جست‌وجوی استاد و پژوهشگر

• software engineering
• binary code analysis
• network traffic analysis

Hetman Oleksandr Igorevich serves as an Associate Professor at the National Research University Higher School of Economics (HSE) within the Faculty of Computer Science and the Basic Department 'System Programming' affiliated with the Institute for System Programming named after V.P. Ivannikov of the Russian Academy of Sciences (ISP RAS). He began his tenure at HSE in 2018 and maintains additional appointments at ISP RAS since 2007 and Moscow State University's Computational Mathematics and Cybernetics faculty since 2016. His educational background includes: 2013: Candidate of Physical and Mathematical Sciences 2007: Master's degree in Applied Mathematics and Physics from Moscow Institute of Physics and Technology 2005: Bachelor's degree in Applied Mathematics and Physics from Moscow Institute of Physics and Technology Professor Hetman specializes in software engineering, binary code analysis, and network traffic analysis. His research demonstrates consistent focus on network security, traffic classification, and binary code analysis methodologies, with publications spanning from 2014 to 2021. His work shows progressive development from foundational research on data format recovery to sophisticated network traffic analysis systems. His teaching portfolio prominently features the 'Computer Networks and Telecommunications' course for Software Engineering Master's students across multiple academic years (2020-2026). He has secured multiple research grants from Russian funding bodies including RFBR (РФФИ) projects 12-01-31417, 14-07-00606, 15-07-07652, 16-29-09632 and RGNF (РГНФ) project 16-06-00830-ОГН. His professional activities indicate active engagement in both academic instruction and research leadership within Russia's computer science community, with publications appearing in reputable Russian academic journals and international computer science publications.

• Algorithms
• Networks
• Parallel Computing
• +۳ مورد دیگر

Sandeep Bhatt is a Teaching Professor in the Department of Computer Science at Stevens Institute of Technology, within the Charles V. Schaefer, Jr. School of Engineering and Science. He holds a PhD, MS, and BS in Computer Science from MIT. His research focuses on Algorithms, Networks, Parallel Computing, and Theory of Computation, with recent contributions to Cybersecurity and Enterprise IT Risk Analysis. Dr. Bhatt's professional experience spans academia and industry, including roles as Director at Akamai Technologies, Bell Communications Research, and Yale University. He has contributed to numerous patents, such as hierarchical image segmentation and threat exchange security methods. Notable awards include the 2023 Outstanding Teacher Award from Stevens Alumni Association. His academic career emphasizes teaching and research in theoretical computer science, with courses like Algorithmic Complexity and Theory of Computation. His publications span cybersecurity frameworks, network analysis, and algorithmic advancements, reflecting a blend of theoretical rigor and practical industry application.

Omair Shafiqمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Omair Shafiq is an Associate Professor at the School of Computer Science, Carleton University. His research focuses on advanced topics in natural language processing (NLP), machine learning, and their applications in knowledge graphs, blockchain, and network security. He leads projects that integrate large language models (LLMs) with structured data to enhance information retrieval and decision-making systems. His work spans areas like explainable AI, adversarial robustness, and real-time traffic prediction. Shafiq is affiliated with Herzberg Laboratories and contributes to interdisciplinary research, including smart contracts, encrypted traffic classification, and IoT security. Education details are not explicitly listed in the provided text. His research interests emphasize practical solutions for challenges in AI ethics, data privacy (e.g., GDPR compliance), and scalable analytics for big data applications. He has developed frameworks like ECSGen/iZen for NLP tasks and CARD-B for encrypted traffic classification. His recent publications (2022–2025) highlight innovations in ensemble learning, adversarial attacks defense, and blockchain-based solutions for secure transactions. Shafiq’s work often bridges theory and practice, addressing real-world problems such as cybersecurity in vehicular networks, stock market prediction using deep learning, and user behavior analysis on social platforms. His contributions include tools like RevDet for event detection in news feeds and HybLoc for indoor localization. Though no specific awards are mentioned, his prolific publication record reflects sustained academic impact.

• Cybersecurity
• Machine Learning
• Cryptography
• +۳ مورد دیگر

Dr. Aqeel Sahi is a Lecturer in Computing at the University of Southern Queensland's School of Mathematics, Physics and Computing. He holds an MSc from Universiti Utara Malaysia and a PhD in Cybersecurity and Machine Learning from USQ. With over 15 years of teaching experience, Dr. Sahi instructs courses including Algorithms and Data Structures, Object-Oriented Programming, and Web Technologies. His research spans cybersecurity, machine learning, cryptography, and software engineering, with particular focus on developing advanced security algorithms and machine learning applications for network protection. Recent publications demonstrate strong emphasis on AI-driven security solutions, healthcare applications, and network defense mechanisms. Dr. Sahi maintains professional memberships with IEEE and ACS, bridging academic and industry expertise through his background as a senior software engineer.

Yuhong Liuمشاهده پروفایل

دانشیار

• Trustworthy Generative AI
• Blockchain Security
• IoT Anomaly Detection
• +۳ مورد دیگر

Yuhong Liu is an Associate Professor in the Department of Computer Science and Engineering at Santa Clara University's School of Engineering. She holds a B.S. and M.S. from Beijing University of Posts and Telecommunications (2004, 2007) and a Ph.D. from the University of Rhode Island (2012). Her research focuses on trust, security, and privacy in emerging technologies including Generative AI, IoT, blockchain, and social media. She leads the Trustworthy Computing Lab, which develops solutions for IoT security, misinformation detection, and energy-efficient systems. Her research spans: Generative AI : Trustworthy synthetic data generation and multi-agent collaboration Blockchain : Multi-signature schemes and decentralized consensus IoT Security : Anomaly detection and lightweight cryptography Social Media : Misinformation mitigation and privacy preservation Recent publications emphasize blockchain-enabled security, IoT anomaly detection, and AI fairness, reflecting a trend toward practical, cross-domain solutions for cyber-physical systems. Her work frequently integrates machine learning with cryptographic techniques to address real-world vulnerabilities. Awards & Honors: IEEE Rising Star Service Award (2023) APSIPA Distinguished Lecturer (2021-2022) Researcher of the Year, Santa Clara University (2019) Best Paper Awards at IEEE SocialCom 2010 & UMEDIA 2016 She advises 7+ graduate students on IoT security, blockchain, and social media analytics. She co-leads the NSF-funded EuReCa Global Center for clean energy research and has organized panels on AI ethics and disinformation. Her lab collaborates with industry partners to deploy secure edge computing solutions.

Romaric Duvignauمشاهده پروفایل

دانشیار

• Distributed Systems
• Network Security
• IoT/CPS Applications
• +۷ مورد دیگر

Romaric Duvignau is an Associate Professor at Chalmers University of Technology since 2024, affiliated with the Department of Computer Science and Engineering within the IT Faculty. His primary research focuses on distributed systems, network security, and cyber-physical systems (CPS), with an emphasis on optimizing communication and computational efficiency in distributed applications. Key projects include the READY project for continuous monitoring systems and contributions to P2P energy sharing optimization via the DEEP and HyperP2P initiatives. He has been at Chalmers since 2017 and also holds teaching responsibilities in computer networks and data-driven systems. Research Interests: Distributed Algorithms & Optimization Cybersecurity & Privacy Peer-to-Peer Energy Sharing Vehicular Networks Edge Computing & Streaming Analytics Notable Articles: His recent works include advancements in P2P energy matching algorithms (2025), Byzantine fault-tolerant consensus (2025), and vehicular data localization (2021). Earlier contributions include impactful studies on vehicular networks (DRIVEN framework, 2020) and network traffic analysis (2019). Teaching & Grants: Responsible for courses like Computer Communication (EDA343) and Data-driven Support for CPS. Active in educational innovation, exploring AI impacts on teaching and active learning tools like Kahoot/Mentimeter. Labs/Teams: Member of the Distributed Computing and Systems group at Chalmers Networks and Systems division, collaborating with researchers like Marina Papatriantafilou and Vincenzo Gulisano on energy systems and distributed algorithms.

Ram Basnetمشاهده پروفایل

دانشیار

• Information Security
• Data Mining
• Machine Learning
• +۳ مورد دیگر

Ram Basnet serves as Associate Professor of Computer Science and Co-Director of the Cyber Security Center at Colorado Mesa University, where he bridges academic research with practical cybersecurity applications. His expertise spans information security, data mining, and machine learning, with a focus on solving real-world challenges in network security and educational technology. Basnet's educational foundation includes: PhD in Computer Science from New Mexico Institute of Mining and Technology MS in Computer Science from New Mexico Institute of Mining and Technology BS in Computer Science from Colorado Mesa University His research integrates machine learning with cybersecurity to develop innovative solutions for threat detection and educational enhancement. Current projects include applying deep learning to classify anonymous network traffic, predicting student dropout in MOOCs, and designing interactive educational tools using Jupyter Notebook. Basnet emphasizes hands-on learning through real-world security scenarios in his teaching of Python, Java, C++, and web development. Recent publications reveal a dual research trajectory: cybersecurity applications (DDoS detection, Tor traffic analysis, malicious URL classification) and educational technology (computational thinking interventions, data science education). This synergy reflects his commitment to advancing both security practices and computer science pedagogy through data-driven approaches. As Co-Director of the Cyber Security Center, Basnet leads institutional cybersecurity initiatives while maintaining active engagement in campus life through faculty intramural soccer and basketball leagues. His outdoor pursuits include rafting, hiking, and biking, reflecting a balanced approach to professional and personal life.

Mohammed Abuhamadمشاهده پروفایل

استادیار

• AI Security
• Deep Learning Security
• Software Security
• +۹ مورد دیگر

Mohammed Abuhamad is an Assistant Professor in the Department of Computer Science at Loyola University Chicago. He holds dual Ph.D. degrees in Computer Science from the University of Central Florida and Electrical and Computer Engineering from INHA University (both 2020), and a Master's in Information Technology (Artificial Intelligence) from the National University of Malaysia (2013). His research focuses on AI/Deep-Learning Security, Software and Mobile/IoT Security, and Privacy, with applications in autonomous vehicles, healthcare, and social media. Key interests include adversarial machine learning robustness, behavioral biometrics, malware analysis, and misinformation detection. Dr. Abuhamad leads two research labs: the Cybersecurity Lab and AI for Secure Computing Research Lab (AISeC) . He teaches COMP 358/458: Big Data Analytics and COMP 388/487: Deep Learning during Fall 2024. He has secured significant research funding including: $3,800,000 NSF Grant for 'CyberCorps Scholarship for Service: CyberRamblers Bolster National Security' $50,000 from Dr. Scholl Foundation for online misinformation research $300,000 NSF Grant for experiential cybersecurity training

• eHealth
• Cyber-security
• Internet of Things
• +۳ مورد دیگر

Prof Ahmed Al-Dubai is a Professor at the School of Computing Engineering and the Built Environment , Napier University. His research spans eHealth , Cyber-security , and Internet of Things domains. Research interests include: Edge computing, Network security, Smart cities, Energy harvesting Recent projects focus on: AI-driven emergency vehicle tracking Temporal transcriptomics for sepsis research Chaos-based encryption for IoT He supervises students in areas like: Network optimization Medical data analytics Security protocols Current funded work includes: £3.2M EPSRC grant for multi-modal hearing aids Kuwait College funded energy harvesting solutions Royal Society security research in vehicular networks

Rocky Slavinمشاهده پروفایل

استادیار

• Computer and information security
• Mobile and IoT privacy
• Natural language processing
• +۲ مورد دیگر

Rocky Slavin is an Assistant Professor in the Department of Computer Science at the University of Texas at San Antonio (UTSA), College of AI, Cyber and Computing. His research focuses on computer and information security, mobile and IoT privacy, security patterns, and application of natural language processing to privacy policy analysis. Education: Ph.D. in Computer Science, University of Texas at San Antonio B.S. in Computer Science, University of Texas at San Antonio Slavin's research spans privacy policy alignment with code, IoT threat detection, and accessibility-focused mobile applications. He employs techniques from large language models, sensor fusion, and regulatory compliance frameworks. Recent work includes holistic AI-enabled health computing, few-shot learning for GDPR compliance, and lightweight collaborative inferencing for IoT security. Slavin's publications demonstrate interdisciplinary expertise in security, privacy engineering, and software development. His work addresses challenges in mobile privacy assurance, ambiguity in natural language policies, and developer-focused tools like PoliDroid-AS and PVDetector.

• Machine Learning
• Network Security
• Protocol Analysis
• +۴ مورد دیگر

Dr. Sen Zhao is a Professor at Chongqing University of Posts and Telecommunications, School of Computer Science, Department of Network Engineering. His research spans machine learning applications for network security, protocol analysis, and computational fluid dynamics with biomedical applications. His primary research interests include Machine Learning, Network Security, Protocol Analysis, Computational Fluid Dynamics, Fault Diagnosis, Optimization Algorithms, and Biomedical Engineering. Dr. Zhao has developed innovative approaches for binary protocol reversing using deep learning with knowledge-driven augmentation, multi-modal contrastive learning for vulnerability code representation, and reputation incentive schemes for edge tampering detection. Analysis of his recent publications (2021-2025) reveals a strong focus on applying deep learning techniques to network security challenges, with particular emphasis on protocol analysis, device fingerprinting, and secure query processing. His work bridges theoretical machine learning advances with practical security applications in networking and IoT environments. The interdisciplinary nature of his research is evident in his computational fluid dynamics work applied to biomedical problems. Dr. Zhao has mentored numerous students including Jiayuan Li, Zhen Wang, Bo Sun, Yi Zhao, and Haoyu Bin, who appear as first authors on significant publications. His collaborative network includes prominent researchers like Hongsong Zhu, Limin Sun, and Maya R. Gupta across multiple institutions.

Ferheen Ayazمشاهده پروفایل

مدرس

• Electric Vehicle Charging
• IoT Security
• Blockchain
• +۳ مورد دیگر

Ferheen Ayaz is a Lecturer at City, University of London, specializing in energy-efficient computing, blockchain, and IoT security. Her research includes vehicle-to-grid networks, quantum optimization for 6G, and adversarial robustness in AI. Awards include the Rising Star in Electronics (2024) and Spirit of Sussex Gold Award (2023). She teaches Cloud Computing and contributes to IEEE initiatives on climate and sustainability. Projects include the MAISE project for AI security at the edge and HEIF-funded demonstrations. She collaborates with Imperial College London and STMicroelectronics. PhD research focused on blockchain solutions for vehicular network security, funded by the University of Sussex and H2020 EPSRC.

• Multimedia Communication
• Signal Processing
• Image and Video Communication
• +۳ مورد دیگر

Dr. Shadan Khan Khattak is a Senior Lecturer at the Cardiff School of Technologies, Cardiff Metropolitan University since 2020. His expertise lies in Multimedia Communication and Signal Processing. Prior roles include Assistant Professorships at King Faisal University (Saudi Arabia) and COMSATS University Islamabad (Pakistan), and an R&D Engineer at Panasonic R&D Centre Singapore (2017–2018). His research focuses on image/video communication, cybersecurity in multimedia, and hardware design for signal processing. He has over 10 peer-reviewed publications in journals/conferences like IEEE Transactions on Circuits and Systems, IEEE Signal Processing Letters, and Picture Coding Symposium. He is an active IEEE member and serves as a reviewer for several journals. Recent work includes advancements in ensemble deep learning for medical imaging, privacy in metaverse environments, and FPGA-based TCAM emulation. His contributions span both theoretical research and applied engineering solutions, emphasizing interdisciplinary innovation in technology and healthcare domains.

Jhansi Prathuriمشاهده پروفایل

استاد آموزشی

• Cryptography
• Data Science
• Machine Learning
• +۳ مورد دیگر

Jhansi Rani Prathuri is an Instructional Professor in the Department of Computer and Data Sciences at Merrimack College. She holds a Ph.D. in Computer Science from the University of Hyderabad, an M.S. in Computer Science and Engineering from Andhra University College Engineering, and a B.S. in Computer Science and Engineering from Nagarjuna University. Her research focuses on cryptography, particularly chaos-based cryptographic systems, with contributions to hash functions, pseudorandom bit generators, and symmetric encryption schemes that prioritize low computational complexity and cryptanalysis-friendly design. She also works on machine learning applications in network security, medical data privacy, and social media analysis. In collaboration with Eurofins IT Solutions India Pvt Ltd, she explored machine learning-driven risk assessment for software code changes. Her recent publications emphasize homomorphic encryption, FPGA-based cryptographic implementations, and wireless ad-hoc network security.

Ayad Barsoumمشاهده پروفایل

دانشیار

• Cybersecurity
• Cloud Computing
• Data Integrity
• +۵ مورد دیگر

Ayad Barsoum is an Associate Professor of Computer Science at St. Mary's University, where he serves as the Graduate Program Director of Cybersecurity and Director of the NSA/DHS Designated Center for Cyber Excellence. He joined the university in 2013 and has been instrumental in developing the Master of Science in Cybersecurity program. His leadership secured grants from the Hearst Foundation and Valero Energy Foundation to establish the cybersecurity lab. He holds a Ph.D. from the University of Waterloo (2013), M.S. from Ain Shams University (2004), and B.S. from Ain Shams University (2000). His research integrates cybersecurity, cloud computing, data integrity, and machine learning, with a focus on practical solutions for network security and cryptographic protocols. His publications concentrate on cloud security, network protocols, and machine learning applications in cybersecurity, demonstrating consistent innovation in defensive cyber techniques. Awards: Distinguished Faculty Award (2022-2023) Amazon Web Services Education Grant Multiple research awards from University of Waterloo He serves as a reviewer for NSA CAE-CD and editorial boards, while maintaining collaborations with the Centre for Applied Cryptographic Research.