Dr. Ahmad Azab is a Lecturer at the University of Sydney’s School of Computer Science, specializing in Networking, Cybersecurity, and Machine Learning. He holds a PhD and industrial certifications including CISSP, CCSP, and CCNA. His teaching spans networking, cybersecurity, ethical hacking, and machine learning applications. Research interests focus on network traffic classification, IoT integration in cognitive radio networks, malware analysis, and cybersecurity countermeasures. His work bridges academic research with industrial applications, emphasizing practical solutions for emerging threats. Publications highlight innovations in botnet detection, malware classification, and cybersecurity frameworks. Supervision of bachelor and master’s projects underscores his commitment to mentoring future technologists. Professional affiliations include IEEE and ISC².
Dr. Aryan Kaushik is an Associate Professor at Manchester Metropolitan University (Manchester Met), UK, since 2024, affiliated with the Department of Computing and Mathematics. He also serves as Chief Advisor at RakFort, Ireland, since 2025. Previously, he was an Assistant Professor (senior grade) at the University of Sussex, UK, from 2021-24, and held roles as Recruitment and Admissions Tutor and Academic Advisor there. His academic journey includes a Research Fellow position at University College London (2020-21), and a PhD in Communications Engineering from the University of Edinburgh (2019). He holds an MSc in Telecommunications from the Hong Kong University of Science and Technology (2015). Education: PhD in Communications Engineering, University of Edinburgh (2019) MSc in Telecommunications, Hong Kong University of Science and Technology (2015) Professional Roles: Chair of IEEE ComSoc ETI on Electromagnetic Signal and Information Theory (since 2024) Core Member of IEEE P1955 Standard on 6G-Empowering Robotics Editorial roles across multiple IEEE journals and conferences His research focuses on 5G/6G wireless communications , integrated sensing and communications , reconfigurable holographic surfaces , non-terrestrial networks , and AI-driven network optimization . He has led UKRI-funded projects on topics like AI-assisted ISAC and Net Zero 6G, and collaborates globally with institutions like IIIT-Delhi, University of Bologna, and Imperial College London. Dr. Kaushik has received awards including the Top Editor Award 2025 (IEEE IoT Magazine), Best Editor Awards 2023-2024 (IEEE Open Journal), and was shortlisted for teaching excellence awards at Sussex. He actively contributes to standardization, serves on over 14 IEEE conference committees, and has delivered 110+ keynote/tutorial talks worldwide. His leadership extends to roles like TPC Co-Chair at IEEE ICC 2025 and Chair of Special Interest Groups on AI-driven Non-Terrestrial Networks and Fluid Antenna Systems.
Dr. Michael Ham is an Associate Professor and Coordinator for Cyber Operations Initiatives at Dakota State University's Beacom College of Computer & Cyber Sciences. He leads the NSA-designated Center of Academic Excellence in Cyber Operations (CAE-CO) and oversees DSU's Cyber Operations bachelor's program and Ethical Hacking Certificate. His roles include teaching advanced cybersecurity courses, advising doctoral students, and coordinating cyber events like DakotaCon and GenCyber camps. Education: All degrees from Dakota State University — D.Sc. in Cybersecurity (202X), M.S. (20XX), and B.S. (20XX). Research focuses on cybersecurity education, penetration testing, malware analysis, and developing experiential learning tools. Key projects include open-source platforms for hardware reverse engineering and wireless security training using software-defined radios. He emphasizes cyber hygiene practices and offensive security principles in both teaching and industry consulting. Grants highlight his leadership in addressing cybersecurity workforce shortages, including NSF CyberCorps SFS programs (PI), GenCyber camps (Co-PI), and DoD scholarships. Recent publications address ransomware prevention, X.509 certificate validation, and pandemic-era ICT systems. Dr. Ham also serves as an independent security consultant, advising on infrastructure security, vulnerability remediation, and policy development for organizations. His work bridges academia and industry through practical, hands-on cybersecurity education and tool development.
Matthew Allen Bishop is a Professor in the Department of Computer Science at the University of California, Davis. His primary affiliation is with the College of Engineering. Bishop's research focuses on cybersecurity, including secure programming, insider threat detection, malware analysis, and cybersecurity education. He has contributed extensively to curricular guidelines (e.g., CSEC 2017) and frameworks for cyber defense. His work spans theoretical advancements (e.g., intrusion detection models) and applied systems (e.g., secure voting platforms). Notable research areas include: Cybersecurity Education: Developing curricula and pedagogical frameworks for secure coding and ethical practices. Insider Threat Mitigation: Declarative approaches and behavioral analysis for detecting and preventing attacks. Malware Mitigation: Techniques leveraging uncertainty principles and defensive programming. Election Security: Analyzing vulnerabilities and designing secure voting systems. Bishop has collaborated with institutions like the Department of Homeland Security (DHS) and National Security Agency (NSA) on critical infrastructure protection. His publications span conferences like IEEE Security & Privacy, HICSS, and NSPW, emphasizing real-world applications of cybersecurity principles.
Dr Ivan Petrunin is a Research Professor in Signal Processing for Autonomous Systems and a DARTeC Fellow at Cranfield University's School of Aerospace, Transport and Manufacturing. His work focuses on advancing sensor technologies, data fusion, and decision-making systems for Cyber-Physical Systems, with applications in aerospace, ground-based autonomous systems, and urban air mobility. Key areas include Position, Navigation and Timing (PNT), vehicle health management, and AI-driven fault detection. He leads research at facilities like the Muti-User Environment for Autonomous Vehicle Innovation (MUEAVI) and collaborates with industry partners like Airbus, Rolls-Royce, and Thales. Education: BSc and MSc in Design of Electronic Equipment from National Technical University of Ukraine (1996–1998), followed by a PhD in Signal Processing for Condition Monitoring from Cranfield University (2013). Prior to Cranfield, he was a Lecturer in Digital Signal Processing at NTU Ukraine (2001–2005). Research Interests: Autonomous Systems & Sensor Fusion Machine Learning in Navigation and Safety GNSS Integrity & Urban Air Mobility Condition Monitoring & Structural Health Multi-Agent Reinforcement Learning Publications: Over 100 journal/conference articles and book chapters, with recent works emphasizing hybrid sensor fusion, resilient navigation architectures, and AI-driven solutions for GNSS-denied environments. Notable contributions include multi-sensor fusion frameworks for UAVs and Bayesian filter innovations. Awards: FRIN Fellowship, SMAIAA Membership, IEEE and ION Fellowships, and FHEA recognition. His work is supported by ESA, Innovate UK, and EPSRC. Advising & Labs: Supervises PhD students in UAV navigation and machine learning. Leads Cranfield's facilities for autonomous systems experimentation and advanced timing node infrastructure.
Valerie Maier-Speredelozzi is an Associate Professor in the Department of Mechanical, Industrial and Systems Engineering at the University of Rhode Island. She holds a Ph.D. in Mechanical Engineering from the University of Michigan (2003), with prior degrees in Industrial and Operations Engineering (M.S.E., 2001) and Mechanical Engineering (B.M.E., 1998). Her research bridges lean manufacturing, healthcare systems, and transportation engineering. Ph.D., Mechanical Engineering, University of Michigan, 2003 M.S.E., Industrial and Operations Engineering, University of Michigan, 2001 M.S.E., Mechanical Engineering, University of Michigan, 2001 B.M.E., Mechanical Engineering, Georgia Institute of Technology, 1998 Her work focuses on: Lean Manufacturing and Quality Assurance Human Factors in Industrial Systems Sustainable Supply Chain and Facility Location Transportation Safety and Dynamic Messaging Recent publications highlight trends in: Naval Engineering Education (2024) Healthcare Continuous Improvement (2016) Remanufacturing in SMEs (2016) Dynamic Message Sign Efficacy (2008–2011) Grants from the Office of Naval Research (2020–2021) support collaborations with UConn on safety and ergonomics in naval workforce development. She mentors graduate students and contributes to systems engineering pedagogy through workshops and project-based curricula.
Lenka Zdeborová is an Associate Professor at EPFL, jointly affiliated with the School of Basic Sciences and School of Computer and Communication Sciences. She leads the Laboratory of Statistical Physics of Computational Systems, where her research bridges statistical physics, machine learning, and computational biology. Education: PhD in Physics, Université Paris-Cité (2012) MSc in Fundamental Physics, École Normale Supérieure (2009) BSc in Physics, École Normale Supérieure de Lyon (2007) Her work focuses on phase transitions in learning algorithms, high-dimensional statistics, and neural network theory. Current projects investigate fundamental limits of machine learning, dynamics of graph neural networks, and applications to biological systems. Recent publications explore attention mechanisms in transformers, neural network depth advantages, and Bayes-optimal learning. Methodological innovations include cavity methods for hypergraphs and analysis of high-dimensional inference problems. Supervises doctoral students researching statistical physics approaches to machine learning and optimization. Teaches graduate courses in data science and machine learning for physicists.
Marko Tanasković is a researcher at Singidunum University's Faculty of Informatics and Computing, specializing in control systems, robotics, and electrical engineering. He holds a PhD from ETH Zurich (2015) in Information Technology and Electrical Engineering, following degrees from University of Belgrade (BEng, 2009) and ETH Zurich (MEng, 2011). His research focuses on adaptive control systems, machine learning applications in engineering, and sensorless motor control. Key contributions include: Development of predictive algorithms for traffic systems and industrial automation Innovations in rotor orientation determination for PMSM motors Integration of AI in fraud detection and building climate control Recent work includes a 2024 study on wearable health monitoring devices and a 2022 paper on drone forensics. He has authored/co-authored over 15 peer-reviewed articles and holds patents in motor control technologies. Current affiliations include Singidunum University's Department of Electrical Engineering and Collaboration with ETH Zurich alumni networks. Active in international conferences such as Sinteza and IEEE events.
Urs Hengartner is an Associate Professor at the Department of Computer Science, University of Waterloo. His research focuses on information privacy, computer and network security with emphasis on smartphones, IoT, and machine learning-based authentication systems. He holds a Ph.D. (2005) and M.Sc. (2003) from Carnegie Mellon University, and a Diploma from ETH Zürich (1997). His work spans Adaptive security attacks on ML systems Implicit user authentication frameworks Privacy-preserving technologies for location and genomic data Secure authentication systems resilient to voice/spoofing attacks Recent publication trends show a strong focus on adversarial attack detection (e.g., watermarking evasion, diffusion model attacks) and context-aware authentication systems . His frameworks like MRAAC and SHRIMPS address multi-stage authentication challenges in mobile ecosystems. Key contributions include frameworks for evaluating multi-user authentication systems (SHRIMPS), risk-aware access control (MRAAC), and novel defense strategies against collaborative robot traffic fingerprinting. His work bridges security mechanisms with user-centric design principles.
Robert Soulé is an Associate Professor in the Departments of Computer Science and Electrical Engineering at Yale University, and holds an Adjunct Professor position at the Università della Svizzera italiana (USI) in Lugano, Switzerland. His research focuses on distributed systems, networking, and applied programming languages, with notable contributions to in-network computing, consensus protocols, and energy-efficient systems. He received his B.A. from Brown University and his Ph.D. from New York University, followed by postdoctoral work at Cornell University. Education: B.A. in Computer Science, Brown University, 1999 Ph.D. in Computer Science, New York University, 2012 Research Interests: Dr. Soulé’s work spans distributed systems, networking, and programming languages, emphasizing practical systems such as in-network computing, consensus algorithms (e.g., NetPaxos), and carbon-aware networking. His research bridges theory and practice, addressing challenges in scalability, performance, and sustainability. Articles Trends: Recent work includes innovations in quantum networks (algebraic specifications), carbon-aware networking (energy efficiency), and system optimization (e.g., P4-based data plane verification). He explores network programmability, microservices acceleration, and zero-copy serialization techniques. Awards: Best Paper Awards at ACM DEBS 2012, NSDI 2018, and CoNEXT 2020 Google Faculty Research Award IBM Invention Plateau Award Advising and Grants: He has advised numerous PhD students and postdocs, including Pietro Bressana (Intel Corporation) and Theo Jepsen (Stanford Postdoc). His grants support projects in networked systems, distributed computing, and sustainable infrastructure. Labs/Teams: Active in Yale’s Systems Research group, collaborating on projects like NetChain (sub-RTT coordination) and P4-based systems (e.g., P4xos for consensus). Engages with industry through partnerships on microservices optimization and energy-efficient networking.
Bryce Kanago is an Associate Professor of Economics at the University of Northern Iowa (UNI). He has been with the Economics Department since 2000, starting as an Assistant Professor and advancing to his current rank by 2005. His research focuses on macroeconomics, economic history, and econometrics, with notable work on supply shocks, sports economics, and historical financial dynamics. Research interests include the analysis of real GDP and price deflators, the historical impact of major events on economic systems, and the application of econometric models to understand economic behavior. His work often explores the intersection of economic theory with empirical data, particularly in contexts such as sports economics, international finance, and the effects of inflation and uncertainty on economic outcomes. His recent publications include studies on the comovement of GDP and price deflators during the Great Moderation, the competition for leisure dollars in early 20th-century America through MLB's lens, and analysis of supply shocks in OECD countries. Earlier work delves into historical exchange rates during WWII and inflation uncertainty dynamics. No scientific awards or grants are explicitly mentioned in the provided texts. Advising details and lab affiliations remain unspecified.
Andreas J. Kassler is a Full Professor of Computer Science at Karlstad University, Sweden, where he has been since 2005. He co-chairs the Distributed Systems and Communication (DISCO) group and focuses on networking, cloud computing, and wireless networks. His research includes software-defined networking, future internet architectures, and network optimization. He has authored/co-authored over 130 peer-reviewed publications, holds 6 patents, and serves on editorial boards of journals like Journal of Internet Engineering . Education : Ph.D. in Computer Science, Universität Ulm (2002) Docent (Habilitation), Karlstad University (2007) M.Sc. in Mathematics/Computer Science, Universität Augsburg (1995) Research Interests : Software Defined Networking (SDN) Programmable Dataplanes Wireless Mesh Networks Time-Sensitive Networking (TSN) Edge Computing Machine Learning for Network Optimization Recent Directions : His work spans TSN scheduling, hybrid P4 solutions for 5G, and explainable AI in energy communities. He explores network resilience, latency optimization, and multi-objective control in microgrids. Service Contributions : Track co-chair for VTC 2015 General chair for Wired/Wireless Internet Communications (WWIC) 2013 Editor-in-Chief of IARIA Journal on Advances in Internet Technology Labs/Teams : Leads DISCO group at Karlstad University. Collaborates with global teams on projects like mmWave backhaul networks and SDN-enabled industrial control systems.
Tove Hels is an Associate Professor in the Department of the Built Environment at the Faculty of Engineering and Science, Aalborg University, Denmark. She is a key member of the Traffic Research Group, focusing on transportation safety, road user behavior, and traffic policy. Her work bridges engineering, public health, and social science to improve road safety outcomes. Research Interests: Her research spans traffic safety, cyclist-motorist interactions, speed enforcement, accident risk modeling, and the impact of vehicle technologies. She investigates both infrastructure design and behavioral factors influencing road safety, with a strong emphasis on data-driven analysis and policy evaluation. The recent publications reveal a consistent focus on improving cyclist safety through infrastructure (e.g., advanced stop boxes, roundabout design), addressing under-reporting of traffic injuries, and evaluating behavioral interventions for speeding. Her work integrates epidemiological methods, statistical modeling, and real-world policy applications. Scientific Contributions: Principal Investigator in the Danmarks Trafikulykker cohort study (2025–2029) Project participant in EASE: Intervention against speed offenders (2019–2025) Author of over 29 research outputs including journal articles, reports, and policy briefs Frequent contributor to national media and advisory bodies like Dansk Vejforening Advising and Grants: While no formal students are listed, her leadership in major funded research projects indicates a supervisory role in training junior researchers. She has secured and contributed to significant research grants related to traffic safety monitoring and intervention evaluation. Labs and Teams: She is part of the Traffic Research Group at Aalborg University, collaborating closely with researchers such as H. Lahrmann, T.K.O. Madsen, and A.V. Olesen. The group conducts field studies, data linkage projects, and policy evaluations with national impact.
Mohamed Sarwat is an Associate Professor at Arizona State University specializing in databases , spatial data management , and recommender systems . His research focuses on GeoSpark —a cluster computing framework for spatial data—and its extensions like GeoSparkViz for visualization and GeoSparkSim for traffic simulation. Key Contributions: LARS* (Location-Aware Recommender System), Horton* (Graph Reachability), Sindbad (GeoSocial Platform), and Riso-Tree (Graph Database Indexing) Research Themes: Integration of spatial/temporal data with machine learning, efficient indexing for big geospatial datasets, and scalable frameworks for mobility data science His work spans collaborations with 23+ co-authors across institutions like University of Minnesota, University of Melbourne, and University of Salzburg. Current projects emphasize GeoTorchAI —a spatiotemporal deep learning system—and mobility data science infrastructure.
Professor Andy Tolmie is Chair of Psychology and Human Development at University College London's Institute of Education (IOE). He leads the Centre for Educational Neuroscience, a tri-institutional partnership with Birkbeck and UCL's Institute of Cognitive Neuroscience. His roles include Deputy Director of this Centre and Programme Leader for the Educational Neuroscience MSc. He also oversees the Bloomsbury and UCL ESRC Doctoral Training Centres, instrumental in social science doctoral training across multiple universities. His research focuses on cognitive development, particularly in science learning, executive function, and child pedestrian safety. Key projects include the EEF/Wellcome Trust initiative on executive function in STEM education and interventions to improve group work efficacy in primary classrooms. He has developed behavioral training programs for child pedestrians adopted nationwide. Dr. Tolmie's academic leadership spans editorship of the British Journal of Educational Psychology and roles in the British Psychological Society. His work bridges developmental psychology with educational practice, emphasizing evidence-based strategies for teaching science and fostering critical thinking skills.
