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Anuradhi WelhengeView profile

Lecturer

• Biomedical Engineering
• Telecommunications
• Fog Computing
• +3 more

Dr. Anuradhi Welhenge is a Lecturer at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences within the Faculty of Science and Engineering. She holds a BSc in Electronics Engineering from Asian Institute of Technology (Thailand), Master of Biomedical Engineering from University of New South Wales (Australia), and Doctor of Engineering in Telecommunications from Asian Institute of Technology. With international work experience across Australia, Japan, Thailand, and Sri Lanka, her research spans biomedical engineering, IoT systems, and signal processing. Research interests focus on developing novel computational approaches for healthcare applications, including fog computing-based medical diagnosis systems, wireless body sensor networks for physiological monitoring, and deep learning solutions for telehealth. Her work integrates signal processing techniques with IoT architectures to create sustainable medical technologies. Articles demonstrate strong trends in AI applications for healthcare, particularly deep learning architectures applied to medical imaging diagnostics, physiological signal processing, and IoT-based health monitoring systems. Recent publications show growing emphasis on security frameworks for medical IoT and multi-modal healthcare applications. Scientific Awards: Asian Institute of Technology Fellowships (2011, 2012) Best Paper Awards at UMEDIA 2015 and IEEE ICKII 2019 University of Kelaniya Senate Research Honours (2021, 2022) Outstanding Researcher Award (2022) Active in academic service with memberships in IEEE, IET, and Institution of Engineers Sri Lanka. Coordinates industry-academia collaborations through telehealth research and student mentorship. Leads research on sensor network implementations and computational healthcare systems through university laboratory facilities, collaborating with international teams on medical IoT innovations.

• Digital Forensics
• Cybercrime Investigation
• Information Systems Security
• +3 more

Dr. Pavel Gladyshev is an Associate Professor at the School of Computer Science, University College Dublin (UCD), Ireland. He leads the Digital Forensics Investigation Research Laboratory (DigitalFIRE) and specializes in digital forensics, cybercrime investigation, and information systems security. He established UCD's MSc programs in Forensic Computing and Cybercrime Investigation, focusing on law enforcement and private sector training. His research includes forensic technology automation, big data analysis in forensics, and human-machine interaction in investigations. Educations: Diploma in Computer Systems Engineering, Chelyabinsk State Technical University Master of Science in Computer Science (Research), UCD Doctor of Philosophy in Computer Science (Digital Forensics), UCD Research Interests: Dr. Gladyshev’s work spans forensic technology development, cybercrime analysis, and legal collaboration frameworks. He focuses on advancing investigative tools like automated inference systems, MLAT processes, and forensic device interoperability. Notable projects include the 'Decision-theoretic file carving' and 'Cryptopometry' methodologies. Grants & Leadership: Principal Investigator for the Horizon 2020-funded AIDA project (2020–2023) Recipient of UCD College of Science Teaching Award (2020–2025) Chair of CDFS Standards Committee (2012–2014) Labs & Collaborations: The DigitalFIRE Lab develops cutting-edge tools like the 'Apache NiFi data pipeline' for forensic workflows. Gladyshev collaborates with INTERPOL on IT crime strategies and chairs international conferences (e.g., ICDF2C, DFRWS EU).

Chen KanView profile

Associate Professor

• Advanced Manufacturing
• Quality Control
• Anomaly Detection
• +5 more

Dr. Chen Kan is an Associate Professor in the Department of Industrial, Manufacturing, and Systems Engineering at The University of Texas at Arlington. His research integrates data science with engineering for advanced manufacturing and smart health applications. He is affiliated with the Center on Stochastic Modeling, Optimization, and Statistics (COSMOS) and leads the Sensing, Analytics, and Intelligence Laboratory (SAIL). PhD in Industrial Engineering (2018), Penn State University MS in Industrial Engineering (2012), University of South Florida BS in Electrical Engineering (2010), China University of Mining and Technology Dr. Kan’s research focuses on sensor-based modeling, anomaly detection, and machine learning for manufacturing systems and biomedical applications. His work spans additive manufacturing, metamaterials, ECG signal analysis, and IoT-enabled smart systems. Recent projects include blockchain-integrated security for cyber-physical manufacturing and AI-driven driver readiness monitoring. His publications emphasize 3D point cloud analytics, dynamic network modeling, and deep learning applications in manufacturing and healthcare. Key trends include cybersecurity in AM (2023-2025), metamaterials optimization (2022-2024), and ECG anomaly detection (2012-2023). Scientific awards include: SME Susan Smyth Outstanding Young Manufacturing Engineer Award (2024) NSF CAREER Award (2025) NSF CPS Program Travel Support (2024) Dr. Kan has advised five PhD students and secured over $325k in federal grants, including NSF and USDOT funding. His lab SAIL develops methodologies for real-time process monitoring and optimization across manufacturing and healthcare domains.

• Wireless Sensor Networks
• Computer Networks
• Data Security
• +3 more

Maciej Gębala is affiliated with the Faculty of Computer Science and Telecommunications at Wrocław University of Science and Technology, where he holds a position in the Department of Computer Science Fundamentals . His research focuses on wireless sensor networks, ad hoc networks, data security, and energy-efficient communication systems. He has contributed to projects such as the POIG-funded monograph on measurement data acquisition and storage, and has edited conference proceedings like ADHOC-NOW 2013. His work spans technical areas including fault-tolerant protocols, GDPR-compliant reputation systems, broadcast scheduling, and algorithm design for routing and data protection. He has published extensively in international conferences and journals, addressing challenges in network optimization, energy consumption, and image reconstruction from projections. Dr. Gębala's expertise also extends to radio communication management and network topology planning, with contributions to UTRAN systems and 3D reconstruction algorithms for polyominoes. No specific grants or awards are listed in the provided information.

Nathan S. EvansView profile

Associate Professor

• Network Security
• Automotive Security
• Distributed Systems
• +3 more

Dr. Nathan S. Evans is a Teaching Associate Professor and Academic Director for Cyber Security at the University of Denver. He holds a PhD in Computer Science from Technische Universität München (2011) and has extensive industry experience, including roles as Senior Principal Research Engineer at Symantec Research Labs and CTO of CloudHawk. His research focuses on network security, automotive security, low-level systems, and distributed systems. Dr. Evans is actively involved in academic and industry initiatives such as the Colorado Research Institute for Security and Privacy (CRISP) and organizes cyber competitions like the Mountain West Cyber Challenge. Education: PhD in Computer Science, Technische Universität München (2011) MSc in Computer Science, University of Denver (2009) BSc in Computer Science and BA in Criminal Justice, Baldwin Wallace College (2006) Research Interests: Evans' work bridges theoretical security concepts with practical applications. His recent studies address emerging threats in automotive networks, mobile device security, and cloud infrastructure. He emphasizes hands-on education through initiatives like DU's Cyber Security Master’s program. Articles Overview: Recent publications highlight advancements in cryptocurrency mining analysis, container security, and automotive system vulnerabilities. His work often explores real-world implications of theoretical security models. Labs/Teams: As CRISP’s Point-of-Contact, he leads interdisciplinary efforts in cyber defense. He mentors students in competitions like CyberForce and NSA Ghidra SRE Challenge, fostering workforce development.

Anh Tu HoangView profile

Researcher

• Privacy-Preserving Systems
• Data Security
• Blockchain Technology
• +3 more

Dr. Anh Tu Hoang is a Postdoctoral Researcher at TU Hamburg's Institute for Data Engineering, specializing in privacy-preserving systems, blockchain technologies, and knowledge graph anonymization. He holds a PhD from the University of Insubria, Italy, and degrees from Vietnam National University. His research addresses data security challenges in machine learning, federated learning, and decentralized systems. Education: Bachelor of Information Technology, University of Science, Vietnam National University (Ho Chi Minh City) Master of Information Systems, University of Science, Vietnam National University PhD in Computer Science, University of Insubria, Italy (2020) Research Focus: His work centers on developing mechanisms to protect privacy and security during data sharing, machine learning training, and blockchain operations. Key contributions include time-aware anonymization for knowledge graphs and blockchain-based federated learning frameworks. Projects include CDL-BOT and Orchid, focusing on decentralized learning and privacy-aware systems. Publications: Over 12 peer-reviewed articles in top venues like IEEE ICDE, ACM TOPS, and IEEE TDSC, with a focus on anonymization techniques, federated learning, and cryptographic protocols. Recent work explores zero-knowledge proofs for self-sovereign identity and proximity marketing privacy. Expertise: Combines cryptographic methods with data privacy principles to address modern challenges in distributed systems, social networks, and collaborative AI environments.

• Artificial Intelligence
• Healthcare Informatics
• Data Science
• +5 more

Wajahat Ali Khan is an Associate Professor in Artificial Intelligence at the University of Derby's College of Science and Engineering. His research focuses on AI-driven solutions for healthcare informatics, cybersecurity, and data integration. He leads interdisciplinary projects involving knowledge graphs, semantic web technologies, and clinical decision support systems. Research interests include applying machine learning to healthcare challenges such as disease subtyping, health data interoperability, and personalized medicine. He also explores cybersecurity in network systems and SDN environments. Key contributions include platforms like the Ubiquitous Health Profile (UHPr) for health data interoperability and the Intelligent Medical Platform for dialogue-based healthcare services. His publications span topics from anti-DDoS models to sentiment analysis for depression detection. Collaborations with institutions like Sungkyunkwan University (South Korea) highlight global impact. Khan’s work emphasizes bridging semantic gaps in healthcare systems through adaptive mediation frameworks and data-driven knowledge acquisition methods.

Wes BethelView profile

Associate Professor

• Quantum Computing
• Machine Learning
• Scientific Computing
• +4 more

Wes Bethel is an Associate Professor in the Department of Computer Science at San Francisco State University. His research focuses on quantum computing, machine learning applications in scientific modeling, and high-performance computing frameworks such as the SENSEI in situ visualization system. He leads efforts in optimizing quantum algorithms, real-time fusion energy modeling, and scalable data processing for large-scale simulations. Key research themes include quantum protocols for polynomial computation, GPU-based quantum circuit simulation, and machine learning-driven approaches to plasma physics and radio frequency (RF) heating analysis. His work bridges theoretical advancements with practical implementations in energy science and computational infrastructure. Recent projects emphasize accelerating RF modeling using AI surrogates, improving in situ visualization frameworks for heterogeneous architectures, and addressing challenges in classical-quantum system integration. Contributions span both methodological innovations and applied solutions for fusion energy research and distributed computing environments. No scientific awards are listed in the provided materials. Advising and grants information is not explicitly detailed here, though his publications indicate collaborative involvement in DOE-funded projects and fusion energy initiatives. He contributes to open-source tools like SENSEI and WarpVisIt, supporting community-driven advancements in scientific computing. Research labs and teams associated with his work likely include computational science groups focused on quantum computing, plasma physics modeling, and high-performance data management at San Francisco State University and collaborating institutions.

Robert H DengView profile

Professor

• Applied cryptography
• Data security and privacy
• Mobile and wireless security
• +5 more

Professor Robert H. Deng is a distinguished academic and researcher at Singapore Management University's School of Computing and Information Systems, where he serves as Professor of Computer Science and Deputy Dean for Faculty & Research. With a PhD from Illinois Institute of Technology (1985), he has established himself as a leading authority in cybersecurity, data privacy, and applied cryptography. His research interests span applied cryptography, data security and privacy, mobile and wireless network security, trusted computing, and system security. Professor Deng has published extensively in top-tier security venues, with over 600 publications listed in DBLP. His work bridges theoretical foundations with practical applications, particularly in the domains of IoT security, blockchain systems, and privacy-preserving technologies. Professor Deng has received numerous prestigious awards, including the National Day Award (Public Administration Medal Silver) in 2020, the AXA Chair Professor of Cybersecurity (2017-2025), and the Lee Kuan Yew Fellow for Research Excellence at SMU in 2006. His recent publications demonstrate continued leadership in emerging areas like post-quantum cryptography, federated learning security, and hardware-assisted security systems. His research group has produced significant work on secure authentication protocols, privacy-preserving data analytics, and robust cryptographic systems for real-world applications. National Day Award, Public Administration Medal (Silver), 2020 AXA Chair Professor of Cybersecurity, AXA Research Fund, 2017-2025 Distinguished & Best Paper Awards at NDSS, ESORICS, IEEE ICPADS, etc. Asia-Pacific Information Security Leadership Achievements (ISLA), 2010 Lee Kuan Yew Fellow for Research Excellence, SMU, 2006 University Outstanding Researcher, National University of Singapore, 1999 As a research advisor, Professor Deng has mentored numerous students including SUN Bing. His active participation in the research community is evidenced by recent keynotes at major conferences including DSPP 2024 on post-quantum cryptography and ASIACCS 2024 on client-side encryption. His research group maintains strong industry connections and receives significant funding for cutting-edge security research.

Muhammad A B FayyazView profile

Lecturer

• Applied Artificial Intelligence
• Computer Vision
• Data Analytics
• +3 more

Dr. Muhammad A B Fayyaz is a Lecturer in Applied AI and Data Analytics at Manchester Metropolitan University Business School. As the youngest Pakistani PhD holder (awarded at age 25), he possesses extensive technical expertise in computer vision, machine learning, and business analytics. He leads units in Data Analytics and Decision Support Systems while supervising postgraduate research in applied AI domains. His interdisciplinary research encompasses cybersecurity applications (encrypted traffic analysis, blockchain security), renewable energy optimization (PV system control), and intelligent systems design (humanoid personality assessment, building automation). Fayyaz maintains active industry collaborations through national and international projects, translating technical innovations into business solutions. Recent publications demonstrate Fayyaz's versatility across AI subfields, with consistent focus on explainable methodologies and real-world applications. His work integrates theoretical advances with practical implementations in energy, transportation, and cybersecurity domains.

Rachid Ait Maalem LahcenView profile

Lecturer

• Cybersecurity
• Machine Learning
• Mathematics Education
• +3 more

Rachid Ait Maalem Lahcen is a Lecturer in the Department of Mathematics at the University of Central Florida (UCF), affiliated with the College of Sciences. He teaches courses such as MAC1105C and focuses on cybersecurity, mathematics education, and interdisciplinary research. His work bridges technical cybersecurity challenges with pedagogical innovations in STEM education. Research interests include cybersecurity frameworks, machine learning applications in security, human factors in cybersecurity behavior, adaptive learning systems, and mathematical cryptography. He has published extensively on topics like cyber threat intelligence, vulnerability analysis, image encryption techniques, and strategies for effective remote instruction. His articles reflect a strong emphasis on interdisciplinary approaches, combining cybersecurity with education technology and mathematics. Notable contributions include frameworks for resilient cyber defense, motivational strategies in mathematics labs, and adaptive courseware design for STEM retention. No scientific awards or grants are explicitly listed in the provided text. Office hours and contact details are available at MSB 221, and he contributes to UCF's outreach initiatives in the College of Sciences.

Mo ShaView profile

Associate Professor

• Wireless Networking
• Internet of Things
• Applied Machine Learning
• +3 more

Dr. Mo Sha is an Associate Professor in the Knight Foundation School of Computing and Information Sciences at Florida International University (FIU). Previously, he served as an Assistant Professor in the Department of Computer Science at Binghamton University, SUNY. His research focuses on wireless networking, Internet of Things, applied machine learning, network security, and cyber-physical systems. Sha holds a Ph.D. in Computer Science from Washington University in St. Louis (2014), an M.Phil. from City University of Hong Kong (2009), and a B.Eng. from Beihang University (2007). He has published over 50 research papers and secured significant grants, including the NSF CAREER and NSF CRII awards. His recognitions include the 2018 Educator of the Year in Computer Science and Career Champion awards at Binghamton University. His work emphasizes interdisciplinary applications of machine learning and networking, addressing challenges in industrial IoT, sensor-actuator systems, and cross-technology communication. Recent research trends include domain adaptation for wireless networks, energy-efficient sensor systems, and meta-learning for runtime network adaptation. Awards: NSF CAREER (2021), NSF CRII (2017), Educator of the Year (2018), Career Champion (2018) Memberships: Senior Member (IEEE, ACM), Sigma Xi Member Sha advises students in areas of wireless systems and cybersecurity, with a focus on real-world implementation. His lab explores cutting-edge solutions for industrial networks and smart environments, leveraging both theoretical and experimental approaches.

Gang QuanView profile

Professor

• Real-time computing systems
• Power/thermal-aware design
• Electronic design automation
• +7 more

Gang Quan is a Professor in the Department of Electrical & Computer Engineering at Florida International University (FIU). He holds a Ph.D. from the University of Notre Dame (2002), an M.S. from the Chinese Academy of Sciences (1994), and a B.S. from Tsinghua University (1991). His research focuses on real-time computing systems, power/thermal-aware design, electronic design automation, advanced computing architectures, and reconfigurable computing. He has contributed extensively to neuromorphic computing, homomorphic encryption for secure computation, and energy-efficient scheduling in multi-core and embedded systems. Key areas of expertise include thermal-aware system design for automotive and embedded applications, fault-tolerant neural networks, and resource management in data centers and cloud environments. His work integrates hardware-software co-design principles to address challenges in reliability, energy efficiency, and real-time performance. He has authored numerous peer-reviewed articles in top-tier conferences and journals. Dr. Quan's research has practical applications in improving the resilience and efficiency of computing systems across domains like automotive electronics, cloud infrastructure, and neuromorphic hardware. He maintains an active role in academic leadership at FIU, contributing to both educational initiatives and cutting-edge research projects in electrical and computer engineering.

• 6G Wireless Networks
• 5G and Beyond Networks
• Quality of Experience in Multimedia
• +3 more

Dr. M Arslan Usman is a Senior Lecturer in Computer Science at Kingston University's Faculty of Engineering, Computing and the Environment, with academic responsibilities in research, teaching (as a Fellow of the Higher Education Academy), and knowledge exchange. His global research spans 12 years across Sweden, South Korea, and the UK, including industrial R&D experience at Nokia-West Central Africa and Pangea Connected UK under Innovate UK's Knowledge Transfer Partnership. His research focuses on: 6G wireless networks and 5G/B5G technologies Quality of Experience (QoE) in medical/entertainment multimedia AI-assisted decision systems and IoT applications Multimedia networking and wireless standardization He actively contributes to ITU WP5D standardization, chairs IEEE symposiums, and reviews for IEEE Transactions on Multimedia. Current PhD supervision includes four students, with ongoing recruitment for exceptional candidates.

Kurt RohloffView profile

Associate Professor

• Cybersecurity
• Homomorphic Encryption
• Lattice-Based Cryptography
• +5 more

Prof. Kurt Rohloff is an Associate Professor in the Department of Computer Science at New Jersey Institute of Technology (NJIT). He serves as the founding director of the NJIT Cybersecurity Research Center, focusing on advanced cryptographic techniques such as homomorphic encryption, lattice-based cryptography, and secure computing. With a background in electrical engineering from the University of Michigan (Ph.D., 2004) and Georgia Tech (B.E., 1999), his career includes prior roles as a DARPA Program Investigator in industry before joining academia. His research emphasizes practical implementations of privacy-preserving technologies, including open-source libraries like PALISADE and OpenFHE , which enable secure computation on encrypted data. Key areas of contribution include FPGA-accelerated homomorphic encryption, proxy re-encryption systems, and applications in healthcare, telecommunications, and distributed computing. He has led workshops such as WAHC (Workshop on Encrypted Computing and Applied Homomorphic Cryptography), fostering interdisciplinary collaboration. Rohloff’s work bridges theoretical cryptography with real-world challenges, addressing scalability, performance optimization, and usability of secure computing frameworks. His innovations target use cases like encrypted genome analysis, privacy-preserving AI, and secure data sharing across distributed networks.