Find Academic Supervisors

• Artificial Intelligence
• Machine Vision
• Distributed Computing
• +11 more

Dr. Dan Ionescu is a Professor at the University of Ottawa's School of Electrical Engineering and Computer Science, Department of Electrical and Computer Engineering. He has been affiliated with the university since 1985, contributing extensively to teaching and research. His career includes visiting professorships at École Nationale Supérieure de Télécommunications (Paris) and Universitat Politècnica de Catalunya (Barcelona). He founded the Machine Intelligence Research Laboratory (1988) and the Network Computing and Control Research Laboratory (NCCT, 1999), which he currently directs. His research spans Artificial Intelligence, Machine Vision, Distributed Computing, Network Control, and Formal Methods. Notable contributions include methodologies in Expert Systems, Image Processing, Temporal Logic, and Network Management. His work has received industrial and governmental grants from CITO, Nortel, NSERC, and others. Recent research focuses on Web-based collaborative platforms (e.g., UC-IC, Watch-Together), Autonomic Computing, and AI applications in gesture control and 3D IR camera systems. Dr. Ionescu’s technical leadership includes pioneering the first distributed network management platform with industry partners and designing the NCIT*net 2 network architecture. His current interests include AI-driven solutions for cybersecurity, medical imaging, and IoT-enabled disaster response systems. He has advised numerous projects in AI ethics, quantum computing, and real-time control systems. His research trends emphasize interdisciplinary innovation, combining AI with healthcare (e.g., MRI analysis, deepfake detection), cybersecurity (GAN-based intrusion detection), and IoT (Salv AIoT platform). Collaborations with IBM CAS and Diatem Networks highlight his industry-academia integration. His legacy includes over 40 years of impactful contributions to computing and engineering education.

• Artificial Intelligence
• Data Science for Social Good
• Big Data Analytics
• +3 more

Dr. Neha Gupta is a teaching-focused Assistant Professor in the Information Systems Management and Analytics (ISMA) Group at Warwick Business School (WBS), University of Warwick. She holds a PhD in Urban Data Science from the Warwick Institute of Science of Cities (WISC) and is a Fellow of the Higher Education Academy (FHEA) and Warwick Institute of Higher Education (WIHEA). She co-leads the AI in Education Learning Circle and has received awards for teaching excellence and pastoral care. Education: PhD in Urban Data Science, Warwick Institute of Science of Cities (WISC), University of Warwick (2018) Her research focuses on applying AI, Data Science, and Software Engineering to Higher Education and industry sectors. She previously worked in technical, consultancy, and managerial roles at firms like AGCO Corporation, HSBC, and TATA Consultancy Services. She is PRINCE2 certified in Project Management. Key Research Areas: AI in Education Data Science for Social Good Cyber Security in Business Teaching: She teaches modules including Digital Business Services, Artificial Intelligence in Business, Programming for Business Applications, and Cybersecurity in Business across multiple programs like MSc Management of Information Systems, BSc International Management, and MSc Business Analytics. Awards: WBS CORE Award for Excellence in Teaching (2022-23) Outstanding Contribution to Pastoral Care Award (2023-24) Outside academia, she practices yoga, engages in spiritual disciplines, and volunteers with local youth programs.

Qiongkai XuView profile

Lecturer

• Natural Language Processing
• Machine Learning
• Security
• +3 more

Dr. Qiongkai Xu is a Lecturer in Natural Language Processing (NLP) at Macquarie University's School of Computing, with an honorary fellowship at the University of Melbourne. He holds a PhD in NLP from the Australian National University (ANU). His research focuses on auditing machine learning models, particularly addressing privacy/security issues in NLP/ML models and developing evaluation frameworks. Key areas include text watermarking, adversarial attacks, data leakage mitigation, and model authentication. Recent publications emphasize robustness against backdoor attacks, generative model watermarking, and healthcare applications of LLMs. His work bridges theoretical security advancements with practical NLP challenges. Awards: DAAD Scholarship (2022), Research Pitching Session Winner (MQ, 2022 & 2024) Grants: Leading the Climate Litigation Risk project on AI-driven greenwashing detection. Labs/Teams: Data Horizons Research Centre, Future Communications Research Centre, and Frontier AI Research Centre.

• Cloud Computing
• Edge Computing
• Fog Computing
• +7 more

Roles and Affiliations: Sukhpal Singh Gill is a Lecturer (Assistant Professor) in Cloud Computing at Queen Mary University of London (QMUL), UK. He leads the GillNet Research Lab and is the Editor-in-Chief of the International Journal of Applied Evolutionary Computation (IJAEC) . He also serves as an Associate Editor for journals like IEEE IoT and Nature Scientific Reports. As Programme Director for MSc Advanced Computer Science and MSc Business Analytics, he contributes to curriculum development and education excellence. Research Interests: His research focuses on Cloud Computing, Edge AI, Internet of Things (IoT), and Energy Efficiency. He explores AI-driven solutions for resource management, security, and sustainable computing. Key areas include fog-edge integration, serverless computing frameworks, and healthcare applications. Publications and Impact: With over 200 peer-reviewed publications (including IEEE TCC, Elsevier JSS, and ACM TOIT), Dr. Gill has achieved 12,500+ citations and an H-index of 54 (Google Scholar). His work has been featured in IEEE Spectrum and Tech Monitor. Notable contributions include frameworks like HealthEdgeAI (healthcare systems), CloudAISim (cloud simulation), and EdgeAISim (edge computing modeling). Awards and Recognition: Recognized with the 2024 IEEE Outstanding Reviewer Award, Elsevier Editor’s Choice Award, and Queen Mary Education Excellence Award. He is a Fellow of the Higher Education Academy (FHEA). Teaching and Leadership: Teaches modules like Cloud Computing (Postgraduate) and Semi-structured Data Modeling. Leads the Networks and Systems Teaching Group (N&STG) and chairs academic misconduct panels. Advocates for inclusive curriculum design and intercultural development in higher education. Labs and Collaborations: The GillNet Lab develops next-generation systems for EdgeAI, CloudAIBus, and CloudAISim. Collaborates with institutions like Lancaster University, The University of Melbourne, and industry partners on fog-cloud IoT ecosystems.

Alistair BarrosView profile

Professor

• Service Computing
• Enterprise Systems
• Business Process Management
• +5 more

Professor Alistair Barros is Head of School and Academic Program Leader of Service Science at QUT’s School of Information Systems (Faculty of Science). He holds a PhD from the University of Queensland, with extensive ICT experience in academia and industry (e.g., Global Research Leader at SAP AG). His research focuses on enterprise systems design, optimization, and interoperability in cyber-physical environments leveraging cloud, IoT, and blockchain technologies. Key areas include service computing methods (microservices, business process management), model-based systems re-engineering, and distributed service coordination. Education: PhD in Information Systems from the University of Queensland. Research Interests: Enterprise systems modernization, service-oriented architectures, Industry 4.0 applications, and digital transformation in sectors like construction, manufacturing, and supply chains. He has published 180+ peer-reviewed articles (including 6 edited books) and holds 17 US patents. Notable contributions include the BPMN 2.0 standard co-authorship and the 'Workflow Patterns' paper (most cited in BPM). Awards: Test of Time Award (BPM 2015), Wharton Education Award (TeachConnect platform), and impactful industry collaborations (e.g., SAP NetWeaver, Federal Government WPIT project). Grants & Projects: Led ARC, EU FP6, and industry-funded projects. Current focus includes re-engineering legacy systems for microservices, EV charging network design, and adversarial ML in cybersecurity. Labs/Teams: Active in QUT’s Centre for Data Science, focusing on service science and cyber-physical systems integration.

Daniel MosseView profile

Professor

• Computer Architecture
• Computer Systems
• Cyber-Physical Systems
• +8 more

Dr. Daniel Mosse is a Professor in the Department of Computer Science at the University of Pittsburgh. He holds a PhD from the University of Maryland, College Park, and has been a faculty member since 1993. His research focuses on distributed systems, real-time systems, power management, security, and fault tolerance, bridging operating systems and networking research. He has held leadership roles, including Chair of the Department of Computer Science since 2009 and Visiting Professorships in Brazil. His work spans grants totaling millions, addressing energy-efficient computing, secure infrastructure, and embedded systems. Education: PhD (1993), MS (1990), and BS (1985) in Mathematics and Computer Science. Research interests include distributed real-time systems, multicore systems, embedded systems, and wireless networks. Notable awards include the 2007 Provost's Innovation in Education Award and the 2006 Bellet Teaching Award. Key grants include NSF-funded projects on energy-efficient data centers, secure emergency management systems, and power-aware scheduling. He advises numerous students and teaches courses on operating systems and distributed systems. His contributions to real-time systems, fault tolerance, and energy management have been recognized through over 150 publications and international collaborations.

Garth V. CrosbyView profile

Associate Professor

• Internet of Things (IoT)
• Industrial Internet of Things (IIoT)
• Cyber-physical Systems Security
• +5 more

Garth V. Crosby is an Associate Professor at Texas A&M University's Department of Engineering Technology and Industrial Distribution within the College of Engineering. He is also affiliated with the Multidisciplinary Engineering program. His research focuses on IoT and IIoT security, cyber-physical systems, and STEM education innovation. Crosby holds a Ph.D. in Electrical Engineering from Florida International University (2007), an M.S. in Computer Engineering, and a B.S. in Electronics (Applied Physics) from the University of the West Indies. His work spans cybersecurity frameworks for emerging technologies, including blockchain-based federated learning, post-quantum cryptography, and IoT threat mitigation. He has contributed to educational advancements through online lab design and faculty efficacy studies in hybrid learning environments. Crosby's recent publications emphasize securing robotic IoT systems, supply chain blockchain applications, and ransomware evasion techniques using generative AI. His research portfolio includes over 40 peer-reviewed articles in journals like IEEE Transactions and conferences such as ASEE and FiCloud. Key themes include volunteer cloud reliability models (ProTrust), edge computing security, and educational technology evaluation. Crosby's interdisciplinary approach bridges engineering systems with pedagogical innovation, addressing both technical and human factors in modern technological challenges.

Karen Butler-PurryView profile

Professor

• Power System Automation
• Smart Grid Security
• Distribution Automation
• +4 more

Karen Butler-Purry is a Professor of Electrical & Computer Engineering at Texas A&M University, holding the Raytheon Company Professorship. She is affiliated with the Power System Automation Laboratory and the College of Engineering. Her research focuses on intelligent systems for power distribution automation, fault diagnosis, and renewable energy integration. She completed her B.S. at Southern University (1985), M.S. at UT Austin (1987), and Ph.D. at Howard University (1994). Her work emphasizes cyber-physical systems, smart grid security, and microgrid resilience. Recent research includes AI-driven grid restoration, cybersecurity frameworks for distribution systems, and self-healing shipboard power systems. She has pioneered methods for transformer lifespan prediction and unbalanced load management in distributed networks. Publications span over three decades, with notable contributions to IEEE journals and conferences. Her educational initiatives include the Texas A&M System AGEP Alliance and LSAMP programs, advancing underrepresented minority participation in STEM academia. She leads interdisciplinary projects at the intersection of power systems, machine learning, and equity-focused academic workforce development. Labs/Teams: Principal Investigator in the Power System Automation Laboratory, collaborating with industry partners like Raytheon. Active in curriculum development for digital systems education through initiatives like the Enrichment Experiences in Engineering (E³) program.

Gaurav SharmaView profile

Professor

• Data Analytics
• Cyber Physical Systems
• Signal and Image Processing
• +3 more

Gaurav Sharma is a Professor at the University of Rochester, holding joint appointments in the Department of Electrical and Computer Engineering, Computer Science, and Biostatistics and Computational Biology. He is also a Distinguished Researcher at the Goergen Institute for Data Science and Artificial Intelligence. His research focuses on Data Analytics, Cyber Physical Systems, Signal and Image Processing, Computer Vision, Media Security, and Communications. Previously, he served as Director of the Center for Emerging and Innovative Sciences (2008-2010) and held roles at Xerox Research (1996-2003). He earned a PhD in Electrical and Computer Engineering from North Carolina State University (1996), alongside degrees from Indian institutions including the Indian Institute of Science and IIT Roorkee. Education: PhD (NCSU), M.S. Applied Mathematics (NCSU), M.S. Electrical Communication Engineering (IISc), B.Eng (IIT Roorkee) Awards: Fellow of IEEE, SPIE, and IS&T; Elected Sigma Xi member Editorial Roles: Editor-in-Chief of IEEE Transactions on Image Processing (2018-2020) and Journal of Electronic Imaging (2011-2015) Leadership: Chair of IEEE Signal Processing Society committees and SPIE/EI conferences His research spans interdisciplinary areas like medical imaging analytics, cybersecurity in IoT systems, and computational methods for biomedical applications. Recent work includes studies on cardiac mortality patterns, viral protein inhibition, and federated learning for privacy-sensitive healthcare tasks. Dr. Sharma’s contributions to academic governance include roles on IEEE publication committees and strategic planning boards, reflecting his commitment to advancing technical standards and open science practices.

Mohammed ShafaeView profile

Assistant Professor

• Cyber-physical systems security
• Smart manufacturing systems
• Statistical process monitoring
• +7 more

Mohammed Shafae is an Assistant Professor in the Department of Systems and Industrial Engineering at the University of Arizona, where he has been serving since 2018. He is also a member of the Graduate Faculty, contributing to advanced research and mentorship in engineering disciplines. Education: PhD in Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia, United States MS in Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia, United States MS in Production Engineering, Alexandria University, Alexandria, Egypt BS in Production Engineering, Alexandria University, Alexandria, Egypt His research centers on cyber-physical systems security , smart manufacturing systems , and data-driven quality control , with a strong emphasis on leveraging manufacturing data for process monitoring, modeling, and securing machining and additive manufacturing systems. His work integrates statistical process monitoring, machine learning, and advanced metrology to enhance manufacturing resilience and efficiency. His recent publications (2022–2025) reflect a clear trend toward securing Industry 4.0 systems, with a focus on digital twin security, attack detection in machining, and anomaly detection in additive manufacturing using photodiode and thermal data. He also explores futuristic applications such as in-situ lunar manufacturing using regolith-based materials. Scientific Awards: First Place in the ASCEND Propel Pitch Competition (AIAA, Fall 2020) Virginia Tech Teaching Excellence Award (Spring 2017) Harold Schneikert Graduate Fellowship (Spring 2016) David H. Burrows Graduate Fellowship (Fall 2015) Best Track Paper Award (IEOM 2012) Prize for Excellence in Senior Design Project (Cairo, 2009) Dr. Shafae actively mentors graduate students, co-authoring numerous publications with advisees in areas such as cybersecurity, additive manufacturing, and process monitoring. While specific grant details are not listed, his research is clearly funded through competitive fellowships and likely external grants given the volume and quality of his outputs. He has no listed labs or teams in the provided text, but his collaborative work suggests active participation in research groups focused on smart manufacturing and cybersecurity.

• Security
• Embedded Systems Security
• Cyber-Physical Systems Security
• +13 more

Francesco Regazzoni is a Senior Researcher at the Faculty of Informatics, Università della Svizzera italiana (USI), and affiliated with the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI). His work bridges embedded systems, cybersecurity, and artificial intelligence, with a focus on securing hardware and cyber-physical systems. Research Interests: His expertise spans embedded and cyber-physical systems security, side-channel attacks, post-quantum cryptography, hardware trojans, random number generators, and the security of AI and approximate computing. He also contributes to hardware/software co-design and operating systems security. The analysis of his recent publications reveals a consistent focus on hardware and system-level security , particularly in resource-constrained environments like IoT and embedded devices. His work integrates machine learning for attack detection and applies formal methods to ensure trust in hardware. A growing emphasis is placed on securing AI systems from physical and adversarial threats. Scientific Contributions: Over 100 peer-reviewed publications One book and one patent Extensive international collaboration (Belgium, Netherlands, USA, Switzerland, Singapore) Advising and Grants: While specific advisees and grants are not listed, his leadership in funded research projects and involvement with ALaRI and IDSIA suggest active mentorship and project coordination. His work has been supported by industry (e.g., ST Microelectronics, HP), the Swiss National Foundation, and the European Union. Labs and Teams: He is part of the Graph Machine Learning Group (GMLG) at IDSIA, which evolved from the Advanced Learning and Research Institute (ALaRI). This group focuses on graph machine learning, reinforcement learning, and dynamical systems, particularly in non-stationary environments.

• Cybersecurity
• Healthcare IT Security
• Machine Learning
• +4 more

Carlos Cilleruelo Rodríguez is an Associate Professor in the Department of Computer Science at Universidad de Alcalá. He is affiliated with the TIFyC research group ("Information Technologies for Training and Knowledge") and specializes in cybersecurity, healthcare IT security, and authentication systems. His research focuses on dark web analysis, malware detection, and adaptive security frameworks. He earned his Doctorate from Universidad de Alcalá in 2023 with the thesis "A cybersecurity review of healthcare industry 2023" , supervised by Dr. José Javier Martínez Herráiz and Dr. Luis de Marcos Ortega. His work spans ransomware mitigation in manufacturing, continuous authentication architectures (JBCA), and privacy issues in IoT healthcare devices. Recent projects include the ProTego initiative for mobile authentication in eHealth environments. Publications highlight trends in machine learning for behavioral authentication, darknet interconnections, and security in Bring-Your-Own-Device (BYOD) healthcare settings. No scientific awards explicitly mentioned. Labs/teams: Active contributor to the TIFyC group, focusing on cybersecurity education and applied research in critical infrastructure protection.

Umair ShahView profile

Assistant Professor

• Stakeholder Theory
• Technology Ethics
• Computational Social Science
• +4 more

Umair Shah is an Assistant Professor (Teaching Stream) at the University of Waterloo's Department of Management Sciences. He holds a Doctorate in Management of Technology (2016) and a Master of Applied Science in Management Sciences (2012), both from the University of Waterloo. His research interests span stakeholder theory, technology ethics, computational social science, and human factors in technology adoption. He actively teaches courses such as Organizational Behavior, Production and Service Operations Management, and Foundations of Senior Management, reflecting his expertise in both theoretical and applied aspects of management sciences. Shah's recent work emphasizes ethical dimensions of AI, human-technology interaction, and circular economy strategies. His articles critically examine moral dilemmas in autonomous systems, stakeholder engagement in technology firms, and cybersecurity governance. He has published in prestigious journals like Journal of Business Ethics , Technovation , and Journal of Cleaner Production , with a focus on bridging theoretical frameworks and real-world applications. His teaching portfolio includes courses taught consistently since 2020 across multiple academic levels, demonstrating his commitment to experiential learning and pedagogical innovation. Though not currently accepting graduate students, his research collaborations span interdisciplinary themes in technology ethics and stakeholder analysis.

Xiaolu ZhangView profile

Professor

• Digital Forensics
• Cybersecurity
• IoT Security
• +5 more

Xiaolu Zhang is currently a Professor in the Information Systems and Cybersecurity department at the University of Texas at San Antonio (UTSA), affiliated with the College of AI, Cyber and Computing. Her research focuses on digital forensics, cybersecurity, and emerging technologies like IoT and blockchain. Specializes in Digital Forensics, IoT Security, and Privacy in Virtual Environments Active in Mobile Application Forensics, Cloud Storage Forensics, and Blockchain Forensics Her recent publications highlight trends in Metaverse privacy challenges, advanced forensic techniques for mobile and cloud platforms, and automated knowledge-sharing systems for IoT investigations. She also contributes to digital forensic education through experiential learning frameworks.

Hakima CHAOUCHIView profile

Professor

• Internet of Things (IoT)
• Wireless Networks
• Mobile Computing
• +5 more

Professor Hakima CHAOUCHI is affiliated with Telecom SudParis, part of the NeSS group. Her primary research focuses on Internet of Things (IoT), wireless networks, RFID technology, and cyber-physical systems. She has contributed extensively to advancements in IoT infrastructure, localization techniques, and network security. Research interests include IoT architecture, RFID-enabled systems, distributed edge computing, and energy-efficient protocols. Her work spans topics like anomaly detection in IoT data, resilient cyber-physical systems, and resource allocation in 5G networks. Key contributions include the PrIoT project for IoT prototyping, studies on dynamic resource allocation in HetNets, and frameworks for mobile cloud computing privacy. She has authored multiple books and book chapters on IoT and wireless networks, including The Internet of Things: Connecting Objects to the Web . Her publications emphasize practical applications like industrial autonomous systems, vehicular networks, and edge AI integration. She collaborates actively with industry and academia, contributing to standards like MIH-IEEE 802.21 and security protocols for mobile networks.