جست‌وجوی استاد و پژوهشگر

Kaitlyn Regehrمشاهده پروفایل

دانشیار

• Digital Humanities
• Social Media Impact on Youth
• Image-Based Sexual Harassment
• +۷ مورد دیگر

Dr Kaitlyn Regehr is an Associate Professor and Programme Director of Digital Humanities in the Department of Information Studies at University College London (UCL). She holds a doctorate from King's College London and has established herself as a leading expert on the cultural impacts of social media, particularly on children and youth. Her research directly informs policy and legislation, including the Online Safety Act and new Cyberflashing legislation. University College London - Department of Information Studies Programme Director of Digital Humanities Associate Professor in Digital Humanities Dr Regehr's research focuses on the intersection of digital culture, youth safety, and policy development. She leads significant projects including the "Safer Scrolling Report" which generated media attention across 300 outlets including the Guardian's front page, and has influenced national curriculum changes. Her work on "Understanding and Combatting Youth Experiences of Image-Based Sexual Harassment and Abuse" has led to policy recommendations adopted by educational authorities. She also contributed to the Mayor of London commissioned report "Women We See: Advertising in London's Public Spaces" which informed national advertising standards. Analyzing her recent publications reveals a consistent focus on how digital platforms facilitate and amplify image-based sexual harassment against youth, with particular attention to school environments across diverse socioeconomic contexts. Her work demonstrates how platform design features actively direct harmful content to young people, creating distinct digital sexual cultures in different educational settings. The research combines digital humanities methodologies with critical analysis of social media platforms' role in normalizing abuse. Dr Regehr has provided consultation to the House of Lords, Members of Parliament, the Metropolitan Police, Scottish Government, and Education Scotland on digital citizenship education and online safety. She has been awarded over £2 million in research funding including AHRC projects and UCL Policy Lab funding. Her media work includes producing and serving as topic specialist on documentaries for BBC News, BBC Women's Hour, Channel 4, and the Economist.

Jasmine Gnanaduraiمشاهده پروفایل

دانشیار

• Machine Learning
• Wireless Sensor Networks
• Cloud Computing
• +۷ مورد دیگر

Jasmine Gnanadurai serves as an Associate Professor in the Department of Electrical Engineering & Computer Science at George Fox University's College of Engineering. She teaches a range of courses including Introduction to Computer Science, Software Engineering, Servant Engineering, and Senior Design. Her office is located in Wood-Mar 222, where she holds regular office hours for student consultation. Dr. Gnanadurai's educational background includes: Ph.D. in Computer Science (2017) from Anna University, Chennai, India M.Phil (2006) from Bharathidasan University, Tiruchirappalli, India M.C.A. (2000) from Bharathidasan University, Tiruchirappalli, India B.Sc. in Computer Science (1997) from Bharathidasan University, Tiruchirappalli, India Dr. Gnanadurai's research spans multiple cutting-edge domains in computer science and engineering. Her work in wireless sensor networks has focused on optimization algorithms for zone-based networks, particularly using techniques like DBSCAN clustering and particle swarm optimization. She has made significant contributions to cloud computing architecture, especially in resource allocation and load balancing for peer-to-peer networks. Her expertise extends to machine learning applications across diverse fields including agriculture productivity, student feedback analysis, and crowd behavior recognition. Her recent publications demonstrate a strong interdisciplinary approach, bridging computer science with practical applications in healthcare, education, smart cities, and transportation. She has published extensively on deep learning techniques for EMG-based hand gesture recognition, blockchain applications for electric vehicle charging infrastructure, and immersive technologies for educational settings. These works reflect her commitment to addressing real-world challenges through technological innovation. Dr. Gnanadurai actively contributes to the engineering education mission at George Fox University through multiple channels. She teaches foundational computer science courses, advanced software engineering, and participates in the university's distinctive Servant Engineering program where students develop solutions to humanitarian needs. She also guides senior engineering students through their capstone Senior Design projects, which partner with industry sponsors to solve real-world problems. As part of the College of Engineering, Dr. Gnanadurai works within interdisciplinary teams that collaborate on projects addressing humanitarian needs through the Servant Engineering program. This program connects students with community partners to develop engineering solutions that serve vulnerable populations, reflecting the university's Christian mission of service. Her involvement in Senior Design connects students with industry partners including Xerox, Intel, Garmin, and numerous regional companies, providing students with valuable real-world experience.

Mark S. Seidenbergمشاهده پروفایل

استاد

• Language Acquisition
• Reading Skills
• Cognitive Psychology
• +۳ مورد دیگر

Mark S. Seidenberg is a Professor in the Department of Psychology at the University of Wisconsin-Madison, specializing in language acquisition and reading skills. His research bridges cognitive psychology, neuroscience, and educational practice, with particular focus on how humans learn to read and process language. Dr. Seidenberg's educational background includes a Ph.D. from Columbia University in New York. His research interests span language acquisition, reading development, cognitive neuroscience of language, and educational implications of reading science. His work has significantly influenced both theoretical understanding of reading processes and practical applications in education. His publication record shows consistent scholarly activity from the 1990s through 2022, with recent work examining gender representation in children's books, embodied semantics, and the science of reading. His research demonstrates a progression from foundational studies of word recognition and naming to broader investigations of reading science and its educational applications. Dr. Seidenberg has contributed to major scientific journals including Science, Psychological Science, Cerebral Cortex, and Journal of Experimental Psychology. His 2013 paper "The Science of Reading and Its Educational Implications" represents a significant synthesis of research in the field. His work connects cognitive psychology with educational practice, particularly in understanding reading development and disorders. The trajectory of his research shows increasing attention to practical applications of reading science in educational settings, as evidenced by publications like "Taking educational research to school" (2009) and "How should reading be taught?" (2002).

Sunil Aryalمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۱۰ مورد دیگر

Sunil Aryal is an Associate Professor of Data Science at the School of Information Technology, Faculty of Science Engineering and Built Environment, Deakin University, Australia. He received his PhD and Master by Research degrees from Monash University Australia and has published over 70 papers in top-tier international venues in Artificial Intelligence, Machine Learning and Data Mining. Dr. Aryal's educational background includes: Graduate Certificate of Higher Education Learning and Teaching, Deakin University (2020) PhD in Computer Science, Monash University (2017) Master of Information Technology (Research), Monash University (2012) Master of Information Technology (Coursework), University of Southern Queensland (2008) Bachelor of Information Technology, Purbanchal University, Nepal (2005) His primary research interests focus on making Machine Learning and Data Mining algorithms robust and flexible to handle heterogeneous, noisy and uncertain data in real-world problems. His work spans across several specific areas including anomaly detection, clustering, kernel/similarity-based learning, ensemble methods, learning from limited data, reinforcement learning, natural language processing, and computer vision. Dr. Aryal is particularly interested in applying these techniques to solve challenges in Defence, National Intelligence, Engineering, Manufacturing, Healthcare and Education. Dr. Aryal co-leads the Machine Learning for Decision Support (MLDS) Research Group at Deakin University and has secured over AUD 4.5 million in external research funding. His research is supported by diverse organizations including US and Australia Defence Agencies, the Australian Office of National Intelligence, Worksafe Victoria, the Victorian State Department of Education and Training, the Technology Innovation Institute (TII) UAE, and Table Tennis Australia (TTA). His notable awards include multiple Deakin University research and teaching awards, the Australian Postgraduate Award for his PhD studies, and several student travel awards during his doctoral candidature. Dr. Aryal actively supervises numerous PhD and Master's students and has contributed significantly to teaching in various courses at Deakin University and previously at Federation University. He serves on several university committees and contributes to the research community as a reviewer, program committee member, and editor for various journals and conferences.

• Multimodal Communication
• Multimodal Corpora
• Spoken Language and Gestural Behaviour
• +۶ مورد دیگر

Patrizia Paggio serves as an Associate Professor and Senior Researcher within the Department of Nordic Studies and Linguistics at the University of Copenhagen's Faculty of Humanities, concurrently holding a full professorship at the University of Malta's Institute of Linguistics and Language Technology since September 2011. Her scholarly work centers on the intricate relationship between verbal and nonverbal communication modalities, with international recognition for advancing methodologies in multimodal analysis. Academic Background: PhD in Computational Linguistics from the University of Copenhagen (1997), dissertation: "The Treatment of Information Structure in Machine Translation" Professor Paggio's research program investigates how gestures, head movements, and other nonverbal cues interact with spoken language to construct meaning in natural communication. She has pioneered methodologies for constructing and analyzing multimodal corpora, while maintaining technical expertise in machine translation systems, grammar engineering, and content-based querying frameworks. Her theoretical work spans formal syntactic structures, discourse phenomena, information packaging, and ontological representations for linguistic data, consistently bridging computational methods with linguistic theory. Analysis of her recent publications (2020-2025) reveals a sustained focus on computational approaches to nonverbal communication, particularly the automatic detection and annotation of head movements and gestures in both physical and digital environments. Key contributions include the GEHM Zoom corpus for online interaction analysis, eye-tracking studies of emoji processing, and diachronic modeling of historical language change. Her work strategically integrates eye-tracking, corpus linguistics, and machine learning techniques, establishing her at the convergence of linguistic theory, cognitive science, and artificial intelligence applications. Professional Leadership: Coordinator of the international GEHM (Gestures and Head Movements in Language) research network Organizer of MULTIMODAL CORPORA 2018, 4th European/Nordic Symposium on Multimodal Communication, and LREC2022 Workshop on People in Vision, Language and the Mind

Elena Simona Lohanمشاهده پروفایل

استاد

• Wireless Positioning
• GNSS Algorithms
• Wearable Computing
• +۷ مورد دیگر

Elena Simona Lohan is a Professor at Tampere University's Faculty of Information Technology and Communication Sciences , leading the Signal Processing for Wireless Positioning research group. She also holds a Visiting Professor position at Universitat Autonoma de Barcelona's SPCOMNAV group. Education : MSc in Electrical Engineering (Politehnica University of Bucharest, 1997), DEA in Econometrics (École Polytechnique, Paris, 1998), PhD in Telecommunications (Tampere University of Technology, 2003) Research Focus : GNSS algorithms, interference detection, wearable computing, and 5G positioning convergence. Her work spans signal processing for GNSS (Galileo, GPS, GLONASS, BeiDou), indoor/outdoor localization, and IoT communication. She coordinates the H2020 MSCA European Joint Doctorate A-WEAR and serves as Associate Editor for the Journal of Navigation and IET Radar, Sonar & Navigation . Notable projects include GRAMMAR (Galileo receivers), UJI IndoorLoc Platform , and SP4TE collaborations. She has authored over 250 publications and 6 patents.

Denis Kalkofenمشاهده پروفایل

دانشیار

• Virtual Reality
• Mixed Reality
• Augmented Reality
• +۵ مورد دیگر

Dr. Denis Kalkofen serves as an Associate Professor at the Institute of Computer Graphics and Vision (ICG) at Graz University of Technology, Austria. His academic journey began with a Dipl.-Ing. (2004) from the University of Magdeburg, followed by a Dr. techn. (2009) from Graz University of Technology. University of Magdeburg - Dipl.-Ing. (2004) Graz University of Technology - Dr. techn. (2009) University of Michigan, Ann Arbor - Virtual Reality Laboratory member Stanford University - Visiting Assistant Professor at Computational Imaging Laboratory (2019) University of South Australia - Visiting Researcher at Wearable Computer Laboratory (2019) Dr. Kalkofen's research centers on developing visualization, interaction, authoring, and display technologies for Virtual and Mixed Reality environments, with particular emphasis on combining computer graphics and computer vision techniques to create comprehensible and accessible VR/MR experiences. His work spans situated visualization (addressing label placement and X-ray visualization), photometric rendering (recovering lighting properties for realistic AR), and content creation automation (leveraging existing data sources for AR content). His publication trends over the past 15+ years demonstrate consistent leadership in AR/MR research, evolving from foundational work on visualization techniques to current cutting-edge research in neural rendering, neuroadaptive systems, and industrial AR applications. Recent work shows increased focus on practical industrial implementations, error management in AR assembly, and multimodal learning applications. Best paper award for 'enRoute: Dynamic path extraction from biological pathway maps' (2012) Best short paper for 'TutAR: Augmented Reality Tutorials for Hands-only Procedures' (2018) Best paper for 'Tools for Teaching Mining Students in Virtual Reality' (2020) Honorable Mention for Best Paper for 'Adaptive User-Perspective Rendering' (2017) Dr. Kalkofen leads Team Kalkofen at ICG, supervising researchers including Peter Mohr, Shohei Mori, David Mandl, and Ana Stanescu. His team has secured significant funding for projects related to AR/VR content creation, visualization techniques, and industrial applications. They maintain strong collaborations with institutions including Stanford University, University of South Australia, and University of Michigan. Team Kalkofen operates within the Institute of Computer Graphics and Vision at TU Graz, focusing on three main research thrusts: situated visualization for AR, photometric rendering for realistic MR, and automated content creation for professional AR applications. The team maintains active partnerships with industry and academic institutions worldwide, contributing to the advancement of practical AR/MR technologies.

• Digital Signal Processing
• Image Processing
• Multimedia Communications
• +۳ مورد دیگر

Marco Carli is a Full Professor at the Department of Industrial, Electronic, and Mechanical Engineering, Roma Tre University, Italy. He holds a Laurea in Telecommunication Engineering from Università degli Studi di Roma 'La Sapienza' and a Ph.D. from Tampere University of Technology. His research focuses on digital signal and image processing applied to multimedia communications, including digital watermarking, multimedia quality evaluation, and information security. He has led projects such as EHEM (medieval architecture modeling), ISEEYOO (AI-based anomaly detection), and INSECTT (secure IoT-AI systems). Carli serves as an Associate Editor for IEEE Transactions on Image Processing and Area Editor for Signal Processing: Image Communication. He is an IEEE Senior Member. His work spans over 55 journal publications and numerous conferences, addressing topics like immersive VR applications, cybersecurity, and perceptual quality metrics. Education: Laurea in Telecommunication Engineering, Università di Roma 'La Sapienza', 1990s Ph.D. in Telecommunication Engineering, Tampere University of Technology, Finland Projects: EHEM: Digital modeling of medieval architecture and art ISEEYOO: Anomaly detection in Cyber-Physical Systems INSECTT: Secure IoT systems with AI Research Interests: Signal/image processing for multimedia Quality evaluation and watermarking Cybersecurity and IoT applications Awards: None explicitly listed Grants/Advising: Multiple EU-funded projects (e.g., ImmerSAFE, RESISTO, ATENA) and collaborations with industry partners like Thales Alenia Space.

Ralph Etienne-Cummingsمشاهده پروفایل

استاد

• Neuromorphic Engineering
• Neural Prosthetics
• Biomedical Robotics
• +۵ مورد دیگر

Ralph Etienne-Cummings is the Julian S. Smith Professor of Electrical and Computer Engineering at Johns Hopkins University (JHU), where he also serves as Vice Provost for Faculty Affairs. He holds secondary appointments in Computer Science and is affiliated with JHU's Applied Physics Lab. His work spans three decades, pioneering advancements in neuromorphic engineering, neural prosthetics, and biomorphic robotics. Etienne-Cummings leads the Computational Sensory Motor Systems Laboratory and has developed systems for closed-loop neural interfaces, prosthetics, and biomedical sensors. Education: BSc in Physics (1988), Lincoln University MSEE (1990) and PhD (1994) in Electrical Engineering, University of Pennsylvania Research Interests: His work focuses on neuromorphic systems, bio-inspired algorithms, and neural prosthetics. Key areas include spinal cord stimulation for mobility restoration, wearable health monitoring, and ultrasonic imaging for infertility treatment. He has contributed to silicon Central Pattern Generators (CPGs) for bipedal robotics and developed the first large-scale neural computer using VLSI chips. His lab explores organoid intelligence and biohybrid systems, blending neuroscience with engineering. Impact & Recognition: Named Fellow of AIMBE (2021) and IEEE (2012) Recipient of JHU Discovery Awards (2018–2019) and NSF CAREER Award (1996) Developed the 'Microbead'—a 0.009mm³ wireless neural stimulator Industry & Outreach: Served as founding director of JHU's Institute of Neuromorphic Engineering and advised firms like Panasonic and Avago. Testified in federal court on intellectual property disputes. Recognized as a 'ScienceMaker' in the HistoryMakers Archive for contributions to African American STEM leadership. Labs & Collaborations: Directs the Computational Sensory Motor Systems Lab. Collaborates with DARPA on prosthetics and the NIH on bioelectronic medicine. His work bridges academia and industry, emphasizing practical applications of neural engineering.

V. Menkovskiمشاهده پروفایل

دانشیار

• Graph Neural Networks
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Dr. V. Menkovski serves as an Associate Professor in Data Mining at Eindhoven University of Technology's Department of Mathematics and Computer Science. He also holds associate professor positions with EAISI Health and EAISI High Tech Systems, and is an ICMS Affiliated member. His work spans multiple domains of artificial intelligence and computational physics, with significant contributions to fusion energy research. Mathematics and Computer Science, Data Mining (Primary Appointment) EAISI Health (Associate Professor) EAISI High Tech Systems (Associate Professor) ICMS (Affiliated Member) Menkovski's research focuses on Graph Neural Networks, Machine Learning, Deep Learning, and their applications in diverse fields from plasma physics to metamaterials. His work demonstrates strong interdisciplinary connections, particularly between computer science and fusion energy research. He has developed novel approaches for crowd simulation, tokamak plasma monitoring, and metamaterials homogenization using advanced neural architectures. His fingerprint reveals expertise in Quality-of-Experience, Autoencoders, Neural Networks, Annotation, Graph Neural Networks, Video Streaming, Adversarial Machine Learning, and Anomaly Detection. Analysis of his recent publications (2023-2025) shows a clear trend toward applying Graph Neural Networks to complex physical systems, particularly in fusion energy research and materials science. His work increasingly integrates symmetry principles with neural architectures, as seen in his research on equivariant networks for metamaterials and symmetry-informed networks for zeolite analysis. There's also significant focus on practical applications in fake news detection, anomaly detection, and plasma state monitoring. Best Paper Award ICPM 2021 (with Sommers and Fahland) Best Paper Award of LoG 2022 (with multiple co-authors including Huang, Chen, Fang, Zhao, Yin, Pei, Mocanu, Wang, Pechenizkiy, and Liu) Menkovski teaches several advanced courses including Deep Learning, Advanced Topics in Artificial Intelligence, and Sociophysics 2, which runs through August 2025. His supervised work portfolio includes 79 projects, indicating substantial mentorship activity. He has received significant media attention for his research, including coverage by 11 news outlets, blog posts, and mentions on social media platforms. His work on 'Supervised Learning of Process Discovery Techniques Using Graph Neural Networks' was particularly noted in media coverage. His research involves collaboration with multiple institutions and teams, particularly in fusion energy research (Eurofusion Tokamak Exploitation Team, ASDEX-Upgrade team, EUROfusion MST1 Team). He works closely with researchers across disciplines, including physicists working on tokamak plasma and materials scientists studying metamaterials and zeolites.

Albert Gattمشاهده پروفایل

استاد

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Albert Gatt is a Professor of Natural Language Processing at Utrecht University's Department of Information and Computing Sciences, where he also serves as Programme Director for AI & Data Science. He holds an Associate Professor position (on leave) at the University of Malta's Institute of Linguistics and Language Technology. His research focuses on Natural Language Generation (NLG), multimodal models, and under-resourced language support, particularly for Maltese. He leads projects like NL4XAI and MASRI, addressing challenges in explainable AI and speech recognition. Education: Advanced degrees in computational linguistics and AI (not explicitly detailed in text). Key Projects: Multilingual NLG, Vision-Language benchmarks, Maltese ASR, and NLP evaluation methodologies. Research interests span data-to-text generation, vision-language interfaces, and evaluation practices. His work bridges computational linguistics with cognitive science, emphasizing human-AI collaboration. Notable contributions include the TUNA corpus, SimpleNLG toolkit, and foundational studies on referring expression generation. Publications (2025-2024) explore robust fine-tuning, LLM evaluation, and visual-linguistic grounding. Collaborations span academia and industry, addressing ethical AI and language equity. Supervises a global team of researchers and PhD students across multiple institutions, fostering innovation in NLG, multimodal AI, and Maltese language tech.

Liam Wellsمشاهده پروفایل

مدرس

• Audio-Visual Arts
• New Media Art
• Installation Art
• +۵ مورد دیگر

Liam Wells is the Course Leader for the BA (Hons) Film and Moving Image Production and an MA Moving Image and Sound Unit Tutor at Middlesex University. His practice-based research investigates non-linear relationships between time, space, and audio-visual works within site-specific contexts. Key themes include hybrid processes, duration, and experimental aesthetics in installations, performances, and webcasts. Education: Pursuing a PhD by Practice at the University of the Arts London (since 2010). His work has been exhibited internationally, including at the Shanghai World Expo Pavilion and screenings across Europe and Australia. Awards include selection for the One Minutes Vol 4 film festival and commissions from the One Minutes Foundation and DEFRA-funded projects. Research Output Highlights: 2011: Presentations at Ravensbourne, NAHEMI Conference, and ADM National Conference 2010: Shanghai World Expo Pavilion installation 2007–2009: Collaborative performances with Prof John Bowers and Alex Sanders Awards and Grants: PhD Scholarship (University of the Arts London) Arts Council East Funding (2004–2005) DEFRA-funded Soil-Net.com Project (2005) Lab/Team Affiliations: Member of the UEA Electro Acoustic Studio Ensemble Cofounder of WebJam international webcast series Contributor to Furtherfield.org online art review platform

Al Bovikمشاهده پروفایل

استاد مهمان

• Digital Video
• Visual Neuroscience
• Deep Learning
• +۳ مورد دیگر

Al Bovik is a Visiting Professor at the University of Texas at Austin's Department of Electrical and Computer Engineering, part of the College of Engineering. His research focuses on digital video, visual neuroscience, and deep learning, with an emphasis on developing perceptual theories for high-quality streaming video and virtual/augmented reality systems. He has pioneered advancements in video quality assessment, compression, and AI-generated media evaluation. He has received prestigious awards including the IEEE Edison Medal, Primetime Emmy, and membership in multiple national academies. Bovik co-founded the IEEE Transactions on Image Processing and established the IEEE International Conference on Image Processing in 1994, which he chaired. His influential books include The Essential Guides to Image and Video Processing . Key Research Contributions: HDR/SDR video quality metrics, perceptual artifact detection, and AI-driven quality assessment frameworks. Labs/Teams: Leadership in IEEE initiatives and collaborations in media quality and signal processing. His work bridges theoretical research with practical applications in video streaming, medical diagnostics, and user-generated content analysis.

Francesco Ragusaمشاهده پروفایل

پژوهشگر ارشد

• Computer Vision
• Pattern Recognition
• Machine Learning
• +۵ مورد دیگر

Francesco Ragusa is a Research Fellow at the University of Catania, Italy, holding an Industrial Doctorate in Computer Science (2021). He spent part of his PhD at the University of Hertfordshire, UK. His work focuses on First Person (Egocentric) Vision, including Human-Object Interaction, Industrial Applications, and Augmented Reality. He co-founded NEXT VISION s.r.l., an academic spin-off from the University of Catania since 2021. Key projects include the MECCANO dataset for industrial human-object interactions and the EGO-EXO4D initiative analyzing skilled human activity from multi-perspective views. Research interests span Computer Vision, Pattern Recognition, and Machine Learning. He contributed to seminal datasets like MECCANO and ENIGMA-51, advancing understanding of human behavior in industrial settings. His work emphasizes practical applications, such as wearable assistive systems and visual navigation solutions. Ragusa has delivered tutorials at major conferences (e.g., ICIAP, VISIGRAPP) on First Person Vision’s history, challenges, and trends. He actively promotes industrial collaborations and has secured grants including PNRR MUR (Code E63C22001940006) and EU funding under Next Generation EU. His teaching and professional activities include organizing workshops on egocentric vision for AI-driven assistants and industrial safety systems. Current research directions involve multimodal synthetic data utilization, gaze-based interaction analysis, and cross-modal fusion for human-robot collaboration.

Massimo Orazio Spataمشاهده پروفایل

پژوهشگر ارشد

• Deep Learning
• Biomedical Engineering
• Computer Science
• +۳ مورد دیگر

Massimo Orazio Spata is a Research Fellow in Computer Science at the University of Catania's Department of Mathematics and Computer Sciences, specializing in deep learning applications for biomedical, audio, and biometric systems. He has held roles at STMicroelectronics since 1999, focusing on system integration, image processing, and biomedical device R&D. He teaches courses such as Mobile Programming and Computer Architecture at secondary schools and has advised numerous students on grid computing and middleware projects. Education: PhD in Computer Science (University of Catania, 2008), MSc in Computer Science (University of Catania, 2013), and a teaching certification in Computer Science (University of Catania, 1998). Research interests include deep learning algorithms, biomedical device development, grid scheduling, and cybersecurity. He has authored patents on lab-on-chip systems, bio-computer analysis, and scheduling methods, and his work has been recognized with STMicroelectronics Innovation Awards (2016–2014) and a Cisco CCNA certification. Key collaborations include projects with Google (Mediapipe Objectron for robotics), Huawei (video deblurring), and involvement in the PNRR Horizon HiCONNECTS project (2024–present). He serves on conference committees (e.g., ICAETA 2023) and has developed e-learning systems and CAD tools for STMicroelectronics.