Pierre Roduit is a Professor and Head of the Institut Energie et environnement at HES-SO Valais-Wallis. His expertise spans Energy Systems, Demand Side Management, and Machine Learning applications in sustainability. He leads research on smart energy solutions, including grid flexibility, non-intrusive load monitoring, and hydropower maintenance. Current projects include the EU-funded domOS (H2020) for smart building OS and the Innosuisse-backed Industrialisation of energy services for small buildings. His work integrates IoT and data science to enhance energy efficiency in residential and industrial contexts. Key Projects: GOFLEX (H2020), SEMIAH (FP7), Cavitation Monitoring System Focus Areas: Thermal Energy Storage, Predictive Maintenance, Smart Grids His research bridges academic innovation with industrial applications, advancing technologies such as NILM and acoustic-based turbine monitoring. Over 20 years, he has authored over 30 publications and secured €16M+ in EU and industry funding.
Asma Shakil is a researcher in computer science with a focus on software engineering, biometrics, and education technology. Her work spans empirical studies on code readability, pedagogical strategies for capstone courses, and biometric signature verification systems. She collaborates with scholars across institutions, contributing to conferences like ITiCSE, ACE, and CHI. 2025: Co-authored a paper on team-based capstone projects. 2024: Authored multiple studies on student engagement and capstone course design. 2019: Explored gaze-based code navigation in CHI proceedings. 2008-2010: Pioneered offline signature verification research with Hidden Markov Models. Research Interests: Asma's work bridges software engineering practices, biometric authentication, and educational innovation. Her recent papers emphasize scalable teaching methods and team-based learning in computing education, while her earlier contributions focus on statistical modeling for signature verification. Publication Trends: Her research has evolved from biometrics to education technology, with a recurring emphasis on empirical validation. Key subfields include functional decomposition, code navigation, and pedagogical interventions for student engagement. Collaborations: Regular co-authors include Paul Denny, Sara Hooshangi, and Ewan D. Tempero, indicating sustained partnerships in education technology research.
Dr. Philip G Zhao is a Senior Lecturer in Robotics and AI at the University of Manchester's Department of Computer Science. Prior to this role (since 2024), he held positions at the University of Glasgow from 2018 to 2023. He leads the Machine Intelligence Alliance across multiple universities and is a Senior Member of IEEE and IET member. His research focuses on AI-driven cross-system design and optimization for robotics, Cyber-Physical Systems (CPS), IoT, communication networks, and computer vision. His work emphasizes practical applications such as edge intelligence, teleoperation systems, and medical robotics. Dr. Zhao has secured £3M in total research funding, including £800k as Principal Investigator, and holds two U.S. patents. Notable achievements include three Best Paper Awards and over 2,800 Google Scholar citations. His recent projects involve edge-enabled industrial CPS co-design, metaverse teleoperation frameworks, and dynamic human-robot interaction studies. He actively supervises PhD students and promotes collaborative robotics in healthcare and education. Labs/Teams: Machine Intelligence Alliance (MIA), leading interdisciplinary research across multiple universities.
Daniele Antonioli is an Assistant Professor in Digital Security at EURECOM, France. His research focuses on securing Cyber-Physical Systems (CPS), Mobile/Wireless Systems, Embedded/IoT devices, and Industrial Control Systems (ICS). He specializes in applied cryptography and system-level security, addressing vulnerabilities in emerging technologies such as Bluetooth, FIDO2, and vehicular networks. Maintains a lab focused on reverse engineering, hardware-software co-analysis, and automated security testing frameworks. Developed tools like MiniCPS and CPSBot for CPS/IoT security research. Research interests include: - Bluetooth Exploitation : BIAS, KNOB, BLURtooth attacks. - Embedded Systems Security : E-Trojans, E-Spoofer, Breakmi. - Privacy-Preserving Systems : Decentralized contact tracing protocols. - IOT/IICS Threat Modeling : AttackDefense Framework. Publications highlight both technical contributions (e.g., SimProcess simulation framework) and practical impact (e.g., EmuOCPP for EV charging security). Recipient of Singaporean Presidential Graduate Fellowship (2015-2019) and ST Engineering Research Excellence Award (2017). Active in international collaborations, including EU-funded projects. Advises on CPSBot botnet simulation and FP-tracer browser fingerprinting detection systems. Engages in standardization efforts for secure industrial communication protocols.
Imran Naseem serves as an Adjunct Associate Professor in the School of Engineering at The University of Western Australia, specifically within the Department of Electrical, Electronic and Computer Engineering. His academic profile shows significant research contributions with 1,533 citations and an h-index of 13 according to Scopus metrics. His research expertise spans several interconnected domains: Machine Learning algorithms, particularly least mean square variants (76% research focus) Deep learning architectures for computer vision and biometric security Neural networks and radial basis function applications (41% research focus) Fast convergence techniques in signal processing (39% research focus) Biomedical applications including anticancer peptides classification Dr. Naseem's recent publication trends demonstrate strong focus on developing robust AI systems for security applications, particularly in biometric authentication systems that can detect presentation attacks. His work bridges theoretical signal processing with practical applications in computer vision and healthcare diagnostics. The research fingerprint shows significant activity in Mean Square Mathematics (100%) and Least-Mean-Square Algorithm development. His collaborative network includes multiple international co-authors across different research institutions, with recent publications appearing in IEEE Access, Applied Intelligence, and Frontiers in Physiology. The University of Western Australia profile indicates he has supervised at least one research project as noted by the 'Supervised Work (1)' designation in his academic profile.
Hany Farid is a Professor at the University of California, Berkeley with a joint appointment in the Department of Electrical Engineering & Computer Sciences (EECS) and the School of Information. He specializes in digital forensics, forensic science, misinformation analysis, image analysis, and human perception. His research focuses on detecting manipulated media (photos, videos, audio) and understanding societal impacts of AI-generated content. Education: PhD in Computer Science (University of Pennsylvania, 1997), Postdoc at MIT (1999). Affiliations: Co-founder and Chief Science Officer at GetReal Security. Grants: Supported by NSF, DARPA, Adobe, Meta, Oak Foundation, and others. His work bridges computational techniques with human perception, addressing challenges like deepfake detection and forensic science reliability. He has authored over 200 papers and two book-length studies on photo forensics and synthetic media. Research Highlights: Developed algorithms to detect photo/video manipulation via lighting/shadow inconsistencies. Investigated AI-generated voice and facial synthesis detection. Explored societal impacts of misinformation and predictive algorithms in criminal justice. Awards: Alfred P. Sloan Fellowship (2002) John Simon Guggenheim Fellowship (2006) Fellow of the National Academy of Inventors (2020) IEEE Fellow (2018) Advising: Mentored over 40 students, including PhD candidates in computer vision and forensic science. Current focus includes M.S./PhD students in digital forensics and AI ethics.
Hemant Singh Sengar is a researcher and co-author of several publications in the fields of computer science and engineering. His work spans network protocols for mobile ad-hoc networks, data security in communication systems, and deep learning applications in biometric systems and public health. He has collaborated with experts like Arpit Jain, Om Goel, and others across multiple institutions. Research interests include secure communication in non-ideal environments, cryptographic protocols for smart grids, and machine learning-driven approaches to data aggregation, intrusion detection, and medical imaging. His recent publications focus on optimizing network efficiency and enhancing security in emerging technologies. Key contributions include modeling MANET protocols (2025), lightweight authentication for smart grids (2024), and non-invasive blood group prediction via EfficientNet (2024). Earlier work addresses face mask detection (2023), biometric spoofing (2022), and data-driven product management strategies (2020).
Yousef Elmehdwi serves as Associate Chair and Associate Teaching Professor of Computer Science at the College of Computing, Illinois Institute of Technology, where he holds a primary appointment in the Department of Computer Science. Education: Ph.D. in Computer Science, Missouri University of Science and Technology M.S. in Information Technology, Mannheim University of Applied Sciences B.S. in Computer Science, University of Benghazi His research focuses on Cybersecurity with specialized expertise in privacy-preserving computation, secure cloud data architectures, and encrypted data analytics. He has pioneered lightweight identity management solutions for vehicular networks and developed novel cryptographic frameworks for biometric authentication systems, emphasizing infrastructure-minimized security protocols that maintain robust privacy guarantees in resource-constrained environments. Analysis of his 2012-2017 publications reveals a cohesive research trajectory centered on homomorphic encryption techniques for secure outsourced computation, with significant contributions to privacy-preserving query processing, encrypted k-NN classification, and VANET security architectures. His work consistently bridges theoretical cryptography with practical cloud deployment challenges, demonstrating particular innovation in minimizing computational overhead while maintaining semantic security. Scientific Awards: No awards documented in source materials. Advising and Grants: Graduate student mentorship details and external funding sources were not specified in available documentation, though his publication record indicates collaborative research activities. Labs and Teams: Affiliation with specific research laboratories or structured teams was not mentioned in the provided institutional profile.
Geethapriya Thamilarasu is an Associate Professor in the Computing and Software Systems department at the University of Washington Bothell , affiliated with the School of Science, Technology, Engineering & Mathematics. She previously served as Assistant Professor at the SUNY Institute of Technology and earned her Ph.D. in Computer Science & Engineering from State University of New York at Buffalo (2009), M.S. from SUNY Buffalo, and B.E. from BITS Pilani , India.
Sotirios Kentros is an Associate Professor in the Computer Science Department at Salem State University , focusing on Distributed Computing , Computer Security , Cryptography , and Computer Science Education . His work emphasizes formal mathematical analysis for system safety and security, particularly in cyber-physical systems and electronic voting. Ph.D. and M.Sc. in Computer Science and Engineering from University of Connecticut M.Sc. in Biomedical Engineering from National Technical University of Athens and University of Patras Dipl.-Ing. in Computer Science and Engineering from University of Patras His research bridges Distributed Systems with Security , including work on Oblivious RAM , At-Most-Once Semantics , and Location-Based Authentication . Publications span venues like IEEE Trans. Inf. Forensics Secur. , USENIX Security Symposium , and DISC . Teaching includes courses like Computer Networks , Operating Systems , and Computer Security .
Dr. Lakshmidevi Sreeramareddy is an Assistant Professor in the Computer Science Department at Salem State University, with extensive experience in human-computer interaction , usable security , and machine learning . She earned her D.Sc. in Information Technology (2014), M.S. in Computer Science (2008), and B.Sc. in Information Science and Engineering (2006) from Towson University and Visvesvaraya Technological University. Specialized in gesture-based authentication systems Focus on accessibility for cognitively diverse users Contributing to ABET accreditation efforts for IT programs Teaching expertise spans databases, system administration, and programming Her research combines usability testing with machine learning algorithms to enhance security protocols, particularly for vulnerable populations. Publications highlight collaborations with Dr. K. Kaur, Dr. J. Feng, and colleagues on topics like Finch Labs, shoulder-surfing vulnerabilities, and educational retention strategies. Key trends from her 10+ publications include: Authentication innovation for mobile/web systems Security evaluations through empirical user studies Applications of machine learning in behavioral pattern analysis Curriculum development for programming education ABET accreditation-compliant instructional design Accessibility-focused research for cognitively diverse users She has taught courses in software design, database administration, human-computer interaction, and IT system integration while serving on university committees and mentoring students.
Katerina Kanta is a Lecturer at the University of Portsmouth's School of Computing within the Faculty of Technology. She is actively affiliated with the Portsmouth AI and Data Science Centre and the Centre for Cybercrime and Economic Crime, contributing to interdisciplinary cybersecurity research. She holds a Ph.D. in Context-Based Password Cracking for Digital Investigation from University College Dublin, awarded on June 25, 2023. Her research spans password cracking, digital forensics, cybersecurity, authentication, and STEM education. She applies AI techniques to enhance forensic investigations and examines societal impacts through studies on password portrayal in media and mobile health security. Her work aligns with UN Sustainable Development Goals for security and quality education. Recent publications reveal a strong trend toward context-based password cracking using generative models, alongside investigations into cybersecurity frameworks for 6G networks and mobile health applications. This demonstrates technical innovation blended with practical societal applications across digital forensics and security domains. As a Co-Investigator on the active XTRUST-6G project (2025-2027), she contributes to developing security solutions for next-generation telecommunications. She has no listed doctoral advisees but participates in collaborative research teams through university centers. She actively engages with the Portsmouth AI and Data Science Centre and Centre for Cybercrime and Economic Crime, working within teams that address real-world security challenges through applied research and industry partnerships.
Aamir Akbar serves as an Assistant Professor in the Department of Computer Science & Information Systems at Abdul Wali Khan University Mardan (AWKUM), where he also co-directs both the AWKUM AI Lab and AWKUM Robotics initiatives while coordinating final year projects. His academic journey culminated in a PhD from Aston University, Birmingham, UK, with research focused on energy-efficient methods for hybrid mobile cloud computing. His educational background includes a PhD in Computer Science from Aston University (2015-2019, awarded February 2020), where his dissertation explored energy-efficient approaches for hybrid mobile cloud computing systems. Dr. Akbar's research spans multiple cutting-edge domains in computer science, with particular emphasis on Artificial Intelligence , Cloud and Fog Computing , Internet of Things , and Software Defined Networks . His multidisciplinary approach combines evolutionary computation, multi-objective optimization, and machine learning to develop resource-efficient intelligent systems. His work frequently addresses challenges in Cyber-Physical Systems, Mobile-Cloud Computing, and IoT/IoV applications, demonstrating strong practical relevance to real-world problems. Analysis of his recent publications (2021-2024) reveals a clear research trajectory toward increasingly sophisticated AI applications in networking and distributed systems. His work shows strong focus on reliability, energy efficiency, and security across multiple domains including medical IoT, smart cities, and industrial automation. The growing citation impact (612 total citations, h-index: 14) demonstrates increasing recognition of his contributions to these fields. Dr. Akbar maintains active presence in the research community with 19 total publications showing accelerating output in recent years (6 in 2021, 2 in 2022, 5 in 2023, and 2 in 2024). His Google Scholar profile reflects substantial impact with an h-index of 14 and i10-index of 16. As an educator and researcher, Dr. Akbar brings both academic rigor and industry experience to his role. His technical expertise spans the full stack of modern computing systems, from frontend development (HTML, CSS, Angular, React) through backend frameworks (Flask, Django, Node.js) to database systems (MySQL, PostgreSQL, MongoDB) and cloud infrastructure (AWS, Kubernetes, Docker). This comprehensive skill set enables him to bridge theoretical research with practical implementation. He leads research initiatives through the AWKUM AI Lab and AWKUM Robotics, providing students with opportunities to engage in cutting-edge projects that combine theoretical foundations with real-world applications. His GitHub activity shows ongoing engagement with AI and systems development projects, including repositories focused on neural networks, Python programming, and network simulation.
Christoph Sorge is a full Professor and Head of the Chair of Legal Informatics at Saarland University, where he leads interdisciplinary research at the intersection of law and computer science. His work focuses on data protection, IT security, and the legal implications of emerging technologies such as AI, blockchain, and wearables. He is actively involved in teaching courses like 'Recht der Cybersicherheit' and 'IT-Forensik für Juristen'. The Chair of Legal Informatics is engaged in numerous high-impact research projects funded by BMBF, DFG, and BMWK, including D'Accord , EDWI , K3I-Cycling , PAIRS , ScaleTrust , and research on botnet crime . These projects explore adaptive privacy systems, data spaces, AI governance, and cybersecurity compliance, often in collaboration with legal and technical partners. His research interests span data protection law , cybersecurity , AI and law , digital ecosystems , and applied cryptography . He has contributed to the legal classification of Bitcoin and digital signatures, and his team develops tools for transparent and compliant data processing. The recent publications reflect a strong trend toward interdisciplinary compliance solutions , combining legal analysis with technical design. Themes include GDPR-compliant privacy dashboards, secure data exchange in crises, ethical AI, and legal frameworks for data trusteeship. The work consistently emphasizes user rights , transparency , and technical feasibility in legal contexts. Notable scientific contributions include project-based research rather than individual awards, though the impact is evident in policy-relevant outputs and collaborations with public institutions like the Saarland State Chancellery. Prof. Sorge supervises research staff on topics such as algorithmic summaries of court rulings, legal issues of cryptography, wearable data protection, and cloud security. While formal PhD students are not listed, his team includes researchers like Aljoscha Dietrich and Bianca Steffes. The Chair also contributes to eGovernment education through the eGov Campus project, developing data protection training for administrative staff. Lab and team activities are centered around the Chair of Legal Informatics , which functions as a research hub for legal-technical innovation, hosting internal projects and participating in national and international collaborations.
Shambhu J. Upadhyaya is a Professor in the Department of Computer Science and Engineering at the University at Buffalo, SUNY . He also serves as Director of the SEAS/SOM Cybersecurity MS Program and the Center of Excellence in Information Systems Assurance Research and Education (CEISARE) , recognized by the NSA/DHS as a Center of Excellence. His research spans information assurance , computer security , behavioral biometrics , and fault-tolerant computing . Ph.D., Electrical & Computer Engineering, University of Newcastle, Australia (1987) M.E./B.E., Electrical Engineering, Indian Institute of Science, Bangalore (1982/1979) His research projects focus on insider threat assessment , continuous authentication , deception techniques , secure wireless communications , and game-theoretic security models . He has directed 300+ publications and mentored Ph.D./M.S. students like A. Sanzgiri , R. Mehresh , and M. Jadliwala , many of whom hold prominent roles in tech companies. Key scientific recognitions include: IEEE Fellow IEEE Region 1 Technological Innovation Award (2018) SUNY Chancellor's Award for Scholarship (2019) Best Poster at IEEE BTAS Conference (2016) IBM Faculty Partnership Fellowship (2000-01) He has taught courses like CSE566 Wireless Networks Security , CSE341 Computer Organization , and CSE552 VLSI Testing , while leading departmental initiatives such as ABET accreditation and internship coordination. His Electronic Test and Design Automation Lab was sponsored by IBM and supported research on VLSI testing.
