Irina Brass is a Professor of Science, Technology and Regulation at University College London’s Department of Science, Technology, Engineering and Public Policy (STEaPP). Her research focuses on anticipatory and adaptive regulatory frameworks for emerging technologies, particularly IoT, AI, and advanced biotherapeutics. She leads projects like the REG-MEDTECH initiative and collaborates with government agencies, standards bodies, and interdisciplinary teams. Current Role: Professor at UCL STEaPP Leadership: Chair of BSI’s IoT/1 Technical Committee (2017-2021), member of Standards Policy and Strategy Committee (2020–) Her research spans: Regulation of connected medical devices and cybersecurity Governance of AI and algorithmic systems Standardization challenges for IoT and biotherapeutics Adaptive policy frameworks for disruptive technologies She leads the MPA in Digital Technologies and Policy and teaches courses on digital technology dilemmas and risk governance. Her accolades include the BSI Standards-Makers Award (2019) and UCL Provost’s Education Award (2020) .
John E. Taylor is the Frederick Law Olmsted Professor and Associate Chair for Faculty Development and Research Innovation at the Georgia Institute of Technology's School of Civil and Environmental Engineering within the College of Engineering. His research focuses on the intersection of human and engineered networks, with particular emphasis on creating resilient infrastructure systems that serve society's needs while creating more livable communities. Taylor's research interests span multiple domains including Smart City Digital Twins , Urban Infrastructure Resilience , Network Dynamics , and Building-Occupant Interaction . His work examines how human behavior, infrastructure systems, and environmental factors interact during normal operations and extreme events. He has developed innovative approaches to understanding urban systems through the lens of network theory and computational modeling. His publication record demonstrates consistent contributions to the fields of urban analytics and infrastructure resilience, with a recent focus on digital twin technologies for urban systems. Taylor's work shows a clear trajectory toward increasingly sophisticated integration of AI, network science, and civil infrastructure engineering to address complex urban challenges. His research has particular relevance for cities facing climate change impacts and seeking to build more equitable and resilient communities. Taylor leads the Network Dynamics Lab at Georgia Tech, where he mentors PhD students and postdoctoral researchers. His lab has produced significant work on human-infrastructure interaction, particularly during disasters and extreme events. The lab's research combines computational modeling, data analytics, and field studies to understand and improve urban systems. His work has been applied to real-world challenges including river emergency response systems, urban heat exposure forecasting, and disaster response optimization. Taylor has collaborated with city officials and agencies to implement systems that have demonstrable community benefits, such as the AI-enabled camera system for drowning prevention on the Chattahoochee River and crime reduction systems using mobile cameras guided by AI algorithms.
Professor Lyudmila Mihaylova is a distinguished academic at the University of Sheffield's School of Electrical and Electronic Engineering, where she holds the position of Professor of Signal Processing and Control. She has established herself as a leading researcher in the fields of signal processing, Bayesian methods, and autonomous systems, with significant contributions to particle filtering techniques for intelligent transportation systems. Her work bridges theoretical developments with practical applications across multiple domains including transportation, healthcare, and industrial automation. Prof. Mihaylova's research interests center on nonlinear filtering, sequential Monte Carlo methods, statistical signal processing, and sensor data fusion. Her work spans both theoretical advancements and practical implementations, with particular focus on high-dimensional problems including vehicular traffic flow estimation, image processing, and localization in sensor networks. She has extensive experience with various image modalities such as optical, thermal, LIDAR, SAR, and hyperspectral imaging. Her group actively develops novel methods for autonomous intelligent systems focusing on sensing, tracking, decision making, and machine learning applications. Analysis of Prof. Mihaylova's recent publications reveals a strong trend toward uncertainty quantification in machine learning models, particularly for safety-critical applications. Her work increasingly integrates traditional signal processing techniques with modern deep learning approaches, with applications spanning sewer inspection robotics, medical diagnostics (particularly sleep apnea detection), UAV swarm tracking, industrial manufacturing, and autonomous vehicle systems. A significant portion of her recent research focuses on developing robust methods that can handle incomplete or outlier-corrupted data while providing reliable uncertainty estimates. Among her notable professional achievements: President of the International Society of Information Fusion (ISIF) Senior member of the IEEE Signal Processing Society Associate Editor for IEEE Transactions on Aerospace and Electronic Systems Associate Editor for Elsevier Signal Processing Journal Prof. Mihaylova has successfully mentored numerous PhD students and postdoctoral researchers, many of whom have gone on to prominent academic and industry positions. Her research has been supported by major funding bodies including EPSRC, EU, MOD/DSTL, and industry partners, with recent projects including 'Protecting Environments with UAV Swarms' (InnovateUK, 2022-2024), 'ShiRAS: Towards Safe and Reliable Autonomy in Sensor Driven Systems' (NSF-EPSRC, 2019-2023), and 'Confident safety integration for Cobots' (Lloyd's Register Foundation, 2019-2020). Her research group follows a collaborative approach with the philosophy 'We share knowledge, we grow.' Prof. Mihaylova maintains active research collaborations with institutions worldwide and has held previous academic positions at Lancaster University (2006-2013) and University of Bristol (2004-2006), along with research visiting positions at the University of Ghent, Katholic University of Leuven, and the Bulgarian Academy of Sciences.
Michael S. Rogers is a retired four-star U.S. Navy admiral and currently serves as an Adjunct Professor and Senior Fellow at Northwestern University's Kellogg School of Management Public Private Initiative, while also contributing as an expert to the McCrary Institute's cyber and critical infrastructure security initiatives. Bachelor's degree from Auburn University Master's degree in National Security Distinguished Graduate, National War College Graduate of Highest Distinction, Naval War College MIT Seminar XXI Fellow Harvard Senior Executive in National Security Alumnus His research focuses on practical applications at the intersection of cyber security, national security, and critical infrastructure protection, emphasizing cross-sector collaboration between government, military, and private industry. Rogers leverages his extensive operational experience to develop real-world solutions for global cyber threats and policy frameworks, with particular expertise in international cyber policy development and public-private partnerships. As a Senior Fellow and Adjunct Professor, he actively shapes the Public Private Initiative's research agenda, advising corporate leaders in finance, telecommunications, and technology sectors while engaging global academic and business audiences on emerging cyber challenges and strategic responses.
Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data
Charity Nyelele is an Assistant Professor in the Environmental Sciences department at the University of Virginia. Her research bridges human well-being and environmental systems, focusing on biodiversity, climate change, and ecosystem services through the lens of environmental justice and equity. Specializes in urban forestry and socio-ecological synthesis Active in climate justice, carbon sequestration, and stormwater management Nyelele's recent work integrates machine learning and social media data to map recreational ecosystem services and optimize tree planting frameworks. She has developed multi-objective decision support tools to address urban ecosystem service trade-offs and leads research in fire-driven ecosystem restoration across Western US forests. She teaches courses on Environmental and Climate Justice , Management of Forest Ecosystems , and co-instructs Politics, Science, and Values . Contact: hbt3mb@virginia.edu
Hayretdin Bahsi is an Assistant Professor at the School of Informatics, Computing, and Cyber Systems at Northern Arizona University . His research focuses on cybersecurity, with expertise in malware detection, IoT security, and machine learning applications in defense mechanisms. He collaborates internationally on maritime cybersecurity, healthcare systems, and critical infrastructure protection. Research Interests include Android malware analysis, botnet detection, explainable AI in intrusion detection, and threat modeling for AI-driven systems. His work addresses challenges like concept drift in malware detection and privacy-preserving techniques for IoT networks. Publications span 66 scholarly works since 2009, emphasizing cybersecurity trends in AI, IoT, and healthcare. Recent contributions explore large language model (LLM) applications in vulnerability detection and cyber threat modeling for healthcare systems. Collaborations include projects on maritime cyber-insurance, cyber incident management in low-income countries, and datasets like MedBIoT for IoT botnet analysis. His work bridges theory and practice, addressing real-world cybersecurity challenges.
Marco Serafini is an Assistant Professor in the Department of Computer Science at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Sciences (CICS). He leads the DREAM Lab (Data systems Research for Exploration, Analytics, and Modeling) and is part of the Center for Data Science. Prior to UMass, Serafini worked as a Senior Scientist at the Qatar Computing Research Institute (QCRI) and held a postdoctoral fellowship at Yahoo! Research in Barcelona. He earned his PhD in Computer Science from TU Darmstadt (Germany), where his thesis was recognized through nominations for best thesis awards across German, Swiss, and Austrian computer science societies. His research focuses on the intersection of database systems, distributed systems, and data science, emphasizing scalable architectures for big data analytics and machine learning. Key areas include computation pushdown in cloud DBMSs, graph neural network training systems, and efficient graph pattern matching. His work addresses challenges in tail latency mitigation, resource optimization, and transparent scaling of ML models. Serafini has contributed to influential systems like Arabesque (for distributed graph mining), E-Store (elastic partitioning), and Aion (event-time stream processing). He has been awarded an NSF CNS Core grant to advance scalable GNN training. His publications span top venues such as ACM SIGOPS, VLDB, and ICDE, reflecting his expertise in both theoretical foundations and practical system implementations. Professional recognition includes thesis nominations from major computer science societies and sustained contributions to open-source projects in distributed computing. Serafini advises students through the DREAM Lab, focusing on preparing the next generation of data systems researchers.
Farah Magrabi is a Professor of Biomedical and Health Informatics at Macquarie University's Australian Institute of Health Innovation. She holds a BE (Hons 1) from the University of Auckland and a PhD in Biomedical Engineering from the University of New South Wales. Her research focuses on the safety and effectiveness of digital health and AI technologies, with notable contributions to patient safety standards, including the ISO/TS 20405 specification. She has been recognized with awards such as the Sax Institute’s Research Action Award (2015) and the Telstra Health Brilliant Women in Digital Health Award (2021). Magrabi’s leadership roles include co-chairing the Australian AI Alliance’s Working Group on Safety, Quality, and Ethics, and the 2023 Medinfo World Congress. She advises the Australian Digital Health Agency and contributes to global initiatives like the OECD’s Global Partnership for AI (GPAI). Her work spans policy development, clinical decision support systems, and AI ethics. Key projects include the NHMRC Centre of Research Excellence in Digital Health’s Safety Research Stream and evaluations of AI in clinical settings. Her research outputs emphasize AI governance, healthcare resilience, and climate change adaptation. Notable collaborations include studies on pandemic responses, generative AI in pain management, and digital health usability. Magrabi’s interdisciplinary approach bridges engineering, informatics, and policy to advance safe and equitable healthcare technologies.
Paul Pearce is an Associate Professor in the School of Cybersecurity and Privacy at Georgia Institute of Technology. He specializes in network security and internet measurement, focusing on DNS manipulation, IPv6 scanning, and malware analysis. His research has led to significant contributions, including the NSF CAREER Award (2023) and the IMC Community Contribution Award (2022). He holds a PhD in Computer Science from UC Berkeley (2018), following MS and BS degrees from the same institution. Education: PhD in Computer Science, UC Berkeley (2018) MS in Computer Science, UC Berkeley BS in Computer Science, UC Berkeley California Community Colleges: Chaffey College and Mount San Antonio College Research Interests: Network security and measurement IPv6 infrastructure and scanning Malware analysis and threat detection Privacy risks in browser extensions Global DNS manipulation and censorship Grants & Awards: NSF NeTS Early-Career Investigator Workshop PI (2025) NSF CAREER Award (2023) DARPA Riser (2022) IMC Community Contribution Award (2022) Distinguished Practical Paper at IEEE S&P (2015) Teaching: Teaches CS8803-EMS: Advanced Network Security and Measurement at Georgia Tech.
Anthony Patt is a Full Professor of Climate Policy at ETH Zurich's Department of Environmental Systems Science and Deputy Head of the Institute for Environmental Decisions. He holds a PhD in Public Policy from Harvard University, along with a Juris Doctor from Duke University and a Bachelor's in Architecture and Landscape Design from Yale. His research focuses on identifying effective governmental approaches to eliminate greenhouse gas emissions and adapt to climate change impacts. Key areas include societal processes related to consumption patterns, technology use, and modeling interactions between people, institutions, and technological systems. Patt has authored over 100 peer-reviewed articles and serves as Coordinating Lead Author for the IPCC's Sixth Assessment Report on international cooperation. He has led major projects on renewable energy policies, climate adaptation, and energy security. Notable grants include funding from the European Union, Swiss National Science Foundation, and international organizations. Patt's work emphasizes the co-benefits of climate policies, such as financial impacts on cities and energy transition strategies. His career spans academic roles at institutions like the International Institute for Applied Systems Analysis (IIASA) and Boston University, alongside contributions to policy initiatives like the EU's 100% renewable electricity roadmap. Patt's expertise bridges environmental science, policy analysis, and stakeholder engagement, addressing global challenges through interdisciplinary approaches.
Andreas Malikopoulos is a Professor at Cornell University's School of Civil & Environmental Engineering and Director of the Information and Decision Science Lab (IDS Lab). Previously, he held roles as the Terri Connor Kelly and John Kelly Career Development Professor at the University of Delaware (UD) and founding Director of UD's Sociotechnical Systems Center. He also served as the Alvin M. Weinberg Fellow at Oak Ridge National Laboratory (ORNL), Deputy Director of ORNL's Urban Dynamics Institute, and Senior Researcher at General Motors R&D. His research focuses on cyber-physical systems (CPS), stochastic control, and learning-driven approaches for optimizing energy efficiency and sustainable mobility in smart cities and transportation systems. Education: PhD (Mechanical Engineering, University of Michigan, 2008), M.S. (Mechanical Engineering, University of Michigan, 2004), Diploma (National Technical University of Athens, 2000). Research Interests: Analysis and control of CPS, stochastic scheduling, game theory, and mechanism design applied to emerging mobility systems (e.g., autonomous vehicles, electric vehicles). He emphasizes integrating learning and control for socially optimal solutions in transportation networks. Awards: IEEE ITS Young Researcher Award (2019), UD’s Outstanding Junior Faculty Award (2020), Alvin M. Weinberg Fellowship (2010), and recognition as a NAS Kavli Frontiers of Science Scholar (2012). He is an IEEE Senior Member, ASME Fellow, and serves on editorial boards of leading journals. Teaching: Focuses on optimal decision-making, control theory, and emerging mobility systems. Courses include stochastic optimal control and game theory at Cornell. Labs: Leads the IDS Lab, which develops scalable frameworks for CPS and smart city applications. Current projects include coordinated routing for mixed-traffic systems and AI-driven recommendations for autonomous vehicles.
Kaidi Yang is an Assistant Professor at the National University of Singapore (NUS) in the Department of Civil and Environmental Engineering, specializing in Intelligent Transportation Systems and related fields. He holds a PhD from ETH Zurich (2019), an M.Sc. in Control Science and Engineering from Tsinghua University (2014), and dual bachelor’s degrees in Automation and Mathematics from Tsinghua University (2011). His research focuses on advancing traffic control, connected/automated vehicles, shared mobility systems, and data privacy in transportation. He has contributed to developing algorithms for efficient traffic signal control, platooning coordination, and privacy-preserving data sharing in transportation networks. Education: Ph.D., Civil and Environmental Engineering (Transportation), ETH Zurich, 2019 M.Sc., Control Science and Engineering, Tsinghua University, 2014 B.Sc./B.Eng., Dual Degrees in Pure/Applied Mathematics and Automation, Tsinghua University, 2011 Yang has received prestigious awards including the Swiss National Science Foundation’s Postdoc Mobility Fellowship (2021–2022) and the IEEE ITS Conference Best Student Paper Award (2020). He serves as an Associate Editor for the IEEE Conference on Intelligent Transportation Systems (2024). His work bridges theoretical advancements in operations research, robotics, and machine learning with practical applications in urban mobility systems. Recent efforts emphasize integrating privacy-preserving techniques into traffic management and optimizing mixed-autonomy platoon control.
Dr. Kaiqun Fu is an Assistant Professor in the McComish Department of Electrical Engineering and Computer Science at South Dakota State University (SDSU). He holds a Ph.D. and M.S. in Computer Science from Virginia Tech (2021 and 2016). His research focuses on spatial data mining, spatiotemporal event analysis, graph neural networks, and urban computing applications such as traffic impact prediction and social media-driven insights. He also explores physics-informed machine learning for power systems and interdisciplinary topics like 'deaths of despair' in rural areas. Education: Ph.D. in Computer Science, Virginia Tech, 2021 M.S. in Computer Science, Virginia Tech, 2016 Research Interests: His work emphasizes machine learning and deep learning applications in spatial-temporal domains, including: Graph neural networks for traffic incident prediction Social media analysis for urban challenges Physics-informed models for power grid stability Citation forecasting in scientific publications Grants & Projects: NSF CRII ($174,734): Spatiotemporal impacts of traffic events via graph neural networks (2024–2026) NSF EAGER ($300,000): Socio-economic impacts of emerging technologies (2024–2026) SDSU RSCA ($10,118): Graph transformer-based location learning (2023–2024) Professional Involvement: He chairs ACM SIGSPATIAL's SRC committee, serves on SDSU's Computer Science curriculum committees, and is an IEEE member. He co-edits Frontiers in Big Data and advises on interdisciplinary projects like climate-impacted grid security (NSF RII Track-2, $750,000). Labs/Teams: Collaborates with interdisciplinary groups focusing on smart cities, data-driven infrastructure resilience, and GeoAI applications.
Damon McCoy is a Professor of Computer Science and Engineering at NYU Tandon School of Engineering and Co-Director of the NYU Center for Cybersecurity (CCS). He holds a Ph.D. in Computer Science from the University of Colorado, Boulder (2009) and a B.S. in the same field (1999). His research focuses on empirically measuring the security and privacy of technology systems, with a current emphasis on online payment systems, cybercrime economics, automotive security, privacy-enhancing technologies, and censorship resistance. He co-directs Cybersecurity for Democracy , a multi-university initiative addressing online threats to democratic processes. His research has examined topics such as the monetization of YouTube conspiracy content, toxic online discourse targeting election officials, and vulnerabilities in automotive systems. He has collaborated with institutions like the International Computer Science Institute and led studies on social media platforms' content moderation practices, including Facebook's handling of political ads and YouTube's ecosystem of deceptive advertising. McCoy's work often bridges cybersecurity with societal impacts, analyzing how technology intersects with democracy, misinformation, and economic exploitation. His contributions include developing frameworks for securing automotive software updates (Uptane) and methodologies to combat cybercrime supply chains. He has received funding from the National Science Foundation and other agencies to pursue projects addressing emerging cyber threats. Key affiliations include the NYU Center for Cybersecurity and the Center for Automotive Embedded Systems Security (CAESS), where he contributed to early automotive security analysis. He advises on cybersecurity policy and has published extensively in top-tier journals and conferences, focusing on real-world applications of security research.
