Find Academic Supervisors

Marko ŠaracView profile

Professor

• Cybersecurity
• Artificial Intelligence
• Blockchain Technology
• +7 more

Marko Šarac is a Professor at Singidunum University in the Faculty of Informatics and Computer Science . He holds a Master's degree in Contemporary Information Technologies (2008) and a PhD in Advanced Protection Systems (2013) from the same institution. His research spans Cybersecurity, Artificial Intelligence, Blockchain, Internet of Things (IoT), Machine Learning, and Data Privacy . Education: Master: Contemporary Information Technologies, Singidunum University, 2008 PhD: Advanced Protection Systems, Singidunum University, 2013 Research Focus: SSL Traffic Security, Virtual Datacenters, Biometric Cryptography, and IoT Healthcare Systems Developed frameworks for Explainable AI in Metaverse Security , Blockchain-based IoT Security Gateways , and Machine Learning for Medical Diagnostics Notable Publications: 2025: CNN-enhanced attack detection for IoT-based Metaverse 2024: Modified Firefly Algorithm for medical dataset classification 2023: Space weather prediction using metaheuristics Projects: Co-author on 9+ books including Internet Marketing (2020) and Computer Network Security (2014) Contributed to 50+ peer-reviewed journals and conference papers on cybersecurity and AI Grants & Collaborations: Active in IEEE , ZINC , and Sinteza conference series Collaborated with researchers across Europe and Asia on IoT, Blockchain, and Cloud Security Contact: msarac@singidunum.ac.rs

• Cybersecurity
• Privacy
• Networking for Big Data
• +4 more

Shui Yu is a Professor of the School of Computer Science in the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS), where he also serves as the Deputy Chair of the UTS Research Committee. His academic career spans over 20 years in Australia and 7 years in China, with additional teaching experience in Hong Kong and Indonesia. He has developed more than 10 units in cybersecurity, computer science, data analytics, and computer games, serving as the Course Director for Computer Science undergraduate programs. Professor Yu's research interests center on cybersecurity, privacy, networking aspects of Big Data, and applied mathematics for computer science. He pioneered the field of 'networking for big data' in 2013 and edited the seminal book 'Networking for Big Data' published in 2015. His work has practical applications in industry, including Amazon Cloud's auto-scale strategy against distributed denial-of-service attacks. Current research focuses include privacy and security concerns associated with big data, security issues in smart grids, anonymous transactions on Blockchain, and anonymous communication for web browsing privacy. Analysis of his recent publications reveals a strong research trajectory spanning cybersecurity, privacy-preserving technologies, networking for big data, and applied mathematics. His work shows increasing focus on quantum-resistant cryptography, federated learning security, and adversarial robustness in AI systems. The interdisciplinary nature of his research bridges theoretical foundations with practical applications in IoT, blockchain, and cloud environments. Fellow of IEEE (2023) Distinguished Lecturer of IEEE Communications Society (2018-2021) Distinguished Visitor of IEEE Computer Society (2022-2024) Professor Yu has secured numerous research grants from the Australian Research Council, including current projects on privacy and fairness in high intelligence models (DP240100955), improved security and privacy for online platforms (LP220200808), and secure blockchain for financial applications (LP220100453). He has served on editorial boards of multiple IEEE journals including IEEE Communications Surveys and Tutorials, IEEE Communications Magazine, and IEEE Internet of Things Journal. His service extends to organizing major conferences such as IEEE Globecom 2015 and IEEE INFOCOM 2016-2017.

Tracy CampView profile

Professor

• Ad Hoc Networks
• Wireless Sensor Networks
• Machine Learning
• +3 more

Tracy Camp is a Professor and Founding Department Head of Computer Science at the Colorado School of Mines. She leads the Toilers research group, focusing on ad hoc networks and wireless sensor systems for geosystems. With over 20 NSF grants and $20M in funding, her work has produced 12 software tools used globally. She holds ACM and IEEE Fellowships, a Fulbright Scholarship, and the Mines Outstanding Faculty Award. Education: B.S. Mathematics, Kalamazoo College (1987) M.S. Computer Science, Michigan State University (1989) Ph.D. Computer Science, The College of William & Mary (1993) Research Interests: Her work bridges machine learning and geosystems, including dam integrity monitoring via seismic data, UAV communication protocols, and secure encrypted traffic classification. She emphasizes interdisciplinary approaches for real-world challenges like disaster response and environmental safety. Awards: ACM Fellow (2017) IEEE Fellow (2015) NSF CAREER Award (2007) Fulbright Scholar (2006) Grants & Impact: Over 80 refereed publications and 12 invited articles, cited ~7,000 times. Her grants include initiatives to broaden participation in computing, such as the S-STEM scholarship program. Software tools developed under her grants have been adopted by 3,000+ researchers in 86 countries. Labs & Teams: Directs the Toilers group, advancing ad hoc network evaluation and geophysical monitoring. Collaborates on projects like the ADMIRE dam monitoring system and the DREAM master’s program for underrepresented students.

• IoT Security
• Network Measurement
• Privacy Engineering
• +3 more

Dr. Anna Maria Mandalari is an Assistant Professor at University College London (UCL), affiliated with the Electronic and Electrical Engineering Department. She serves as Director of the SafeNetIoT laboratory and the Internet Engineering/Telecommunications MSc/MRes Programme at UCL. She holds an Honorary Research Fellow position at the Institute for Security Science and Technology at Imperial College London. Her research focuses on IoT security, privacy engineering, and network measurement techniques, particularly analyzing information exposure from consumer devices and developing adaptive network security strategies. Her work has led to significant contributions such as the IMC'19 Community Contribution Award-winning study on multidimensional IoT device exposure and the Telekom Challenge-winning IoTrim prototype for blocking non-essential IoT traffic. She has secured funding through InnovateUK’s CyberASAP program and participated in high-impact projects like NATwatcher and the European roaming analysis initiative. Key Awards: Community Contribution Award (IMC'19), Best Paper Award (C2B(I)D 2015) Grants: InnovateUK CyberASAP Phase 2 funding, Telekom Challenge Development Stream Top 5 prize Labs/Teams: Director of SafeNetIoT Lab, Leading M2M and IoT security teams at UCL Her recent work addresses IoT device misactivation risks (e.g., smart speakers), scalable detection of IoT devices in the wild, and protocol design informed by crowdsourced data. She actively collaborates with industry partners like Telekom and academic institutions on network infrastructure security and performance optimization.

Juan CaballeroView profile

Research Professor

• Computer Security
• Systems Security
• Software Security
• +4 more

Juan Caballero is an Associate Research Professor at the IMDEA Software Institute in Madrid, Spain. He holds a Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University, USA, along with a M.Sc. from KTH Royal Institute of Technology (Sweden) and a Telecommunications Engineering degree from Universidad Politécnica de Madrid (UPM, Spain). His research focuses on computer security, including systems, software, and network security, with a particular emphasis on program analysis techniques for malware analysis and defense mechanisms. He also explores applications of machine learning in security and the economic aspects of cybercrime. His academic career includes roles such as TPC chair for ACSAC 2018 and DIMVA 2016, and he has contributed to numerous conferences and workshops. His work spans malware lineage analysis, TLS deployment studies, and the development of tools like AVClass for malware labeling. Caballero has been recognized with prestigious awards, including the La Caixa Fellowship (2003) and Outstanding Paper Awards at Usenix Security 2011 and the Best Paper Award at USENIX Security 2007. His research interests are further detailed in publications covering topics like malware distribution, cryptographic key analysis, and network security. Caballero’s contributions to the field include foundational work in protocol reverse-engineering, binary code analysis, and proactive detection of malicious infrastructure.

Domenico CiuonzoView profile

Assistant Professor

• Information Fusion
• Statistical Signal Processing
• Wireless Sensor Networks
• +9 more

Dr. Domenico Ciuonzo is an Assistant Professor at the University of Naples Federico II , Department of Electrical Engineering and Information Technologies. His research focuses on Information Fusion , Statistical Signal Processing , Wireless Sensor Networks , Network Analytics , Artificial Intelligence , and Wireless Communications . He actively explores applications in deep learning , encrypted traffic classification , and IoT security . His recent work emphasizes explainable AI , quantum machine learning , and class incremental learning for network intrusion detection. Over 30+ publications span topics like decision fusion in MIMO systems , mobile app traffic analysis , and cybersecurity for IoT . He is also involved in dataset development (e.g., MIRAGE-APP× ACT-2024) and algorithmic frameworks for encrypted traffic classification . Key research trends include: Optimizing holographic communication systems and reconfigurable intelligent surfaces for wireless networks Developing attack-adaptive intrusion detection systems resilient to emerging threats Advancing quantum computing applications in network monitoring and prediction His research integrates statistical signal processing with modern AI techniques , addressing challenges in data privacy , network security , and resource-constrained environments .

• Cyber Security
• Internet of Things
• Blockchain
• +4 more

Dr. Baraq Ghaleb is an Associate Professor within the Centre for Distributed Computing, Networks and Security at Edinburgh Napier University's School of Computing Engineering and the Built Environment. He actively delivers and leads modules for both Undergraduate and Postgraduate programs in the Cyber Security and Systems Engineering subject group. Dr. Ghaleb earned his PhD from Edinburgh Napier University in June 2019, following completion of his MSc and BSc degrees. His academic journey has positioned him as an expert in cybersecurity and IoT technologies. His research focuses on investigating security vulnerabilities of Internet of Things standards and utilizing cutting-edge advancements to address these vulnerabilities. With expertise spanning Cyber Security, Internet of Things, Blockchain, and Machine Learning, Dr. Ghaleb bridges theoretical research with practical applications, particularly in securing IoT ecosystems. His recent work shows a clear progression from foundational networking research to contemporary security challenges involving blockchain, cryptography, and AI-enhanced security solutions. Dr. Ghaleb has secured significant research funding as Principal Investigator and Co-Investigator across multiple projects with a total budget of approximately £435,000. His externally funded projects include SafeNet (Carnegie Trust), Trusted Threat Sharing (Innovate UK), TruElect (Innovate UK), and LastingAsset (Innovate UK). He currently supervises numerous PhD students working on diverse security challenges: Blockchain-based Privacy-preserving Cybersecurity Intelligence Sharing (Elfatih Ahmed) Enhancing Security and Privacy of Blockchain-based Healthcare Systems (Faneela) Design of complex encryption schemes for IoT security (Shahbaz Khan) Intelligent and Privacy-Preserving Security Solutions for IoT Networks (Iain Baird) Dr. Ghaleb is affiliated with the Centre for Distributed Computing, Networking and Security and the Centre for Cybersecurity, IoT and Cyberphysical Systems, where he contributes to cutting-edge research in secure network architectures, cryptographic techniques, and privacy-preserving frameworks across multiple domains including automotive supply chains, healthcare systems, and environmental monitoring.

Arash Habibi LashkariView profile

Associate Professor

• Cybersecurity
• Network Security
• Threat Hunting
• +7 more

Dr. Arash Habibi Lashkari is an Associate Professor and Canada Research Chair in Cybersecurity at York University's School of Information Technology. He holds a PhD from the University of Technology Malaysia and completed a Post-Doc at the University of New Brunswick. With over 25 years of teaching experience, he specializes in cybersecurity risk management, malware analysis, and threat hunting. He pioneered Canada’s first cybersecurity Capture the Flag (CTF) competition for post-secondary students and has authored 10 books and over 110 academic articles. Research Interests: Cybersecurity, Network Security, Threat Hunting, Malware Analysis, Cybersecurity Risk Management, and Blockchain Security. He leads the Behaciour-Centric Cybersecurity Center (BCCC), focusing on vulnerability detection technologies and cybersecurity dataset generation. Awards: Mitacs 150 Top Researchers (2017), University of New Brunswick Teaching Innovation Award (2020), Gold Medal at 2020 Canadian Online Publishing Awards, and multiple international security competition awards. His work has been featured in CBC, CIC, and global cybersecurity conferences. Key Projects: Development of intrusion detection datasets (e.g., CIRA-CIC-DoHBrw-2020), malware analysis frameworks, and AI-driven cybersecurity tools. He actively collaborates with organizations like Aviva and NICT on cybersecurity resilience strategies. Teaching: Offers courses in Digital Forensics, Network Security, and Cybersecurity Management. His 'Think-Que-Cussion' teaching methodology emphasizes interactive learning.

Mitra BaratchiView profile

Associate Professor

• Spatio-Temporal Data Analysis
• Machine Learning
• Mobility Data Modeling
• +5 more

Mitra Baratchi is an Associate Professor at the Leiden Institute of Advanced Computer Science (LIACS) , Leiden University. She leads the Spatio-temporal data Analysis and Reasoning (STAR) research group, co-leads the Automated Design of Algorithms (ADA) group, and founded the Special Interest Group on Spatio-Temporal Data Mining (SIG-SDTM) . PhD from University of Twente (Mobility Data) Master’s/Bachelor’s in Computer Engineering, Iran Research Interests focus on automated pattern extraction from spatio-temporal data across urban, environmental, and industrial domains. Key applications include: Automated Machine Learning (AutoML) for Earth Observations Time-Series Forecasting for public health (e.g., pandemic modeling) Urban Mobility Optimization with ESA, Honda, and municipalities Reliable Vehicular Communication Systems Smart Garments for Health Risk Detection Geocast Protocols for Internet-wide Communication Grant Highlights include €120K NWO-Aspasia, €2.9M Marie Skłodowska-Curie, €350K NWO-KLEIN, and €135K Center for BOLD Cities funding. She has supervised 12 PhD students and 4 current Master’s students since 2011, with notable best paper award at WWIC'16. Teaching includes Machine Learning (2020-present) and Urban Computing (2018-present) at Leiden, plus past courses in Data Visualization, Software Engineering, and Research Methods.

• Cybersecurity
• Internet of Things (IoT)
• Artificial Intelligence (AI)
• +12 more

Jawad Ahmad is a Lecturer in the School of Computing, Engineering and the Built Environment at Edinburgh Napier University . His work is closely associated with the Centre for Cybersecurity, IoT and Cyberphysical Systems and the Centre for Distributed Computing, Networking and Security , where he contributes to cutting-edge research in secure and intelligent systems. His research interests are centered on cybersecurity , artificial intelligence , and Internet of Things (IoT) technologies. He focuses on developing advanced intrusion detection systems, privacy-preserving frameworks using federated learning and homomorphic encryption, and secure data transmission mechanisms leveraging chaos-based and quantum-inspired encryption. His interdisciplinary work extends to healthcare, smart agriculture, and environmental monitoring, demonstrating the broad applicability of his research. The analysis of his recent publications reveals a strong trend toward AI-driven security solutions, particularly using deep learning models like transformers and attention mechanisms for network intrusion detection. He also explores the integration of machine learning with blockchain and distributed ledgers for trusted threat intelligence sharing. His work consistently emphasizes real-world deployment, performance optimization, and resilience against cyber threats in industrial and healthcare settings. Funded Research Projects: Data Sharing in Highly Secure Environments (Innovate UK, £273,181) PhD Studentship on Homomorphic Encryption (6G Health Institute GmbH, £35,082) TrustShare: Privacy-Preserving Threat Intelligence Sharing (Innovate UK, £31,386) Cyber Hunt: Automated Cyberthreat Hunting (Norway Research Council, £37,500) AI Dashboard for COVID-19 Sentiment Analysis (Chief Scientists Office, £135,104) Dr Ahmad actively supervises postgraduate research, currently serving as Director of Studies for Hisham Ali and as second supervisor for other PhD candidates. He is involved in multiple collaborative research teams focusing on cybersecurity, AI, and IoT, often working with Prof Bill Buchanan and other leading researchers in the field. His research is published in high-impact journals such as IEEE Access , Frontiers in Computational Neuroscience , and Sensors , and presented at international conferences.

Furat Al-ObaidyView profile

Lecturer

• Machine Learning
• Intelligent Systems
• VLSI Circuits
• +7 more

Furat Al-Obaidy is a Lecturer in the Department of Electrical and Computer Engineering at the University of Michigan-Dearborn's College of Engineering and Computer Science. His expertise spans machine learning, intelligent systems, VLSI circuits, multi-core systems, FPGA architecture, and deep learning applications. PhD in Electrical & Computer Engineering from Ryerson University (2021) MSc in Electrical & Computer Engineering from Ryerson University (2016) MSc in Control & Instrumentation Engineering from University of Technology, Baghdad (1999) BSc in Control & Systems Engineering from University of Technology, Baghdad (1996) His research focuses on power-aware computing systems, thermal imaging for IC testing, hybrid cache architectures, and AI-driven network optimization. He has published extensively on topics including GPGPU power management, 3D NoC routing, and wireless sensor networks. His recent publications highlight applications of neural networks in cache optimization, FPGA architecture, and thermal imaging for hardware diagnostics. Earlier work includes control system simulations for wind turbines and power factor analysis in electrical circuits. Ryerson Graduate Development Award (2021) Ontario Graduate Scholarship Award (OGS) (2020) Ryerson Graduate Fellowship (2018) Graduate Research Excellence Award (2017) Queen Elizabeth II Graduate Scholarship in Science and Technology (2017)

Ning YuView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +6 more

Ning Yu serves as an Associate Professor in the Department of Computing Sciences within the School of Arts & Science at State University of New York Brockport. He earned his Ph.D. in Computer Science from Georgia State University and joined SUNY Brockport in 2017 after serving as a Tenure-Track Assistant Professor at the University of South Carolina Upstate. Georgia State University, Computer Science, Ph.D. Southern Illinois University Carbondale, Computer Science, M.S. Dr. Yu's research spans artificial intelligence, network and information security, big data analytics, deep learning, and cloud computing with significant applications in bioinformatics. His work demonstrates a clear progression from foundational AI and security research toward specialized applications in healthcare and energy systems. The most recent publications show increased focus on graph-based deep learning approaches for biomedical problems, particularly in cancer genomics and drug response prediction. His scholarly impact includes over 40 publications in prestigious venues including ACM/IEEE Transactions, BMC, PLoS, and Information Sciences. The publication trend reveals consistent output with increasing emphasis on interdisciplinary applications, particularly at the intersection of AI and biomedical research. Teacher of The Year, School of Science and Art, SUNY Brockport 2022-2023 Influential Professor 2022, SUNY Brockport, Fall 2023 Provost Post-Tenure Scholarship Award, $3,500, SUNY Brockport, Spring 2024 Google Research Credits Grants (2018, 2020-2021) WORLDWIDE TOP 10 FINALISTS, IBM 2017 Watson Analytics Global Competition As an educator, Dr. Yu has mentored numerous undergraduate researchers who have presented at national conferences including NCUR and SURC. He founded SUNY Brockport's first ACM SIGAI Student Chapter and has secured significant funding including a multi-campus SUNY IITG grant for AI education development. His research group actively recruits students for projects involving cloud development (Azure/GCP/AWS), CI/CD, Docker/K8s, and software architecture. Prior to academia, Dr. Yu accumulated 10 years of professional experience in software development and system networking with certifications from Cisco, Microsoft, and Google.

Keyang YuView profile

Assistant Professor

• Cyber-Physical Systems
• Edge Computing
• Tiny Machine Learning
• +4 more

Dr. Keyang Yu is an Assistant Professor in the Department of Computer Science at Marquette University, affiliated with the College of Engineering. His research focuses on designing data-driven computer systems to enhance cybersecurity and user privacy in Cyber-Physical Systems (CPS) and the Internet of Things (IoT). He teaches COSC 6280: Advanced Computer Security. Research interests include Cyber Physical Systems, Edge Computing, Tiny Machine Learning, and Security & Privacy. He emphasizes privacy-aware automation in CPS at various scales. Recent work explores challenges in IoT privacy preservation despite traffic reshaping and vulnerabilities in smart home router operating systems. Publications highlight innovations such as PACAS (privacy-aware smart cameras), TrafficSpy (disaggregating encrypted IoT traffic), and SmartAttack (open-source security research tools). His work spans both theoretical frameworks and practical implementations in smart home security and renewable energy diagnostics. No scientific awards are explicitly listed, but ongoing contributions to IoT privacy and CPS security reflect impactful research. Advising and grant details are not provided in available texts. His work connects to labs focused on embedded systems security and privacy-preserving technologies.

• Digital Forensics
• Cybersecurity
• IoT Authentication
• +9 more

Dr. Abdulghani A. Ahmed is a Senior Lecturer in Digital Forensics and Course Leader for MSc Cybersecurity at the School of Computer Science and Informatics, De Montfort University (DMU), UK. He is affiliated with the Cyber Technology Institute (CTI) and has a long-standing academic career since 2004, previously serving as a Senior Lecturer at University Malaysia Pahang. University: De Montfort University School: School of Computer Science and Informatics Role: Senior Lecturer, Course Leader (MSc Cybersecurity) Email: aa.ahmed@dmu.ac.uk ORCID: 0000-0001-9748-6067 Education: PhD in Network Security & Intrusion Detection Systems, Universiti Sains Malaysia (2014) MSc in Network Forensics & Identity Spoofing Investigation, Al-Neelain University (2006) BSc (Hons) in Computer Science, Sudan University of Science and Technology (2002) Dr. Ahmed’s research focuses on critical areas in cybersecurity, including digital forensics, IoT authentication, big data privacy, cloud and network security, malware analysis, incident response, and cybercrime investigation. His work integrates machine learning, bio-inspired algorithms, and proactive forensic models to enhance cyber resilience. He has led numerous research projects and published extensively in top-tier journals such as IEEE Access, Sensors, and Springer. The recent publications reflect a strong trend toward digital forensics in cloud and mobile environments, bio-inspired security frameworks, and machine learning applications in cybercrime detection. His work bridges theoretical innovation and practical deployment, particularly in real-time intrusion detection and evidence collection. Scientific Awards and Honors: Multiple Gold, Silver, and Bronze medals from international innovation exhibitions (MTE2018, Citrex, iCAN, ICE-CINNO) CENDEKIA BITARA AWARD 2016 for high-impact journal publication Nominated for Best PhD Thesis Award at USM (2015) Three-time High Impact Publication Award (2011–2013) at USM Dr. Ahmed actively supervises PhD students and has secured significant research funding as Principal Investigator from Innovate UK, COMSTECH-TWAS, and Malaysian government grants. His consultancy work includes cybercrime investigation and incident response for SysArmy Sdn Bhd and Golden Carousel Sdn. Bhd. He is a Senior Member of IEEE and IAENG, and holds professional certifications including SFHEA (UK), Cellebrite CCO, and digital forensics credentials from AccessData and Condition Zebra. He leads the Cyber Technology Institute research group at DMU, focusing on next-generation cybersecurity solutions. His projects include proactive forensic models, IoT security frameworks, and AI-driven botnet detection systems, positioning him at the forefront of applied cybersecurity research.

Murat KantarciogluView profile

Professor

• Data Privacy
• Computer Security
• Machine Learning
• +5 more

Murat Kantarcioglu is an Ashbel Smith Professor of Computer Science at the University of Texas at Dallas within the Erik Jonsson School of Engineering and Computer Science. He holds visiting appointments at UC Berkeley and Harvard University, focusing on data privacy and security. With a Ph.D. in Computer Science from Purdue University (2005), he has made significant contributions to privacy-preserving data mining, blockchain analytics, and secure machine learning. Education: Ph.D. in Computer Science (Purdue, 2005) Current Roles: Ashbel Smith Professor (2021-present), Visiting Scholar at UC Berkeley (2020-present), Affiliate at Harvard (2013-present) Past Roles: Assistant (2005-2011), Associate (2011-2015), and Full Professor (2015-2021) at UTD His research focuses on data privacy , computer security , and machine learning , particularly addressing challenges in privacy-preserving distributed data mining , blockchain analytics , and adversarial machine learning . He has pioneered techniques for secure federated learning , topological analysis of blockchain networks , and privacy-utility tradeoffs in health data systems. Recent publications reveal a strong emphasis on IoT security , graph neural network vulnerabilities , and blockchain data structures . His work combines theoretical rigor with practical implementations using technologies like Intel SGX and homomorphic encryption. Notable Awards NSF CAREER Award (2009) IEEE Technical Achievement Award (2017) AMIA Homer Warner Best Paper Award (2014) Fellow of IEEE (2022), AAAS (2020), and ACM (2016) Key Projects Privacy-Preserving Genomics Data Sharing Adversarial Learning Frameworks Smart Contract Security Medical Data Protection Systems Labs Director of Data Security and Privacy Lab Collaborations with Vanderbilt, UC Berkeley (RISE Lab), and Harvard (Data Privacy Lab)