California San Diego (1995-1998), and University of Ferrara (1998-2003), where he progressed from Associate Professor to full Professor. His educational trajectory demonstrates deep roots in Italian academia with international exposure. Professor Zorzi's research spans wireless communications and networking, with current emphases on mmWave networking for vehicular systems, underwater acoustic/optical communications, non-terrestrial networks, and AI-driven networking solutions. His group conducts experimental validations including at-sea trials for underwater systems and testbeds for vehicular networks. Key projects include PRATA for predictive QoS in autonomous driving and IoT-based environmental monitoring of the Venice Lagoon, demonstrating practical applications of theoretical work. Analysis of his 2022-2025 publications reveals strong trends in applying artificial intelligence to networking challenges across diverse environments. There is significant emphasis on vehicular networks (predictive QoS, teleoperated driving), underwater systems (acoustic/optical communications, AUV swarms), and satellite networks (Starlink integration, NTN security). Experimental validation in real-world scenarios like the Venice Lagoon monitoring project and underwater sea trials characterizes his applied research approach. His scientific accolades include: IEEE Fellow (2007) IEEE Communications Society Best Tutorial Paper Award (2008, 2019) Stephen O. Rice Best Paper Award (2018) Multiple best paper awards at IEEE conferences (2005-2020) As principal investigator for numerous European and US research projects plus 20+ industry-funded initiatives, Professor Zorzi has mentored over 35 PhD students and post-docs. Graduates now hold prominent positions at institutions including Stanford, UCSD, CTTC, and Huawei. His SIGNET group maintains active international collaborations and contributes to open-source networking tools via GitHub, demonstrating commitment to community engagement. The SIGNET Research Group, housed within the Department of Information Engineering, operates specialized experimental facilities for mmWave and underwater communications. Current initiatives include AI-based predictive QoS frameworks for vehicular networks, underwater optical communication systems using ultraviolet light, and large-scale IoT deployments for environmental monitoring. The group's GitHub presence indicates strong open-science practices, while recent sea trials confirm hands-on experimental capabilities beyond theoretical work.

• Multi-objective optimization
• Sustainable logistics
• Industry 4.0
• +۳ مورد دیگر

Francesco Pilati is an Associate Professor at the Department of Industrial Engineering, University of Trento, where he serves as local coordinator for the scientific field ING-IND/17 (Industrial Plants and Logistic Systems). He chairs the research group on Industrial Plants, Production Systems, and Logistics, and teaches courses in Industrial Plants and Design of Digital Production and Assembly Systems. As coordinator of the Master's program in Management and Industrial Systems Engineering and University Coordinator for the EIT double degree in Zero-Defect Manufacture, Pilati bridges academic leadership with advanced manufacturing research. He has also served as Invited Lecturer at universities in Vienna and Göttingen. His research focuses on integrating environmental sustainability with technical-economic criteria through multi-objective optimization and impact assessment. Key areas include: Distribution networks and warehousing systems Manufacturing and assembly line design Hybrid energy production systems Digitization of manual production processes using depth cameras Recent publications highlight applications of Industry 4.0 technologies to pandemic safety, logistics optimization, and smart manufacturing. Pilati has received significant recognition including the Philip Morris Italia Empowering Research Award (2016) and Autostrade per l'Italia academic recognition. His editorial contributions include guest editing special issues on Digital Twins and Smart Factories in Q1 journals.

• Civil Engineering
• Structural Engineering
• Machine Learning in Structural Engineering
• +۴ مورد دیگر

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)

Gian Antonio Sustoمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Predictive Maintenance
• +۶ مورد دیگر

Gian Antonio Susto is an Associate Professor at the Department of Information Engineering , University of Padova . With a Ph.D. in Information Technology and post-doctoral experience at National University of Ireland, Maynooth, he leads research in Machine Learning , Semiconductor Manufacturing , and Industrial IoT . His work bridges Anomaly Detection , Continual Learning , and Algorithmic Fairness with applications in Hydroelectric Power Plants , Particle Accelerators , and Smart Mobility . B.Sc. and M.Sc. in Controls Engineering, University of Padova (cum laude) Ph.D. in Information Technology, University of Padova (2013) Post-Doc at National University of Ireland, Maynooth (2012-2013) Assistant Professor at University of Padova (2013-2021) His research focuses on Explainable AI , Virtual Metrology , and Deep Learning for manufacturing and infrastructure monitoring. Recent projects include the AIMS5.0 (AI for Manufacturing Sustainability) and MICS (Circular Economy in Italy) initiatives. His publications span Engineering Applications of Artificial Intelligence , IEEE Transactions , and Information Processing & Management , with 15+ recent papers on topics like Fault Diagnosis , Continual Learning , and Fair Ranking . Key scientific awards include: IEEE CCTA Best Student Paper Award (2021) IP&M 2020 Ph.D Paper Award Best Industry Paper Award, European Workshop on Advanced Control and Diagnosis (ACD 2019) He has supervised Ph.D. students on projects involving Particle Accelerators , Plant Behavior Modeling , and Explainable AI , with alumni now at institutions like Max Planck Institute , IBM , and Scripps Research . Current teaching includes Reinforcement Learning and Explainable Machine Learning at graduate and Ph.D. levels.

• Biomedical Signal and Image Processing
• Cardiovascular and Neural Modeling
• Physiologically-interpretable Artificial Intelligence systems
• +۵ مورد دیگر

Gaetano Valenza is an Associate Professor of Bioengineering at the University of Pisa, Italy, where he leads the Neuro-Cardiovascular Intelligence Lab at the Enrico Piaggio Research Centre. He holds affiliations with the Neuroscience Statistics Research Laboratory at MIT and has served as a Research Fellow at Harvard Medical School and Massachusetts General Hospital. His academic work spans bioengineering, computational physiology, and affective computing. His research focuses on statistical and nonlinear biomedical signal and image processing , cardiovascular and neural modeling , and physiologically interpretable artificial intelligence . He develops wearable systems for physiological monitoring, with applications in autonomic nervous system assessment, brain-heart interactions, and mental health. His work has led to novel metrics such as the Sympathetic and Parasympathetic activity indices derived from ECG. The 15 most recent publications reflect a consistent trend in brain-heart interplay , complexity analysis of physiological signals , explainable AI in healthcare , and virtual reality applications in mental health . His work integrates advanced signal processing, nonlinear dynamics, and machine learning to decode emotional and cognitive states from physiological data. Dr. Valenza is a Senior Member of IEEE and serves on several technical committees. He is an active editorial leader, currently serving as Associate Editor for IEEE-EMBC , Plos One , Complexity , and Scientific Reports , and has guest-edited special issues in Philosophical Transactions of the Royal Society A and IEEE Journal of Biomedical and Health Informatics . He has led or participated in numerous international research projects, including FP7 and H2020 initiatives such as NEVERMIND and EXPERIENCE. He teaches courses in Biostatistics, Probability & Biostatistics, and Advanced Image Processing at the University of Pisa. As lab head and project coordinator, he leads a multidisciplinary team working on neuro-cardiovascular intelligence, wearable systems, and AI-driven mental health interventions.

• Computer Engineering
• Embedded Systems
• Fault Tolerance
• +۴ مورد دیگر

Corrado De Sio is a Fixed-term Researcher at the Department of Control and Computer Science (DAUIN) , Politecnico di Torino , affiliated with the College of Computer, Film, and Mechatronics Engineering . His academic roles include course instruction and collaboration for Reconfigurable Computing , High Performance Computing (HPC) , and Operating Systems for High-Performance Supercomputers across multiple academic years (2020-2025). Research Interests focus on: Reliability of reconfigurable systems and FPGAs under radiation effects Hardware-software co-design for fault tolerance Embedded systems in aerospace and safety-critical applications Machine learning acceleration on reconfigurable hardware Radiation effects on real-time operating systems and CNN implementations Recent publications address: 2025: Selective hardening of RISCV soft-processors for space applications 2025: Real-time 'signal for help' gesture recognition systems 2024: Reliability analysis of RISC-V processors and CNN placement algorithms 2023: Fault tolerance in FPGA-based CNNs and radiation effects on RTOS Patents include: PyXEL - Python Toolkit for Reconfigurable Hardware Surveillance Software for Real-Time Violence Detection Academic Supervision : Co-supervisor for PhD candidate Arash Amini Bardpareh in Computer and Systems Engineering .

Giulia Giordanoمشاهده پروفایل

استاد

• Automatic control
• Complex networks
• Control systems
• +۱۲ مورد دیگر

Giulia Giordano is a Full Professor in the Department of Industrial Engineering at the University of Trento, Italy, where she leads the Dynamical Networks and Systems Biology research group. She also holds a dual appointment as Visiting Professor and Delft Technology Fellow at the Delft Center for Systems and Control, Delft University of Technology, The Netherlands. Her career includes previous positions as Assistant Professor at Delft University of Technology (2017-2019), Postdoctoral Research Fellow at Lund University, Sweden (2016-2017), and Research Fellow at the University of Udine, Italy (2016). Giulia earned her Ph.D. in Industrial and Information Engineering: Automation (Excellent) from the University of Udine with a thesis titled "Structural Analysis and Control of Dynamical Networks." She completed her M.Sc. and B.Sc. in Electrical Engineering (both Summa cum laude) at the same institution. She also undertook research visits at Caltech (2012) as a SURF Fellow and at the University of Stuttgart (2015) as a DAAD Research Scholar. Her primary research focuses on the analysis and control of dynamical networks with applications in systems biology, mathematical ecology, and mathematical epidemiology. She develops mathematical frameworks that bridge control theory, network theory, and dynamical systems to address complex problems in biological systems. Her recent work spans epidemic modeling, opinion dynamics, biochemical networks, and neurological disorders, with a particular emphasis on structural analysis of networked systems. She employs both theoretical and computational approaches to understand system behavior under uncertainty. Giulia's publications reveal a strong interdisciplinary focus, spanning from theoretical control systems to practical applications in epidemiology and biology. Her recent work shows increasing emphasis on epidemic modeling (particularly related to mpox and SARS-CoV-2), network synchronization, and the application of control theory to biological phenomena like fibromyalgia pathogenesis and opinion formation. Many of her papers appear in top-tier control journals including Automatica and IEEE Transactions on Automatic Control. 2024: Outstanding Service as Associate Editor of IEEE Control Systems Letters 2021: SIAM Activity Group on Control and Systems Theory Prize 2020: Outstanding Reviewer, Annals of Internal Medicine 2017: NAHS Best Paper Prize and EECI PhD Award 2016: Outstanding TAC Reviewer, IEEE Transactions on Automatic Control Giulia actively mentors students and postdoctoral researchers, currently supervising five postdoctoral researchers and two Ph.D. students at the University of Trento. She has advised numerous M.Sc. and B.Sc. students on topics ranging from bio-inspired modeling to optimal control of epidemic systems. Her research is supported by competitive grants including the ERC Starting Grant INSPIRE (Integrated Structural and Probabilistic Approaches for Biological and Epidemiological Systems). She serves as Associate Editor for IEEE Control Systems Letters and Automatica, and is a Senior Member of IEEE and the Control Systems Society. Giulia leads the Dynamical Networks and Systems Biology research group at the University of Trento, which maintains strong international collaborations across Europe and North America. The group's work combines theoretical advances in control theory with practical applications to pressing problems in public health and biological systems, demonstrating the power of mathematical approaches to understanding complex phenomena in the life sciences.

Luca Demetrioمشاهده پروفایل

استادیار

• Adversarial Machine Learning
• Malware Detection
• Cybersecurity
• +۳ مورد دیگر

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.

Maria Elena Valcherمشاهده پروفایل

استاد

• Control Systems
• Systems Theory
• Optimization
• +۵ مورد دیگر

Maria Elena Valcher is a Professor at the Department of Information Engineering, University of Padova, Italy. She is an IEEE Fellow (since 2012), IFAC Fellow (since 2023), Socio Effettivo of Istituto Veneto di Scienze, Lettere ed Arti (since 2017, previously Socio Corrispondente 2008-2017), and Socio Effettivo of Accademia Galieliana di Scienze, Lettere ed Arti in Padova (since 2022, previously Socio Corrispondente 2017-2022). She currently serves as Administrator of the Istituto Veneto and holds leadership positions including EUCA President (2024-2025) and IEEE Control Systems Society Past President. Her research focuses on control systems, systems theory, optimization, Boolean control networks, multi-agent systems, and consensus problems. She has made significant contributions in distributed control, data-driven methods, and network optimization, with recent work exploring applications in opinion dynamics and social networks. Recent publications demonstrate a strong emphasis on data-driven approaches to control systems, particularly in distributed state estimation, unknown-input observer design, and multi-agent coordination. Her work shows consistent development in theoretical frameworks for networked systems with practical applications. Awards and Honors: IEEE Fellow (2012) IFAC Fellow (2023) Socio Effettivo, Istituto Veneto di Scienze, Lettere ed Arti (2017-present) Socio Effettivo, Accademia Galieliana di Scienze, Lettere ed Arti in Padova (2022-present) She teaches 'Controlli Automatici' (Bachelor in Information Engineering) and 'Systems Theory' (Master in Control Systems Engineering) during the 2024/2025 academic year. She has chaired major conferences including the 61st IEEE Conference on Decision and Control (CDC 2022) and serves as Program Chair for ICSTCC 2025.

• Battery simulation
• Cyber physical system
• Design simulation
• +۱۲ مورد دیگر

Sara Vinco is an Associate Professor at the Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, Italy. She specializes in battery simulation, digital twins, and energy-efficient design automation for heterogeneous embedded systems, aligning with Industrial and Information Engineering (Area 0009) and ERC sectors including Computer Architecture and Machine Learning . Her research focuses on advancing cyber-physical systems through simulation frameworks like SystemC-AMS, enabling holistic modeling of analog, digital, and thermal domains. Key projects include data-driven digital twins for EV batteries and low-area digital circuits in industrial/medical applications, supported by commercial contracts such as C-based virtual prototyping. Her recent publications (2022-2023) emphasize machine learning for battery SOH/SOC estimation , energy monitoring in production lines , and multi-domain fault modeling . These works span journals like IEEE Transactions and conferences including DATE and ISLPED. Awarded the FFABR 2017 grant and IEEE FDL Best Paper Award 2011 , she also chairs editorial boards for IEEE Transactions on CAD and DATE Conference. She supervises PhD students Giovanni Pollo (Digital Circuits) and Khaled Alamin (EV Battery Twins), reflecting her leadership in smart systems design.

• Cybersecurity
• Electronic Identity
• Network Security
• +۷ مورد دیگر

Antonio Lioy is a Full Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin. He serves as the Cyber Security Representative for the university in national and European government collaborations, is a member of the SmartData@PoliTO interdepartmental center, and acts as Scientific Advisor for the DRIVESEC partnership. His research is deeply rooted in cybersecurity, electronic identity, network and web security, and quantum-resistant systems. His research interests focus on cybersecurity , electronic identity , network security , web security , and quantum cryptography . He leads the TORSEC Security Group and conducts research in LAB 7, with a strong emphasis on trusted computing, post-quantum cryptography, IoT security, and secure network infrastructures. His work aligns with ERC sectors in computer systems and cryptology, and supports UN SDGs on innovation and sustainable cities. His recent publications show a strong trend toward post-quantum cryptography , remote attestation , trusted execution environments , and secure network virtualization . These works appear in top venues such as IEEE, ACM, and IFIP conferences, with a focus on securing future digital infrastructures against emerging threats. Themes include integrity management in softwarized networks, standard-based attestation, and quantum-resistant PKI strategies. He has supervised numerous PhD students, including Davide Colaiacomo, Francesco Vaccaro, Flavio Ciravegna, Grazia D'Onghia, Lorenzo Ferro, Francesco Settanni, Enrico Bravi, Silvia Sisinni, Luca Mannella, and Ignazio Pedone, whose research spans cybersecurity, quantum computing, and IoT. He leads multiple high-profile research projects such as Q-FENCE, QUBIP, SERICS, and FISHY, funded by Horizon Europe and national programs, demonstrating significant grant acquisition and leadership. He is actively involved in research labs and teams, primarily the TORSEC - Security Group (DAUIN) and LAB 7 - Research Laboratory (DAUIN) , which focus on applied cybersecurity research. His projects often involve collaboration with industry and government bodies, emphasizing real-world impact in secure digital identity, critical infrastructure protection, and quantum-safe transitions.

• Neuroimaging
• Neurodegenerative Diseases
• Alzheimer's Disease
• +۴ مورد دیگر

Federica Agosta is an Associate Professor of Neurology at Vita-Salute San Raffaele University (UniSR) and Functional Unit Head at San Raffaele Hospital (OSR) , Milan, Italy. Her research focuses on neuroimaging in neurodegenerative diseases , including Alzheimer's, Parkinson's, and amyotrophic lateral sclerosis (ALS). PhD in Experimental Neurology (2012), UniSR Neurology Qualification (2008), UniSR MD in Medicine and Surgery (2003), UniSR Research Interests : Federica Agosta's work bridges functional MRI (fMRI) with clinical applications in neurodegenerative disorders. She investigates: Brain network alterations in Alzheimer's and Parkinson's Neuroimaging biomarkers for ALS and dementia Gender-specific neurological manifestations Advanced MRI techniques for early disease detection Cognitive rehabilitation strategies Neurodegeneration and brain connectivity Recent Publications highlight trends in neuroimaging endpoints for ALS, memory systems, Parkinson's scales, sleep apnea, and epilepsy diagnostics. Her ERC Starting Grant (2017) and European Grand Prix for Young Researcher (2019) underscore her leadership in neurodegenerative disease research. Scientific Roles : Coordinator, Doctoral Program in Cognitive and Behavioral Sciences (UniSR, 2024–) Co-PI, San Raffaele Neurotech Hub (2024–) President, Master's Degree Council in Rehabilitation Sciences (2021–) Visiting Professor, University of Belgrade (2017–) Editorial & Societal Contributions : Member of the EAN Scientific Committee , Co-Chair of the ALS & FTD Panel , and active roles in societies like SIN and SINdem . She serves on editorial boards for Neurological Sciences and Neurology .

• Integrated Circuits
• Pervasive Computing
• Electronics
• +۱۴ مورد دیگر

Matteo Nardello is a researcher affiliated with the Department of Industrial Engineering at the University of Trento. His work focuses on embedded systems, IoT, and energy harvesting technologies for sustainable applications. Current academic affiliation: Department of Industrial Engineering, University of Trento Research interests: IoT, embedded systems, energy harvesting, machine learning, cyber-physical systems Contact: matteo.nardello@unitn.it His research integrates hardware-software co-design for batteryless IoT systems, with applications in smart agriculture, industrial monitoring, and autonomous vehicles. Recent work explores deep learning at the edge, energy-efficient sensor networks, and microbial fuel cells for self-powered devices. Key article trends highlight a focus on sustainable power solutions, wireless sensor networks, and machine learning optimization for constrained environments. He contributes to courses on embedded systems, IoT, and AI-powered industrial applications at the University of Trento.

Daniele Loiaconoمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۶ مورد دیگر

Daniele Loiacono is an Associate Professor at Politecnico di Milano's Department of Electronics, Information, and Bioengineering (DEIB), affiliated with the Artificial Intelligence and Robotics Lab (AIRLab). His research focuses on interdisciplinary applications of Artificial Intelligence, Machine Learning, and Deep Learning in medical imaging, radiation therapy, and procedural content generation for games. He leads projects in synthetic image generation for radiotherapy quality assurance, automated treatment planning, and bias analysis in medical AI systems. Key research areas include medical image synthesis using GANs, radiation therapy optimization, and algorithmic game design. His contributions span clinical applications such as total marrow irradiation (TMI) planning and lymph-node segmentation, alongside innovations in shader generation and interactive evolutionary tools for game development. Loiacono collaborates on multi-center studies to validate AI-driven workflows in healthcare and has pioneered methods combining lean Six Sigma with machine learning for treatment process improvement. His work bridges clinical medicine and computer science, addressing challenges in radiation oncology, anatomical imaging, and procedural content automation. The AIRLab serves as a hub for his research, integrating AI advancements into real-world medical and engineering solutions.