Ioannis Tsaknakis is an Associate Professor at the Department of Electrical & Computer Engineering, School of Engineering, University of Peloponnese. He holds a PhD in computational geometry and multidimensional data structures from the University of Patras (2004) and has been actively involved in software systems research since 2004. His work spans Database Information Management , Big Data Systems , and Knowledge Mining , with a focus on data structures and computational geometry. Research Interests : Information Management in Databases Big Data Management Systems Computational Geometry Knowledge Mining in Databases/Web Publications highlight his contributions to IoT-driven educational frameworks, machine learning applications, and cryptographic systems for data security. He has taught courses on software design and data management since joining the University of Peloponnese in 2019. Contact : jtsaknakis@uop.gr . Office hours are in Building K (Monday & Tuesday, 8:00-9:00).
Patrick Lin is a Professor in the Philosophy Department at California Polytechnic State University (Cal Poly), where he serves as Director of the Ethics + Emerging Sciences Group, a non-partisan organization established at Cal Poly in 2007 to focus on the risk, ethical, and social impact of emerging sciences and technologies. He is frequently quoted in national publications on topics including ethics of autonomous vehicles, artificial intelligence, robotics, outer space, Arctic frontiers, military and policing applications, virtual and augmented reality, and smart cities. Lin received his Ph.D. and M.A. from the University of California, Santa Barbara, and his B.A. from the University of California, Berkeley. His academic appointments include Affiliate Scholar at Stanford Law School's Center for Internet and Society, Fulbright Specialist at the University of Iceland's Centre for Arctic Policy Studies (2018), and Visiting Senior Research Fellow at the Centre for Applied Philosophy and Public Ethics in Australia (2010-2016). Lin's research spans technology ethics broadly, with specific expertise in AI ethics, robotics ethics, autonomous vehicle ethics, space ethics, cybersecurity ethics, and military ethics. His work bridges philosophical theory with practical application, examining how emerging technologies challenge traditional ethical frameworks. His research demonstrates consistent themes across different technological domains: examining risk assessment methodologies, developing ethical frameworks for emerging technologies, analyzing social and political implications of technological adoption, and providing practical guidance for developers, policymakers, and users. His publication record shows a progression from early work on nanotechnology ethics to current focus areas including space cybersecurity, AI kitchens, and ethical frameworks for autonomous systems. The articles reflect his interdisciplinary approach, combining insights from philosophy, law, engineering, and policy studies to address complex ethical challenges in emerging technologies. Cal Poly/Academic Senate, Distinguished Scholarship Award (2017) American Philosophical Association's Public Philosophy Op-Ed Award (2015) Cal Poly/College of Liberal Arts, Outstanding Scholarship Award (2009) Lin has secured significant grant funding from organizations including the National Science Foundation, US Department of Defense, and Canadian Institute for Advanced Research for research on military AI risk assessment, AI kitchens and robot cooks, outer space cybersecurity, and autonomous vehicles. He has advised numerous students through his teaching and research activities at Cal Poly, where he teaches courses including Philosophy of Technology, Ethics of Science and Technology, and Introduction to Philosophy. Lin directs the Ethics + Emerging Sciences Group at Cal Poly, which serves as a hub for interdisciplinary research on technology ethics. He also participates in several other research initiatives including his role as Research Director for the Consortium for Emerging Technologies, Military Operations, and National Security (CETMONS) and as a member of the Emerging Technologies of National Security and Intelligence initiative at the University of Notre Dame.
Genya Ishigaki is an Assistant Professor in the Department of Computer Science at San José State University's College of Science. His research focuses on network slicing, combinatorial optimization, and reinforcement learning, addressing resource allocation challenges in next-generation telecommunications networks. Ph.D. in Computer Science, The University of Texas at Dallas, 2021 M.S. in Computer Science, The University of Texas at Dallas, 2021 M.S. in Engineering, Soka University, Japan, 2016 B.S. in Engineering, Soka University, Japan, 2014 Dr. Ishigaki's work explores adaptive network control through machine learning and combinatorial optimization, including elastic network slices , explainable AI , and federated learning . His research addresses critical tradeoffs in resource utilization versus capacity reservation for future demands. Recent publications demonstrate his focus on network automation (2025), information diffusion (2025), federated learning platforms (2024), and DDoS attack detection (2024). Articles span network security , AI-driven optimization , and social network dynamics . NSF Student Travel Grant (2019) Shigeta Education Foundation Ph.D. Scholarship (2019-2021) Outstanding TA Award (2019) JASSO Ph.D. Scholarship (2016-2019) NEC C&C Foundation Travel Grant (2015) He leads the Interconnect Lab, which investigates accountability in autonomous network operations and edge computing-oriented federated learning. His grants include SJSU's RSCA Seed Grant (2022-2023) and University Grant Academy Award (2022).
Professor Farookh Hussain is a distinguished academic at the School of Computer Science , University of Technology Sydney , specializing in Artificial Intelligence , Cloud Computing , and Software Engineering . His research spans diverse sectors including agriculture, manufacturing, healthcare, and transportation. Affiliated with the Australian Artificial Intelligence Institute (AAII) , he leads impactful work in business intelligence and carbon credit systems. Key research areas: AI applications, blockchain for provenance, carbon credit analytics Active in Masters/PhD supervision and cloud computing education Research Highlights : Developed KACINO framework for carbon dynamics modeling Created hybrid cybersecurity frameworks for supply chain risk management Advanced chatbot dialogue breakdown solutions through systematic reviews Proposed hypercomplex knowledge graph recommenders Published extensively on carbon credit price prediction and blockchain storage methods Contributions to water demand forecasting and collaborative robotics adoption Grant Activities : Secured funding from Hampton Capital Asset Management , Innovation Connections , and Science and Industry Endowment Fund Projects include LLM-driven text-to-SQL conversion , blockchain for melanoma data , and AI for storm water management
Erich Schweighofer serves as Associate Professor at the University of Vienna within the Institute for European, International and Comparative Law, specifically affiliated with the Department of International Law and International Relations. His research activities are centered at the Juridicum building (Schottenbastei 10-16, 1010 Vienna), where he maintains an active office presence with scheduled consultation hours. His scholarly focus spans Legal Informatics , Artificial Intelligence and Law , Data Protection , and Legal Knowledge Representation , with particular emphasis on explainable AI systems for legal contexts and formal methodologies for translating legal norms into computational frameworks. This interdisciplinary work bridges jurisprudence and computer science through projects examining biometric regulation, autonomous vehicle governance, and natural language processing applications in legal domains. Analysis of his 2021-2024 publications reveals consistent thematic progression toward operationalizing legal principles in AI systems, with increasing focus on transparency mechanisms, temporal logic for dynamic regulations, and cross-jurisdictional compliance challenges. His work predominantly appears in the International Legal Informatics Symposium (IRIS) proceedings and JURIX conferences, reflecting deep engagement with the legal informatics community. Professor Schweighofer leads a dedicated research team including project assistants Mag. Jessica Fleisch, Mag. Jonas Pfister, Felix Schmautzer, and Mag. Jakob Zanol, while actively participating in the University of Vienna's Working Group on Legal Informatics (Arbeitsgruppe Rechtsinformatik). His collaborative approach extends to organizing the biennial IRIS symposium, which has established itself as a cornerstone event for European legal informatics scholarship since 1998.
Andrew D. Ker is Professor and Associate Professor of Computer Science at the University of Oxford's Department of Computer Science, and Tutorial Fellow in Computer Science at University College, Oxford since 2009. He received his BA in Mathematics & Computer Science (1994-1997) and DPhil in Computer Science (1997-2000) from Oxford, followed by academic appointments including Junior Research Fellow (2000-2003), Special Supernumerary Fellow (2003-2009), and Royal Society University Research Fellow (2003-2011). His research focuses on information hiding, particularly steganography (covert communication in digital media) and steganalysis (detection of hidden data), with additional interests in digital media forensics and programming language semantics. His foundational work includes the mathematical formalization of the 'square root law of steganographic capacity'. Recent publications explore practical implementations in social media platforms, GPU-accelerated steganalysis, and linguistic steganography techniques. He has received multiple scientific awards including Best Paper Awards at ACM Workshops on Information Hiding & Multimedia Security, SPIE conferences, and the International Workshop on Digital Watermarking. As Associate Editor for IEEE Transactions on Information Forensics and Security, he maintains active involvement in the academic community. Professor Ker has supervised numerous graduate students in computer security and steganography research, including doctoral candidates and master's students. He leads research within Cyber Security Oxford and has developed four major lecture series: Lambda Calculus and Types, Discrete Mathematics, Computer Security, and Advanced Security: Information Hiding. He remains active in teaching despite administrative responsibilities as Tutorial Fellow.
Chris Reed is a Professor of Electronic Commerce Law at Queen Mary University of London's School of Law, affiliated with the Centre for Commercial Law Studies (CCLS). He holds a BA from Keele University and an LLM from the University of London. His research focuses on AI regulation, blockchain governance, cloud computing law, and cyber law. He has contributed to EU directives on electronic signatures and commerce and advised parliamentary committees. Notable roles include Academic Dean of the Faculty of Law & Social Sciences (2004–2009) and Director of CCLS. His work bridges legal theory and digital innovation, addressing challenges in cross-border regulation, accountability in AI, and data governance. **Research Interests:** Artificial intelligence liability, blockchain applications in sustainability, cloud computing law, cross-border cyber regulation, and electronic commerce frameworks. His interdisciplinary approach addresses legal gaps in emerging technologies, emphasizing ethical and policy dimensions. **Professional Contributions:** Advised UK government on Hague Conference and OECD/G8 initiatives, participated in EU digital signature hearings, and contributed to international conferences. His publications span over decades, focusing on cyberspace jurisprudence, AI governance, and data trusts. He teaches postgraduate courses on e-commerce transactions and regulation. **Labs/Teams:** Active within CCLS, leading projects like the Cloud Legal Project’s Coursera specialization on cloud computing law. Collaborates globally on AI and blockchain governance.
Amin Mesmoudi serves as Associate Professor in Data Engineering at the University of Poitiers' IUT (Institut Universitaire de Technologie), with dual laboratory affiliations at LIAS-ENSIP (Poitiers campus) and LIAS-ISAE-ENSMA (Chasseneuil campus). His research bridges theoretical database systems with practical large-scale data engineering challenges, particularly in semantic web technologies and machine learning applications. The laboratory maintains physical presences at both ENSIP's Bâtiment B25 in Poitiers and ISAE-ENSMA's Téléport 2 facility in Chasseneuil, facilitating cross-institutional collaboration. Mesmoudi's research program centers on scalable data management systems, with three interconnected pillars: (1) RDF and graph-based query optimization techniques for billion-triple datasets, (2) machine learning integration for spatial query performance and anomaly detection, and (3) explainability frameworks for complex black-box models. His work demonstrates consistent evolution from foundational database systems (2011-2016) toward contemporary AI-driven data engineering, particularly evident in his 2023-2025 publications on temporal dependency preservation and co-selection explainability. The Data Engineering team within LIAS laboratory provides the primary research context for these investigations. Publication analysis reveals strong methodological continuity in addressing scalability bottlenecks across database paradigms. Early work focused on SQL-on-MapReduce benchmarking for astronomy databases (2015-2016), transitioning to specialized RDF processing frameworks (2019-2021), and culminating in current hybrid approaches combining temporal modeling with machine learning (2023-2025). Key technical themes include fragmentation strategies for distributed data, optimizer feedback mechanisms, and graph-based query acceleration - all targeting real-world performance constraints in big data environments. As a core member of LIAS laboratory's Data Engineering team, Mesmoudi contributes to France's national research infrastructure in computer science and automation systems. The laboratory's dual-university structure enables unique cross-pollination between University of Poitiers' academic programs and ISAE-ENSMA's engineering specialization, with Mesmoudi's work exemplifying this synergy through applications spanning astronomy databases to wireless sensor networks.
Jürgen Cito is an Associate Professor with tenure at Vienna University of Technology (TU Wien), specializing in software engineering, explainable AI, and performance engineering. He leads research at the IPA Lab (as indicated by his personal website) and maintains a visiting researcher position at Google. His academic journey began with joining TU Wien as an Assistant Professor in Spring 2020, with promotion to Associate Professor announced in April 2024. His research interests span multiple critical areas of modern software development, with particular focus on developer experience, program comprehension, and the intersection of AI with software engineering practices. His work bridges theoretical foundations with practical industrial applications, as evidenced by collaborations with major technology companies. Analysis of his recent publications reveals a strong emphasis on practical tools and methodologies that enhance software quality, performance, and security. His research trajectory shows increasing focus on explainable AI techniques applied to software engineering problems, performance prediction from source code, and automated security testing approaches that leverage large language models. best teaching award for distance learning for Web Engineering (2020) Cito actively contributes to the software engineering community through numerous conference committee roles, including program committee positions at ASE, ICSE, ESEC/FSE, and other major venues. His lab appears to focus on developer tools, program analysis, and AI-assisted software engineering, with connections to both academic and industrial research environments.
Vir V. Phoha is a distinguished Professor in the Department of Electrical Engineering and Computer Science at Syracuse University's College of Engineering and Computer Science. He holds multiple prestigious fellowships including AAAS, AAIA, IEEE, NAI, and SDPS, and was named an ACM Distinguished Scientist in 2008. Dr. Phoha's research spans across cybersecurity, machine learning, and biometrics. His work focuses on cutting across conventional disciplines to unify basic and common concepts, particularly in security (malignant systems, active authentication), machine learning (decision trees, statistical, and evolutionary methods), and computer networks (anomalies, optimization). He develops field-realizable defensive and offensive cyber-based systems using these methodologies. His recent publications reveal a strong focus on continuous authentication, biometric security, fake news detection, and adversarial challenges in cybersecurity. The research shows an evolution from traditional network security to more specialized areas like wearable device security, keystroke dynamics, and gait authentication. Scientific Awards: Fellow of AAAS, AAIA, IEEE, NAI, SDPS ACM Distinguished Scientist (2008) IEEE Computer Society Distinguished Visitor (2024-2026) ACM Distinguished Speaker (2012-2015) IEEE Region 1 Technological Innovation Award (2017) "Highest Impact Award" IEEE CVPR 2018 Workshop on Biometrics Dr. Phoha serves as an associate editor for the ACM journal, ACM Digital Threats: Research and Practice (DTRAP) , and as an associate editor of IEEE Transactions on Computational Social Systems (TCSS) . He has advised numerous students who have gone on to publish significant research in cybersecurity and biometrics. His work has been supported by grants from DARPA and NSF, including the development of the BB-MAS dataset which became one of IEEE DataPort's most popular datasets.
Dr. Dima Alhadidi is an Associate Professor in the School of Computer Science at the University of Windsor. His research focuses on Cybersecurity, Data Privacy, Machine Learning, and their applications in Health Informatics, Cloud Computing, and Smart Grids. He holds a PhD in Computer Science and Software Engineering from Concordia University (2010). His research interests include secure federated learning frameworks, privacy-preserving techniques for genomic and health data, and adversarial machine learning defenses. Notable contributions include Trustformer (2025), secure aggregation methods in federated learning, and hybrid malware classification using deep learning. Recent work emphasizes mitigating membership inference attacks and developing privacy-preserving analytics for distributed systems. Dr. Alhadidi actively advises graduate students on topics like social network clustering (NICASN 2022) and federated learning security. No scientific awards are explicitly listed. His research spans theoretical frameworks (e.g., λ_AOP calculus) to applied systems in smart grids and healthcare informatics.
Dr. Kenneth Kent is a Professor in the Department of Computer Science at the University of New Brunswick (UNB), where he has served for 14 years. He is the Director of the Information Technology Centre (ITC) and heads the Reconfigurable Computing Group. He also serves as Director of the IBM Centre for Advanced Studies - Atlantic and holds an Honorary Professorship at Hochschule Bonn-Rhein-Sieg. His research focuses on hardware/software co-design, reconfigurable computing, virtual machines, and embedded systems. Dr. Kent earned his PhD and Master of Science in Computer Science from the University of Victoria. His work has led to over 100 refereed publications and the supervision of 70+ graduate students. He co-founded WEnTech Solutions Inc., a software firm addressing waste-to-energy optimization. His awards include the IBM Faculty Fellow of the Year and Project of the Year (as Principal Investigator) for contributions to the J9 Java Virtual Machine. His articles span FPGA acceleration, compiler optimization, cloud storage security, and IoT intrusion detection. Recent work emphasizes energy-efficient Node.js systems and advancements in CAD tools like VTR 9 for FPGA architecture. Dr. Kent’s advising and grants include leading the IBM CAS Atlantic and directing industry-academia collaborations. He has pioneered technologies such as the Eclipse OpenJ9 JVM and the CephArmor storage interface, balancing academic research with commercial innovation. He leads the Reconfigurable Computing Group at UNB and collaborates with the Institute for Visual Computing in Germany. His research bridges theoretical computing and practical applications, with a focus on scalable systems and embedded technologies.
Roberto Rojas-Cessa is a Professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology (NJIT), affiliated with the School of Applied Engineering and Technology. His research focuses on networking, blockchain applications in smart cities, energy systems, wireless communications, and high-performance switching. He has led multiple National Science Foundation (NSF)-funded projects, including initiatives on controlled delivery power grids and next-generation network quality of service. Notably, his work explores blockchain for energy metering, sustainable environmental measures, and smart grid optimization. He is also a Senior Member of the National Academy of Inventors (2024). His research interests span network protocols, distributed systems, and IoT applications. Recent projects include AMI-Chain (a blockchain-based power metering system) and studies on indirect free-space optical communications for vehicular networks. He has contributed to advancements in medium access control for crowded networks and energy packet switches for digital microgrids. Rojas-Cessa’s work integrates machine learning for network management and flood impact analysis. He has developed tools for time-lapse analysis of urban data and agent-based models to evaluate electric vehicle adoption. His publications emphasize scalability, security, and efficiency in both traditional and emerging technologies. Grants: Collaborative Research on Power Grids (NSF, 2016–2018), NeTS-NR: Quality of Service Networks (NSF, 2004–2008) Awards: Senior Member of the National Academy of Inventors (2024) His lab activities include experimental evaluations of digital microgrids and blockchain implementations for carbon footprint tracking. He actively collaborates on projects addressing emergency communications and resilient energy distribution systems.
Ming Li is a Professor of Electrical and Computer Engineering at Duke Kunshan University's Division of Natural and Applied Science, and a Principal Research Scientist at the Digital Innovation Research Center. He holds an adjunct position as a Professor at Wuhan University's School of Computer Science. His research focuses on audio/speech processing, multimodal behavior signal analysis, and applications in autism spectrum disorder diagnosis. Li has over 200 publications and serves on editorial boards of journals like IEEE Transactions on Audio, Speech and Language Processing. Education: Ph.D. in Electrical Engineering from the University of Southern California (2013). Awards include the IBM Faculty Award (2016), ISCA 5-Year Best Paper Award (2018), and Youth Achievement Award (2020). He leads initiatives in anti-spoofing countermeasures, voice conversion, and speech synthesis. Recent Courses: Random Signals and Noise Speech Recognition Data Science Key Research Contributions: Development of datasets like KunquDB, TMCSpeech, and systems for speaker verification, deepfake detection, and autism diagnosis tools. His work bridges signal processing with clinical applications, leveraging AI for social interaction improvement in neurodiverse populations.
Kshirasagar Naik is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, Ontario. He is actively involved in graduate research supervision and has been a member of IEEE since 1994. His academic career spans decades, with a focus on wireless communication, energy efficiency, and cybersecurity. 1992, Doctorate in Computer Engineering from Concordia University, Ontario 1988, Master of Mathematics in Computer Science from University of Waterloo, Ontario 1983, MTech in Computer Engineering from Indian Institute of Technology, Kharagpur, India 1981, BScEng in Electronics and Telecommunication from Sambalpur University, India His research interests include Mobile and Ad Hoc Networks , Cybersecurity , Internet of Things (IoT) , and Intelligent Transportation Systems . He has published extensively on energy optimization in wireless devices, delay-tolerant networks, and security protocols for vehicular systems. Recent publications highlight the integration of Machine Learning and IoT in environmental monitoring, particularly forest fire detection and prediction. Other works focus on cybersecurity , vehicular networks , and energy optimization in data centers and handheld devices. Professor Naik is currently accepting graduate students for research in mobile systems, network protocols, and green computing at the University of Waterloo.
