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Soteris DemetriouView profile

Senior Lecturer

• Computer System Security
• Cyber-Physical Systems Security
• Machine Learning Security
• +3 more

Soteris Demetriou is a Senior Lecturer of Computer Systems Security at Imperial College London's Department of Computing, within the Faculty of Engineering. He leads the Applications, Platforms, and Systems Security (APSS) Research Lab and directs the Academic Centre of Excellence in Cyber Security Research (ACE-CSR). His research focuses on securing mobile, IoT, and cyber-physical systems through techniques like explainable AI, reverse engineering, and trusted computing. Notable contributions include tools for privacy preservation in machine learning models, detection of LiDAR spoofing attacks, and securing Android's middleware. Education: PhD and MSc in Computer Science (University of Illinois at Urbana-Champaign), Diploma in Electrical and Computer Engineering (University of Patras). Research Interests: Mobile/IoT security, AI security, trusted computing, and vulnerability analysis. Key areas include privacy in generative models, adversarial attacks on autonomous systems, and large-scale distributed systems. Publications: Over 50 peer-reviewed papers in top venues like NDSS, CCS, and SOSP. Recent work addresses privacy in speech generation, LiDAR security for autonomous vehicles, and hyperscale serverless architectures at Meta. Awards: Distinguished Paper Award at NDSS 2018, Best Paper at SafeThings 2024, and multiple travel grants. Served on technical committees for PETS, CCS, and AutoSec. Grants & Collaborations: SPRITE+ grant for Bio-IoT security, collaboration with Meta on distributed systems, and leadership in ACE-CSR. Labs: APSS Lab focuses on systems and AI security, with interdisciplinary projects in healthcare and autonomous systems.

• Self-test and self-calibration of circuits
• Fault-tolerant systems
• Analog and mixed-signal circuits
• +3 more

Sule Ozev is a Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University (ASU). She holds a Ph.D. in Computer Science and Engineering from the University of California, San Diego (2002), and has been a faculty member since 2008. Her research focuses on analog and mixed-signal circuit testing, fault-tolerant systems, and RF circuit reliability. She has published over 100 papers and holds a U.S. patent. Notable awards include the NSF CAREER Award (2006) and the ASU Joseph Palais Distinguished Faculty Scholar Award (2023-2024). Her education includes: Ph.D., Computer Science and Engineering, UC San Diego (2002) M.S., Computer Science and Engineering, UC San Diego (1998) B.S., Electrical Engineering, Bogazici University, Turkey (1995) Research Interests: Self-test and calibration techniques for analog/RF circuits, fault simulation, and reliability enhancement. Recent work includes BIST solutions for power management units, MEMS sensor calibration, and IoT security. Publications span RF impedance measurement, low-power systems, and marine sensor applications. Awards also include best paper recognitions at IEEE VLSI Test Symposium (2022) and European Test Symposium (2019). Grants include NSF-funded projects on adaptive test strategies and collaborative research on sensor networks. Service roles include associate editor for IEEE Transactions on VLSI Systems and program chairs for major conferences.

Edward S. AhnView profile

Professor

• Pediatric Neurosurgery
• Neurovascular Surgery
• Fetal Surgery
• +3 more

Edward S. Ahn, M.D., is a Professor of Neurosurgery and Pediatrics at Mayo Clinic in Rochester, Minnesota. As a pediatric neurosurgeon, he specializes in minimally invasive techniques for craniosynostosis, fetal surgery for myelomeningocele, and management of pediatric neurovascular disorders like arteriovenous malformations and moyamoya disease. Education: B.A. in Biology and East Asian Studies, Harvard University (1999) M.D., New York University School of Medicine (2000) Internship in General Surgery, University of Maryland Medical Center (2001) Residency in Neurosurgery, University of Maryland Medical Center (2006) Fellowship in Pediatric Neurosurgery, Children’s Hospital (2007) Dr. Ahn’s research focuses on improving surgical outcomes for children with neurosurgical conditions, including craniosynostosis , hydrocephalus , and Chiari malformation . He pioneered image-based craniometric diagnostics for early craniosynostosis detection and explores telehealth applications for neonatal cranial screening. His recent work analyzes machine learning integration in surgical diagnostics. His publications span pediatric neurosurgical outcomes , fetal interventions , and vascular anomaly management . Dr. Ahn serves on editorial boards for Journal of Neurosurgery: Pediatrics and Child's Nervous System , and has received multiple Top Doctor recognitions since 2011. He also contributes to surgical education as Director of Neurosurgical Medical Student Education at Johns Hopkins University (2013–2014).

• Digital Forensics
• Image Processing
• Multimedia Analysis
• +4 more

Giulia Boato is an Associate Professor at the University of Trento’s Department of Information Engineering and Computer Science (DISI). She teaches courses in Probability and Multimedia Data Security. Her expertise spans Cyber Security, Digital Forensics, and Multimedia Analysis, focusing on image and signal processing for data protection, forensics, and anti-forensics. She collaborates internationally with institutions like Tampere University of Technology and Dartmouth College, co-advising PhD students and contributing to European projects like LIVINGKNOWLEDGE and GLOCAL. Education: PhD in Information and Communication Technology (2005), M.Sc. in Mathematics (2002), Scientific Lyceum (1998) with bilingual Italian-German certification. Past roles include Assistant Professor at DISI (2006–2018) and visiting researcher at the University of Vigo (2006) and University of Innsbruck (2018). Research interests include multimedia data protection, image forensics (tampering detection, computer vs. natural data discrimination), and intelligent data management. She leads projects on social media forensics, event-based retrieval, and synthetic media detection. Awards include Best Paper at IEEE WIFS 2012 and Top 10% Paper at MMSP 2012. Professional contributions include roles as co-chair of workshops, Technical Program Committee member for ICIP and ICC, and reviewer for journals like IEEE Transactions on Information Forensics and Security. She has advised PhD theses and contributed to datasets like TrueFace and WILD for synthetic media analysis.

• Machine Learning
• Data Mining
• Pattern Recognition
• +9 more

Associate Professor Zhidong Li is a prominent researcher at the Data Science Institute within the Faculty of Engineering and Information Technology at University of Technology Sydney (UTS), Australia. With over a decade of experience in data science and machine learning, he leads impactful research bridging theoretical advancements with practical applications across multiple critical infrastructure domains. Dr. Li earned his PhD from the University of New South Wales, Sydney, Australia, and previously served as a senior engineer at Data61, CSIRO (Commonwealth Scientific and Industrial Research Organisation), Australia's federal government agency for scientific research. His research spans machine learning, data mining, pattern recognition, image processing, and human-computer interaction with applications in water, gas, traffic, urbanization, visitor economy, agriculture, environment, finance, property market, railway, law, electric and health sectors. His work particularly focuses on developing interpretable AI models, temporal point processes, and practical applications for smart infrastructure management. His extensive publication record reveals strong thematic consistency in applying advanced machine learning techniques to infrastructure management problems, with particular emphasis on water systems. His research demonstrates progression from fundamental algorithm development toward increasingly sophisticated applications with real-world impact, especially in temporal modeling, graph neural networks, and fairness in AI systems. Scientific Awards 2022 R&D Excellence Award NSW Water Award 2021 UTS Medal for Research Impact for the Vice-Chancellor's Awards for Research Excellence 2018 Australian Museum Eureka Prize for Excellence in Data Science 2019 Victorian iAwards - Industrial & Primary Industries Merit for 'Predictive Analytics for Water Pipe Maintenance' Multiple AWA research innovation awards (NSW, National, QLD) Dr. Li actively supervises Masters and PhD students and leads numerous funded research projects across diverse sectors. His collaborative approach is evident through partnerships with water utilities, transport agencies, and various CRC projects focusing on Food Agility, Digital Finance, and Smartcrete. His work on the world's first independently-audited ethical talent AI in partnership with Reejig demonstrates his commitment to translating research into real-world solutions that address societal challenges while maintaining ethical standards.

Gustavo Adolfo Junipero Rodriguez-RiveraView profile

Academic

• Operating Systems
• Computer Networks
• Memory Management
• +11 more

Gustavo Rodriguez-Rivera is an Associate Teaching Professor in the Department of Computer Science at Purdue University, part of the School of Science. He joined the department in 2000 and holds a Ph.D. in Computer Science from Purdue University (1998), an M.S. in Electrical Engineering from ITESM Campus Monterrey (1990), and a B.S. in Electrical Engineering from the same institution (1985). His research focuses on Operating Systems, Computer Networks, Memory Management, Embedded Systems, Real-Time Systems, and broader areas like Numerical Analysis and Artificial Intelligence. Dr. Rodriguez-Rivera has received multiple teaching awards, including the ACM Faculty Award in Computer Science (2020, 2023) and Best Teacher in the School of Science (2014, 2016). His work spans software engineering education, structural health monitoring for wind turbines, and memory management algorithms. Notable publications include studies on garbage collection techniques, real-time project tracking in programming courses, and vibro-acoustic modulation for turbine blade inspection. He has advised numerous projects and contributed to grants such as the NSF-funded work on wind turbine blade monitoring. His academic contributions also include developing secure programming course modules and tools for interactive debugging systems.

Shan DuView profile

Assistant Professor

• Computer Vision/Graphics
• Machine/Deep Learning
• Image/Video Processing
• +3 more

Dr. Shan Du is an Assistant Professor in the Department of Computer Science, Math, Physics & Statistics at the University of British Columbia Okanagan Campus. She holds a PhD in Electrical and Computer Engineering from UBC (2009) and has over 15 years of experience in image/video processing, computer vision, and machine learning. Previously, she worked as an Assistant Professor at Lakehead University and as a Research Scientist at IntelliView Technologies. Her research focuses on computer vision, deep learning, and biometrics, with applications in video surveillance systems and environmental monitoring. She has secured grants including NSERC Discovery, CFI JELF, and Alberta Innovates. Dr. Du is a Senior Member of IEEE and serves as an Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology and the IEEE Canadian Journal of Electrical and Computer Engineering. Teaching interests include Image Processing, Computer Graphics, and Software Engineering. She advises graduate students and has published extensively on topics like gas leak detection, 3D face modeling, and medical image fusion.

• Human-Machine Interaction
• Digital Twin Technology
• Industrial Augmented Reality
• +6 more

Dorothea Pantförder, Dr.-Ing., is a researcher at the Chair of Automation and Information Systems at the Technical University of Munich, working under Prof. Vogel-Heuser. Her office is located at Boltzmannstr. 15, 85748 Garching b. Munich, where she maintains regular office hours on Mondays from 8:30 a.m. to 9:15 a.m. Her research focuses on human-machine interaction with particular emphasis on digital twin technology, industrial augmented reality applications, and advanced process data visualization techniques. She has pioneered work in 3D visualization for process control systems and has extensively explored how mixed reality technologies can enhance industrial maintenance procedures and operator training in manufacturing environments. Analysis of her publication history reveals a consistent research trajectory centered around making complex industrial processes more accessible through innovative visualization techniques. Her work bridges the gap between theoretical human-computer interaction principles and practical industrial applications, particularly in the context of Industry 4.0 initiatives and cyber-physical production systems. Throughout her career, Pantförder has collaborated extensively with Prof. Vogel-Heuser and other researchers across multiple institutions, contributing to numerous research projects focused on automation technologies, information systems, and human-centered design in industrial contexts. Her work has been supported by various research grants and has contributed to several demonstration projects showcasing Industry 4.0 applications. She has been instrumental in developing visualization techniques that help operators better understand complex production processes, with applications ranging from maintenance support to team knowledge management systems. Her research has evolved from foundational work on 3D process visualization to more recent explorations of digital twins integrated with mixed reality technologies for industrial applications.

Wen LiView profile

Assistant Professor

• Software Security
• Software Engineering
• Network Security

Wen Li is an Assistant Professor at the School of Computing , Utah State University. Their research focuses on software security , software engineering , and network security , with significant contributions to analyzing multilingual software systems. Recent work highlights include: Characterization of multilingual software and language selection patterns Security analysis of cross-language interactions and runtime vulnerabilities Development of fuzzing frameworks like Pyrtfuzz and POLYFUZZ Investigation of dynamic information flow and memory management in complex systems Research trends demonstrate expertise in software testing, language interoperability, and security verification across programming environments.

Xiaoyin WangView profile

Associate Professor

• Natural Language Processing
• Program Analysis
• Software Engineering
• +6 more

Xiaoyin Wang is an Associate Professor in the Department of Computer Science at the University of Texas at San Antonio, with affiliations to the College of AI, Cyber and Computing. She previously held a PostDoc position at UC Berkeley's Department of Electrical Engineering and Computer Science. Ph.D. in Computer Science, Peking University (2012) B.Eng. in Computer Science, Harbin Institute of Technology Her research focuses on Natural Language Processing , Program Analysis , Software Engineering , and Software Security . Key sub-areas include: Software migration and compatibility Build system automation Testing and debugging methodologies Privacy policy enforcement in mobile apps Deep learning for software analysis Augmented/Virtual Reality testing Recent publications (2022-2024) emphasize automated oracle prediction for AR/VR testing, data-sensitive analysis, and DevOps toolchain optimization. Trends include: Adaptation of program analysis to immersive environments Privacy-preserving software engineering Empirical studies on testing frameworks Academic accolades include: NSF CAREER Award (2019) President's Research Achievement Award (UTSA, 2019) Multiple best paper awards at ICSE, TOSEM She has advised 7 graduate students, including: Current: Rodney Rodriguez, Xueling Zhang, Sunzhou Huang Graduated: Xue Qin (Villanova), Foyzul Hassan (UM-Dearborn), Shaikh Mostafa (Amazon/Microsoft) Laboratory work involves tools like: TranStrL: Software internationalization Hima: API mapping for framework evolution NetworkProfiler: Android app fingerprinting DOVAR: AR/VR visualization for education Uniloc: CI failure diagnosis

Ramin MoghaddassView profile

Associate Professor

• Predictive Analytics
• Machine Learning
• Industrial Engineering
• +4 more

Ramin Moghaddass is an Associate Professor in the Department of Industrial Engineering at the University of Miami's College of Engineering. He serves as Director of both the Industrial Research and Assessment Center and the Building Training, Research, and Assessment Center. His work bridges machine learning with industrial engineering to solve complex system monitoring and maintenance challenges. University of Miami, College of Engineering Director, Industrial Research and Assessment Center Director, Building Training, Research, and Assessment Center His research focuses on: Deep state-space modeling for dynamic systems Graph neural networks for smart grid and network anomaly detection Thermal-RGB sensor fusion for manufacturing plant efficiency Image processing for vegetation risk analysis in power networks Bayesian filtering techniques with stochastic neural networks Recent publications highlight trends in sensor-driven system modeling (2025), thermal-RGB fusion for HVAC optimization (2025), anomaly detection in smart grids (2025), and adaptive inspection protocols for large-scale networks (2024). His work combines recurrent neural networks with dynamic Bayesian layers for predictive analytics while exploring graph topology integration. Contact: rxm991@miami.edu | (305) 284-9505

Bo LuoView profile

Professor

• Machine Learning Security
• Internet of Things (IoT) Security
• Cyber-Physical Systems (CPS) Security
• +6 more

Bo Luo is a Professor in the Department of Electrical Engineering and Computer Science at the University of Kansas . He serves as Director of the High Assurance and Secure Systems (HASS) Research Center within the Institute for Information Sciences (I2S) , a National Center of Academic Excellence in Cyber Defense and Research by the National Security Agency. Education: Ph.D. in Information Sciences and Technology, Pennsylvania State University (2008) M.Phil. in Information Engineering, Chinese University of Hong Kong (2003) B.E. in Electronic and Information Engineering, University of Science and Technology of China (2001) His research focuses on security and privacy at the intersection of data science, AI/ML, IoT/CPS, and network security . Current projects include adversarial machine learning, privacy compliance in smart devices, and hardware-enabled security solutions. He leads the InfoSec Research Group , mentoring students in areas like IoT security, deep learning vulnerabilities, and cryptographic systems. Recent article trends highlight IoT device vulnerabilities (2025), privacy compliance in automotive apps (2024), adversarial AI-art detection (2024), and secure computation frameworks (2024). His work appears in top venues like ACM CCS , USENIX Security , and IEEE TDSC . Scientific Recognition: ACSAC 2021 Distinguished Paper Award ACSAC 2017 Best Paper Award CCS 2022 Best Paper Honorable Mention ICPC 2024 Distinguished Paper As Principal Investigator for the Jayhawk SFS CyberCorps Scholarship , he trains future cybersecurity professionals. His lab collaborates on cyber-physical security and AI safety , with alumni placed at institutions like Beloit College, Apple, and Amazon.

Muhammed Fatih ADAKView profile

Associate Professor

• Computer Software
• Data Mining
• Artificial Learning
• +7 more

Muhammed Fatih ADAK is an Associate Professor at Sakarya University's Faculty of Computer and Information Sciences, Department of Computer Engineering. His work spans software engineering, artificial intelligence, and sensor systems with applications in energy, healthcare, and industrial automation. Doctorate: Sakarya University (2016) - Electronic Nose Data Classification with AI Master's: Kocaeli University (2012) - Elevator Simulator Design Bachelor's: Dokuz Eylul University (2009) - Computer Engineering Research focuses on machine learning , software testing , fuzzy logic , anomaly detection , and decision support systems . His articles (2013-2024) explore AI-driven solutions for natural gas monitoring, medical diagnostics, and industrial processes. Recent publications include systematic reviews of AI integration in software engineering phases and predictive models for gas consumption and voice pathology. He supervises graduate students and contributes to academic projects like Smart Gas Sensor Systems and Adaptive Online English Placement Exams . Teaches courses: Data Structures, Fuzzy Logic, Software Testing, Java Programming Co-authored books: Principles of Programming Languages , Visual Programming Managed EU-funded projects like HOPE (street rehabilitation) and AI-Enhanced Performance Evaluation Platforms Develops applications for visually impaired education and workplace safety systems

Benedikt LorchView profile

Researcher

• Steganography
• Multimedia Forensics
• Machine Learning
• +3 more

Dr. Benedikt Lorch is a researcher currently affiliated with the Security and Privacy Lab at the University of Innsbruck, Austria. His professional journey includes roles as a Postdoctoral Fellow (2023–2024) and Research Assistant (2022–2023) at the same institution, preceded by research and academic roles at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany, including a Ph.D. in Computer Science (2018–2023) focused on cybercrime and forensic computing. He has also held visiting researcher positions at Dartmouth College (USA) and Imperial College London (UK). His research interests center on steganography, steganalysis, multimedia forensics, and machine learning applications in digital security. Notable work includes analyzing JPEG image directionality for security implications, improving steganalysis techniques using leaked cover thumbnails, and developing reliable machine learning models for forensic image analysis. His contributions address challenges in digital forensics, such as detecting image tampering, authenticating multimedia content, and enhancing degraded license plate recognition. Key academic awards include the ASQF award for outstanding studies (2019) and a DAAD scholarship for international research (2017). His work spans projects like UNCOVER and emphasizes compliance with regulations like the Artificial Intelligence Act in forensic analysis. Beyond technical contributions, Lorch has explored medical imaging applications, including motion artifact detection in MRI and heart rate estimation using wearable sensors. His research trends reflect a focus on leveraging probabilistic methods and deep learning to enhance forensic reliability, with a particular emphasis on JPEG-based steganalysis and digital image security. Collaborations with institutions like the Pattern Recognition Lab and Biomedical Image Analysis Group highlight interdisciplinary engagement in computer science and medical imaging.

• Hydraulics
• Hydroacoustics
• Hydroinformatics
• +6 more

Dr. Wei Zeng is an ARC DECRA Fellow at the School of Architecture and Civil Engineering (University of Adelaide), specializing in hydraulics, hydroacoustics, and smart infrastructure systems. His research focuses on integrating machine learning, IoT, and physics-based modeling for water network monitoring, hydraulic transient analysis, and pipeline condition assessment. He leads the development of anomaly detection systems commercialized via the startup Adelitics Pty Ltd, deployed in five water utilities globally. His work spans smart water networks, sustainable asset maintenance, and pumped hydro energy storage, with contributions to CFD, control systems, and sensor fusion technologies. He supervises PhD students in related fields and teaches engineering courses on structural dynamics and fluid mechanics. Research Interests: Smart IoT-enabled water infrastructure systems Hydraulic transient modeling for aging energy systems Physics-informed machine learning for civil systems Hydroacoustic-based pipeline condition assessment Pumped hydro energy storage integration with renewables Key Contributions: Commercialized IoT leak detection systems through Adelitics Pty Ltd Pioneered paired-impulse response function (IRF) methods for pipeline diagnostics Developed transient prediction frameworks using physics-informed neural networks Advanced sensor placement optimization strategies for sewer systems Teaching & Supervision: Eligible to supervise PhD/Masters students in hydraulics, hydroinformatics, AI, and CFD. Active in courses covering structural analysis, fluid dynamics, and engineering statics.