Matteo Nardello is a researcher affiliated with the Department of Industrial Engineering at the University of Trento. His work focuses on embedded systems, IoT, and energy harvesting technologies for sustainable applications. Current academic affiliation: Department of Industrial Engineering, University of Trento Research interests: IoT, embedded systems, energy harvesting, machine learning, cyber-physical systems Contact: matteo.nardello@unitn.it His research integrates hardware-software co-design for batteryless IoT systems, with applications in smart agriculture, industrial monitoring, and autonomous vehicles. Recent work explores deep learning at the edge, energy-efficient sensor networks, and microbial fuel cells for self-powered devices. Key article trends highlight a focus on sustainable power solutions, wireless sensor networks, and machine learning optimization for constrained environments. He contributes to courses on embedded systems, IoT, and AI-powered industrial applications at the University of Trento.
Nebojša Bačanin Džakula is an academic affiliated with Singidunum University's Faculty of Mathematics, specializing in Computer Science. He earned his PhD in 2015 with a thesis on improving swarm intelligence metaheuristics for global optimization. His research focuses on AI-driven solutions for cybersecurity, energy forecasting, and optimization algorithms. He has authored/co-authored books on cloud computing and web programming. His work bridges metaheuristics with machine learning, addressing challenges in IoT security, renewable energy prediction, and healthcare diagnostics. He actively contributes to conferences like Sinteza and IEEE events, emphasizing practical applications of AI and optimization in real-world scenarios. Education: Completed doctoral studies at the Faculty of Mathematics (2009–2015). Extensive industry certifications include Microsoft, CompTIA, and Oracle credentials, enhancing his technical expertise. Research Interests: Develops hybrid models combining metaheuristics (e.g., PSO, GA) with deep learning for tasks like intrusion detection, price forecasting, and medical diagnostics. Specializes in optimizing neural networks and feature selection using advanced algorithms. His work often addresses societal challenges in sustainability, cybersecurity, and healthcare. Recent Publications: Focus on AI-driven solutions for IoT security, renewable energy prediction, and medical diagnostics (e.g., Parkinson’s detection via LSTM networks). His articles appear in prestigious journals like Engineering Applications of Artificial Intelligence and Applied Soft Computing.
Russell Tessier is a Professor and Department Head of Electrical and Computer Engineering at the University of Massachusetts Amherst, affiliated with the Manning College of Information and Computer Sciences. His research focuses on reconfigurable computing, FPGA architectures, and hardware security, with notable contributions in CAD algorithms for FPGAs, embedded systems, and multi-tenant FPGA vulnerability analysis. Education: B.S.C.S.E., Rensselaer Polytechnic Institute (1989) M.S. and Ph.D., Massachusetts Institute of Technology (1992 and 1999) Research Interests: Dr. Tessier's work spans FPGA security (e.g., side-channel attacks, power distribution vulnerabilities), reconfigurable cloud computing, and hardware acceleration for applications like SAR imaging and machine learning. His lab, the Reconfigurable Computing Group, develops open-source FPGA cores (e.g., FlexGrip GPGPU, DE4 NetFPGA) and explores cutting-edge security countermeasures. Awards and Honors: Chancellor's Leadership Fellow (2015-2016) NSF Information Technology Research Grant Lilly Teaching Fellow (2002-2003) Multiple College of Engineering Excellence Awards Grants and Projects: Active funding includes NSF SaTC grants on reconfigurable cloud security and NASA support for snowpack measurement systems. His research also addresses FPGA-based solutions for cybersecurity, such as intrusion detection and power-side channel mitigation. Labs and Teams: Leads the UMass Reconfigurable Computing Group, which collaborates on open-source FPGA tools, security frameworks, and embedded system designs. The group maintains platforms like the DE4 NetFPGA and FlexGrip architecture.
Jelena Mirkovic serves as Principal Scientist at USC Information Sciences Institute (USC/ISI) and Research Associate Professor at the University of Southern California's Thomas Lord Department of Computer Science. She has held faculty positions at USC since 2010, progressing from Research Assistant Professor to her current role as Research Associate Professor since 2017, while also serving as Project Leader at USC/ISI. Her educational background includes: PhD in Computer Science from UCLA (2003) MS in Computer Science from UCLA (2000) B.Sc. in Computer Science from University of Belgrade, Serbia (1998) Mirkovic's research spans network security, human-centered attacks, and cybersecurity experimentation infrastructure. Her work focuses on critical security challenges including botnets, denial-of-service attacks, IP spoofing, vulnerability scanning, and user-centric privacy. She has pioneered methodologies for security experiments and led major infrastructure projects including the DETER testbed and SPHERE (Security and Privacy Heterogeneous Environment for Reproducible Experimentation). Analysis of her recent publications reveals consistent innovation across multiple security domains. Her work demonstrates strong technical depth in DDoS defense systems (particularly DNS protection), binary vulnerability analysis, privacy-preserving systems, and security experimentation infrastructure. A notable trend is her focus on bridging theoretical security concepts with practical implementation through large-scale testbeds and real-world data analysis. Her significant scientific achievements include: IEEE Senior Member distinction Best paper award at IEEE COMSNETS 2023 for DNS DDoS defense research Mirkovic has secured substantial research funding as Principal Investigator or Co-PI on numerous grants from NSF, DHS, and other agencies. Current major projects include SPHERE (Security and Privacy Heterogeneous Environment for Reproducible Experimentation), DISCERN (Datasets to Illuminate Suspicious Computations), and modernizing DeterLab education infrastructure. She has successfully led multiple REU sites focused on cybersecurity education and workforce development. She directs the STEEL (Security Research Lab) at USC/ISI, which develops innovative security solutions through interdisciplinary research in network security, human factors in security, and cybersecurity experimentation infrastructure. The lab emphasizes practical implementations that address real-world security challenges while advancing theoretical understanding of security systems.
Habeeb Olufowobi is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA), where he leads the Cyber-Physical System Security Lab. He holds a PhD in Computer Science from Howard University (2019) and previously served as a Lecturer at Howard before joining UTA in 2020. His research is centered on the security and trustworthiness of embedded and distributed systems, particularly in the domains of autonomous vehicles, IoT, and healthcare AI. He investigates cybersecurity challenges at the hardware-software interface in real-time systems and develops AI/ML models that are transparent, explainable, and equitable. His interdisciplinary work integrates principles from cybersecurity, real-time systems, and machine learning. The recent publications reflect a strong trend in securing cyber-physical systems using advanced AI techniques, with emphasis on intrusion detection, secure communication (e.g., named data networking), and robustness of autonomous systems. His work frequently appears in top-tier venues such as IEEE Transactions, VehicleSec, and ICMLA. Project Management Professional (PMP), PMI (2012–Present) Member, Institute of Electrical and Electronics Engineers (IEEE) (2020–Present) Habeeb has secured significant research funding, including an NIH grant on ethical AI for Chagas disease prediction and an AIM-AHEAD grant focused on health equity. He mentors several graduate students, including Paul Agbaje and Afia Anjum, who have received awards and internships at prestigious institutions like Los Alamos National Laboratory. He teaches courses in cloud computing, embedded systems, and information security, and serves as a faculty advisor for the National Society of Black Engineers (NSBE) at UTA. His lab, the Cyber-Physical System Security Lab, focuses on developing scalable and reliable security solutions for critical infrastructure, with growing emphasis on healthcare applications and fairness in algorithmic decision-making.
Charles-Henry Bertrand Van Ouytsel is a Research Assistant and Visiting Lecturer at Université catholique de Louvain , affiliated with the Louvain Polytechnic School (EPL) and the Computer Engineering Center (INGI) . His work focuses on malware analysis , symbolic execution , and machine learning for cybersecurity applications. Research Areas : Packing detection, intrusion detection systems, side-channel security, and adversarial machine learning. Teaching : Involved in courses like Secured systems engineering (LINFO2144) and Software engineering and programming systems seminar (LINFO2359) . His recent publications emphasize malware obfuscation techniques and security evaluation frameworks . Collaborations with Axel Legay and others highlight his contributions to tool development (e.g., Packing-Box , SEMA ). No scientific awards are explicitly mentioned.
Shahid Raza is a Professor of Cybersecurity at the University of Glasgow's School of Computing Science. He previously led the RISE Cybersecurity Unit in Sweden, establishing it as a leading research group. His expertise spans IoT Security, PKI, AI-driven cybersecurity solutions, and hardware/data security. Raza holds a PhD and Docentship from Uppsala University, alongside a Bachelor's with a Gold Medal for academic excellence. Education: B.Sc. (Computer Science, 3.99/4.0 CGPA, Gold Medal), Licentiate, PhD, and Docentship in Cybersecurity from Sweden. He leads EU-funded projects like H2020 CONCORDIA and Horizon Europe CUSTODES, coordinating initiatives such as the Cyber Node and Cyber Range. Active in cybersecurity policy, he serves on the EU SCCG, ECSO, and EARTO Security & Defence Research working groups. Research Interests Public Key Infrastructure (PKI) for IoT AIAgent-Driven Cybersecurity Solutions IoT Certification Standards Hardware Security for Low-Power Devices Grants & Projects Coordinator: Horizon Europe CUSTODES Technical Leader: H2020 Arcadian-IoT Founder: RISE Cyber Range (Sweden's largest cybersecurity test facility) Awards & Memberships IEEE Senior Member Gold Medal for Academic Excellence (Bachelor's)
Associate Professor Mahsa Baktashmotlagh is an ARC Future Fellow at the School of Electrical Engineering and Computer Science, University of Queensland. Her research focuses on machine learning techniques applied to visual data analysis, biomedical data (e.g., antibacterial activity prediction), and cybersecurity. She holds a PhD from the University of Queensland (2014) and has contributed to over 50 peer-reviewed publications. Her research interests include domain adaptation, deep learning, and robust generalization across domains. Notable contributions include the development of DI-NIDS (a domain-invariant network intrusion detection system) and advancements in open-set domain adaptation. Her work bridges theoretical machine learning with practical applications in healthcare and computer vision. Education: PhD in Machine Learning, The University of Queensland (2014) Awards: ARC Future Fellowship (202X) Research Themes: Domain Adaptation, Cybersecurity, Biomedical AI Her recent work explores challenges in cross-domain generalization, adversarial machine learning, and scalable 3D object detection. She is actively involved in supervising graduate students and collaborates on interdisciplinary projects involving robotics and medical imaging.
Dr. Nour Moustafa is an Associate Professor and ARC DECRA Fellow at the School of Systems & Computing (SysCom) , University of New South Wales (UNSW) Canberra , Australia. He leads the Intelligent Security Group and focuses on developing AI/ML-driven cybersecurity frameworks for smart systems. Educated at Helwan University (BSc/MSc in Information Systems) and UNSW (PhD in Cybersecurity). Research Interests include intrusion detection, threat intelligence, privacy preservation, digital forensics, and cyber resilience, with methodologies spanning statistical analysis , machine learning , and deep learning applied to IoT , Edge/Cloud , and Industrial IoT environments. His work emphasizes federated learning for privacy preservation, blockchain for secure AI, and digital twins for network self-healing. Notable contributions include the TON-IoT , Bot-IoT , and UNSW-NB15 datasets for cybersecurity evaluation. Scientific Awards : 2020 Spitfire Memorial Defence Fellowship ACM Distinguished Speaker IEEE Senior Member He has served as guest associate editor for IEEE Transactions journals and held leadership roles in conferences like IEEE TrustCom . His research bridges academia and industry, with over 75 publications in top-tier venues.
Ikjot Saini is a Professor at the University of Windsor’s Faculty of Engineering, co-leading the SHIELD Automotive Cybersecurity Centre of Excellence, Canada’s first organization addressing threats in connected transportation. Her research focuses on automotive cybersecurity, vehicular networks, and privacy-preserving technologies. She has supervised doctoral students Shiva Nejati and Kunj Dhonde, and contributed to courses in the University’s Continuing Education program, specializing in cybersecurity education for professionals. Her work includes pioneering studies on blockchain-based security for connected autonomous vehicles (CAVs), machine learning-driven intrusion detection systems, and privacy-enhancing mechanisms like pseudonym-changing strategies. She has been recognized with the K.W. Michael Siu Award from the APMA Institute for Automotive Cybersecurity (2020). Saini’s research bridges theoretical advancements with real-world applications, ensuring vehicles and infrastructure remain secure against evolving cyber threats. Her contributions span academic publications, industry partnerships, and policy recommendations, positioning her as a leader in vehicular cybersecurity. Ongoing projects emphasize eco-efficiency in cybersecurity solutions and adversarial modeling for privacy evaluation.
Anthony D. Joseph is a Chancellor's Professor in the Department of Computer Science at the University of California, Berkeley, within the College of Engineering. He is a faculty member in the Computer Science Division and part of the RISE Lab and AMP Lab at UC Berkeley. His research spans multiple domains in computer science with a focus on security, distributed systems, and networking. Education: 1998, Ph.D., Computer Science, MIT 1988, S.M./S.B., Electrical Engineering and Computer Science/Computer Science and Engineering, MIT Professor Joseph's primary research interests include Computer and Network Security, Distributed Systems, Mobile Computing, Wireless Networking, Software Engineering, Operating Systems, Genomics, Secure Machine Learning, and Datacenters. His work has significant implications for both theoretical computer science and practical applications in industry. He leads multiple research projects including Mesos, SecML (Secure Machine Learning), D-Trigger, DETER, and Tapestry/Brochure. His research has been instrumental in advancing the fields of distributed systems and security, particularly in the context of machine learning applications. His publications reflect a strong focus on the intersection of security and distributed systems, with recent work emphasizing secure machine learning techniques and resource management in data centers. Professor Joseph's research has evolved from foundational work in networking and distributed systems to addressing contemporary challenges in cloud computing and AI security. Scientific Awards: Diane S. McEntyre Award for Excellence in Teaching Computer Science (2007) NSF Faculty Early Career Development Award (CAREER) (2000) Okawa Research Grant (1999) Professor Joseph has advised numerous graduate and undergraduate students, many of whom have gone on to make significant contributions in academia and industry. His research has been supported by various grants, including the NSF CAREER award. He has been actively involved in teaching core computer science courses including CS162: Operating Systems and Systems Programming and CS262: Advanced Topics in Computer Systems. He leads several research groups including the AMP Lab (which focuses on data analytics) and has been instrumental in projects like Mesos (for resource sharing in data centers) and SecML (focusing on the security of machine learning systems). His labs work on cutting-edge problems at the intersection of systems, networking, and security, with applications ranging from cloud computing to critical infrastructure protection.
Dr Fabio Pierazzi is an Associate Professor in Information Security at the Department of Computer Science, University College London. His research focuses on enhancing systems security through AI, particularly in environments where attackers rapidly adapt to defenses. He investigates adversarial attacks, concept drift mitigation, and explainability of ML-based security systems. Research emphasizes adversarial machine learning in security contexts Works on practical applications in malware analysis and network intrusion detection Explores concept drift robustness and problem-space constraints Collaborates with industry to improve real-world security solutions His publications span top-tier venues like IEEE Security & Privacy, ACM CCS, and USENIX Security. Key themes include adversarial robustness, security evaluation methodologies, and AI's limitations in practice. He supervises research degrees and provides consultancy for security projects.
Dr. Ken Ferens is an Assistant Professor in the Department of Electrical and Computer Engineering at the Price Faculty of Engineering, University of Manitoba. He serves as the Computer Engineering Champion in the Centre for Engineering Professional Practice and Engineering Education and directs the Applied Cognitive Intelligence (ACI) Research Group. Dr. Ferens is a senior member of the Institute of Electrical & Electronics Engineers (IEEE), Chair of the EduManCom Chapter of the IEEE, Vice-Chair of the Computer and Computational Intelligence Chapter of the IEEE, and Chair of the Industry, Teaching Assistants, and Student Forums for Engineering Curriculum Review and Improvement. Ph.D. (Computer Engineering), University of Manitoba, 1996 M.Sc. (Computer Engineering), University of Manitoba, 1991 B.Sc. (Electrical Engineering), University of Manitoba, 1989 Dr. Ferens has over 33 years of research experience in computational intelligence, focusing on cognitive machine learning, artificial intelligence, cognitive computational intelligence, chaos theory applications, agent-based models, and various optimization algorithms including simulated annealing, genetic algorithms, artificial neural networks, and particle swarm optimization. His research applies these techniques to develop software and hardware intrusion detection systems for cybersecurity applications. He teaches graduate-level courses on Computer Network Security and Applied Computational Intelligence, providing students with theoretical background and hands-on experience in state-of-the-art security methods. Analysis of Dr. Ferens' recent publications reveals a strong focus on applying cognitive and chaotic computational techniques to cybersecurity challenges, particularly malware detection and network intrusion detection. His work increasingly integrates complexity theory, fractal analysis, and hybrid optimization approaches to enhance security systems' effectiveness. There's a clear progression toward more sophisticated machine learning architectures applied to increasingly complex security scenarios, with growing emphasis on real-world IoT and network security applications. Best Paper Award at IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC 2022) Best Paper Award at IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC 2015) Best Journal Paper Award for 2013 (Journal of ICT Research and Applications) Best Poster Award at 12th International Conference on e-Health Networking, Application & Services (2010) Best Paper Award at IASTED International Conference on Computer, Electronics, Control, and Communication (1991) Dr. Ferens collaborates with national and international industry partners including the Department of Advanced Information Management, Content Technology Canadian Tire Corporation (CTC), and Magellan Aerospace. His research group has received funding supporting the Cyber-security Research Program, developing practical applications of computational intelligence for security systems. He has supervised numerous graduate students in the Electrical and Computer Engineering department, focusing on research at the intersection of machine learning and cybersecurity. Dr. Ferens leads the Applied Cognitive Intelligence (ACI) Research Group within the Department of Electrical and Computer Engineering, which focuses on applying cognitive, chaotic, and computationally intelligent algorithms to build intrusion detection systems. The group collaborates with industry partners to develop practical security solutions while providing students with hands-on research experience in cutting-edge security technologies. Their work spans both theoretical algorithm development and practical hardware implementation for real-world security applications.
Dr. Yongchao Huang is a Lecturer (Assistant Professor) in the School of Natural and Computing Sciences at the University of Aberdeen, where he has been employed since August 2023. He also holds affiliations with the University of Oxford and the University of Cambridge through past postdoctoral and collaborative roles. He is actively involved in research, teaching, and academic service, and is currently accepting PhD students. His educational background includes: DPhil in Engineering Science, University of Oxford (2013–2017) Additional training in Machine Learning at Oxford (2015–2019) Dr. Huang's research focuses on fundamental and physics-informed machine learning, with core interests in Bayesian inference, variational methods, generative modeling (especially score-based), reinforcement learning, and interdisciplinary AI applications in mechanics, biology, energy, climate, and finance. A central theme of his work is the inference and sampling of probability densities, particularly through innovative particle-based and physics-inspired computational frameworks. He founded the Computational and Physical Learning (CPL) lab at Aberdeen in 2023. His recent publications (2020–2025) reflect a strong trend in probabilistic machine learning, with increasing focus on physics-based inference methods such as electrostatics, fluid dynamics, and material point methods. These works bridge machine learning with applied mathematics and physical simulation, demonstrating a unique interdisciplinary approach. Topics span Bayesian neural networks, acoustic wave propagation, mortality modeling, and adversarial cybersecurity. Dr. Huang has received academic recognition through invitations to serve on program committees and editorial roles: Program Committee Member, ECAI 2024 Organizing Committee, Bioinference 2024 Guest Editor, Journal of Theoretical Biology Senior Scientific Advisor to a UK firm He has supervised 57 MSc theses independently and currently supervises one PhD student. He has secured research engagement through collaborations with institutions including Oxford, Cambridge, and industry partners. His teaching includes courses such as Introduction to Software Engineering , Software Process and Management , and Computational Intelligence at Aberdeen, as well as practicals in inference at Cambridge. Dr. Huang leads the Computational and Physical Learning (CPL) lab at the University of Aberdeen, a curiosity-driven research group focused on foundational advances in machine intelligence. Though currently a solo researcher due to limited resources, the lab emphasizes end-to-end research and open collaboration. He encourages student mobility and interdisciplinary exploration.
Professor Ioannis Katakis is a Faculty Member at the University of Nicosia, where he is affiliated with the School of Sciences and Engineering and the Department of Computer Science. He has held various academic positions across multiple institutions including Aristotle University of Thessaloniki, University of Cyprus, Cyprus University of Technology, Open University of Cyprus, Hellenic Open University, Athens University of Economics and Business, and National and Kapodistrian University of Athens. His educational background includes a PhD in Machine Learning for Automated Text Classification (2005-2009), a Master's in Information Systems (2005-2007), and a Bachelor's in Computer Science (2000-2004), all from Aristotle University of Thessaloniki. Professor Katakis specializes in several cutting-edge areas of computer science and data analysis. His primary research interests include Mining Social, Web and Urban Data , Sentiment Analysis and Opinion Mining , Data Streams , and Multi-label Learning . His work bridges theoretical machine learning approaches with practical applications in social media analysis, healthcare informatics, privacy protection, and smart city technologies. He has published extensively in top venues including CIKM, ECML/PKDD, IEEE TKDE, and ECAI. His recent publications demonstrate a clear trend toward applying machine learning techniques to real-world problems with societal impact. He has focused on areas such as GDPR compliance in smart devices, sentiment analysis in crowd-sourced content, healthcare applications including drug reaction classification and brain disease monitoring, and privacy protection in wearable technologies. His work often involves multi-modal data analysis and addresses challenges in data streams and multi-label classification. Professor Katakis has made significant contributions to his field, with his research cited over 4,200 times. He serves as an Editor for the journal Information Systems and has edited four special issues in journals such as DAMI and InfSys. He regularly contributes to the academic community by serving on program committees for major conferences including ECML/PKDD, WSDM, DEBS, and IJCAI, and by reviewing for prestigious journals like TPAMI, DMKD, TKDE, TKDD, JMLR, TWEB, and ML. He has been actively involved in European research projects, notably serving as Quality Assurance Coordinator and Senior Researcher for projects such as VAVEL (www.vavel-project.eu) and INSIGHT (www.insight-ict.eu). His grant activities demonstrate a strong focus on collaborative, interdisciplinary research with practical applications in urban data management, social media analysis, and healthcare informatics. He has organized three workshops at major conferences (ICML, ECML/PKDD, EDBT/ICDT) and has extensive experience translating research into practical applications through his involvement in European projects.
