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Luis Duffaut EspinosaView profile

Assistant Professor

• Control Theory
• Estimation Theory
• Robotics
• +7 more

Summary Luis A. Duffaut Espinosa is an Assistant Professor in the Department of Electrical and Biomedical Engineering at the University of Vermont (UVM), affiliated with the College of Engineering and Mathematical Sciences. His research focuses on control theory, estimation, robotics, and nonlinear systems with applications in autonomy, quantum control, and environmental monitoring. He holds a Ph.D. in Electrical and Computer Engineering from Old Dominion University (2009) and has held academic positions at George Mason University and postdoctoral roles at Johns Hopkins University and the University of New South Wales. Education: Ph.D. in Electrical and Computer Engineering (2009), Old Dominion University M.S. in Mathematics (2005), Pontificia Universidad Católica del Perú B.S. in Physics (2003), Universidad Nacional de Ingeniería, Peru Research Interests: His work emphasizes data-driven control and estimation methodologies, including model-free approaches for power systems, environmental monitoring, and quantum control. Current projects include real-time data assimilation in harsh environments, resilient robotics in GPS-denied conditions, and SAR with small satellites. He co-directs the Autonomous and Intelligent Systems Research Laboratory (AIRLab) and is part of the CREATE center. Recognition: 2024 NSF CAREER Award for work on safety-aware data-driven control frameworks Teaching & Advising: He teaches courses in estimation theory, control systems, and signal processing. Advises a team of graduate and undergraduate students focusing on autonomy, robotics, and control systems. Notable students include Danial Waleed (Ph.D. 2024), Jacob Friz-Trillo (M.S. 2025), and Farnaz Boudaghi (Ph.D. candidate). Labs & Collaborations: AIRLab: Focuses on data-driven control for autonomy in robotics and engineered systems CREATE: Research on resilient energy and autonomous technologies

• Distributed Computing
• Applied Economics
• Policy and Administration Law
• +2 more

Naranker Dulay is a Professor in the Department of Computing at Imperial College London, part of the Faculty of Engineering. He holds affiliations with the Centre for Cryptocurrency Research and Engineering, Centre for Smart Connected Futures, and the Distributed Software Engineering group. His research focuses on Distributed Computing, Applied Economics, Policy and Administration Law, Computer Software, and Information Systems. His work explores blockchain technologies, smart contracts, consensus algorithms, and distributed systems. Recent research includes optimizing post-trade processing using distributed ledgers and developing adaptive protocols for dispute resolution in smart contracts. He is also involved in cybersecurity and privacy-preserving technologies for data management. Key contributions include frameworks like Chainlog for logic-based smart contracts and FADE for self-destructing data. His articles span over a decade, emphasizing blockchain scalability, energy-efficient neural networks, and decentralized macro-programming in wireless sensor networks. Dr. Dulay collaborates across interdisciplinary domains, blending technical innovation with socio-technical challenges. His affiliations reflect a commitment to advancing smart connected futures through cutting-edge research.

Geoffrey NelissenView profile

Assistant Professor

• Real-Time Systems
• Schedulability Analysis
• Multicore Platforms
• +3 more

Geoffrey Nelissen is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology. He holds additional roles as a Research Scientist and Investigador Auxiliar at INESC TEC (Portugal), contributing to interdisciplinary research in real-time systems. His work focuses on schedulability analysis, parallel task execution, and real-time communication protocols, with applications in embedded systems and multicore architectures. Research Interests: Real-Time Scheduling Response Time Analysis Multi-core and Parallel Systems Time-Sensitive Networking (TSN) Formal Verification Recent work emphasizes schedule abstraction frameworks, memory contention analysis, and deterministic communication protocols. His research has received recognition through multiple best paper awards including ICESS 2021 and RTAS 2022. He actively contributes to conferences like RTSS and ECRTS while teaching courses on Real-Time Systems and Operating Systems. Collaborations span European institutions with focus on embedded systems, autonomous driving, and safety-critical applications. Current projects explore holistic approaches to WCRT analysis and resilient real-time communication architectures.

• Cybersecurity
• Malware Detection
• Machine Learning
• +7 more

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.

Gurjot SinghView profile

Research Fellow

• Cybersecurity
• Aviation Network Security
• Secure Communications
• +3 more

Gurjot Singh is a Research Fellow at the Department of Computer and Information Science (IDA) at Linköping University, Sweden. His research focuses on cybersecurity in aviation systems, next-generation communication networks, and industrial IoT security. He collaborates with prominent researchers like Andrei Gurtov and Suleman Khan, contributing to projects such as SEC-AIRSPACE and Post Quantum Secure Handover Mechanisms. His work spans vulnerability assessments, secure protocol design, and post-quantum cryptography applications. Education details are not explicitly stated, but his research aligns with advanced cybersecurity and network engineering domains. Key research interests include aviation cyber risk assessment, secure data link communications, and privacy-preserving authentication mechanisms for IoT and industrial environments. Publications emphasize cybersecurity challenges in aviation, industrial networks, and IoT, with recent work addressing quantum-resistant protocols and drone remote identification security. Collaborations include contributions to conferences like NordSec and AIAA DATC. He is affiliated with the cybersecurity lab at LiU, supporting educational programs like Digital4Business, which integrates AI and cybersecurity expertise. His role in the Database and Information Techniques (ADIT) division highlights involvement in interdisciplinary research groups.

• Offshore Renewable Energy
• Marine Engineering
• Coastal Structural Design
• +3 more

Dr. Rameeza Moideen is a Researcher at the University of Edinburgh's School of Engineering, affiliated with the Energy Systems Research Institute. Her work focuses on offshore renewable energy infrastructure, coastal structural resilience, and fluid-structure interaction dynamics. Research Interests Her research spans vortex-induced vibrations in marine power cables, extreme wave impacts on coastal decks, and climate change adaptation for port infrastructure. She applies advanced numerical simulations to analyze hydrodynamic forces, structural stresses, and material degradation mechanisms. Key Research Trends Recent work emphasizes lazy wave dynamic cables under varying currents (2025), focused wave impacts on bridge decks (2023-2021), and marine growth effects on tubular structures (2021). These studies combine computational modeling with real-world climate scenarios to improve offshore energy systems and coastal infrastructure durability. Awards & Grants No specific awards or grants mentioned in available texts. Research is likely funded through institutional and collaborative projects within the Energy Systems Institute. Labs & Teams Active within the Energy Systems Research Institute at Edinburgh, collaborating on offshore renewable energy projects and coastal engineering initiatives.

Kaiqun FuView profile

Assistant Professor

• Spatial Data Mining
• Machine Learning
• Deep Learning
• +3 more

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.

Munther A. DahlehView profile

Professor

• Robust Control Theory
• Networked Systems
• Systemic Risk
• +5 more

Munther A. Dahleh is the William A. Coolidge Professor in the Department of Electrical Engineering and Computer Science at MIT and Director of the Institute for Data, Systems, and Society (IDSS). He is also affiliated with MIT’s Laboratory for Information and Decision Systems (LIDS). Previously, he served as Associate Department Head of EECS and held visiting roles at Caltech. His research focuses on foundational theory for networked systems, systemic risk detection, social network dynamics, and transportation systems modeling. Education: BS in Electrical Engineering from Texas A&M University (undergraduate), PhD in Electrical and Computer Engineering from Rice University. Research interests span robust control theory, information-propagation dynamics, distributed decision-making, and the interplay between physical and information networks. He has advised on systemic risk mitigation strategies for interconnected systems and contributed to energy grid resilience frameworks. Labs/Teams: IDSS, LIDS, and cross-disciplinary MIT initiatives. His work bridges engineering, economics, and policy to address complex societal challenges.

Anand NatarajanView profile

Assistant Professor

• Quantum Computing
• Theoretical Computer Science
• Complexity Theory
• +3 more

Anand Natarajan is an Assistant Professor at the Massachusetts Institute of Technology (MIT), affiliated with the Theory of Computation research group. His work focuses on quantum computing, complexity theory, and theoretical computer science, particularly exploring quantum verification, nonlocal games, and the intersection of quantum information with computational complexity. He is a key contributor to foundational results like the MIP*=RE theorem, which resolved longstanding questions in quantum complexity theory. Research Interests: Quantum Verification and Cryptography Quantum Complexity Classes (QMA, MIP*) Nonlocal Games and Tsirelson's Theorem Quantum Error-Correcting Codes Oracle Separations in Complexity Theory Recent Work Trends: His publications from 2020-2024 emphasize advancing our understanding of quantum advantage, noise resilience in quantum protocols, and bridging quantum computing with classical complexity theory. Notable contributions include resolving the quantum PCP conjecture via game-theoretic frameworks and analyzing the computational limits of interactive proof systems involving entangled provers. Lab/Team Affiliation: Core member of MIT's Theory of Computation group under Vinod Vaikuntanathan, collaborating on algorithms, security, and programming languages research.

Damon McCoyView profile

Professor

• Cybersecurity and Privacy
• Online Payment Systems
• Economics of Cybercrime
• +6 more

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.

• Social Network Analysis
• Network Science
• Data Science
• +7 more

Giacomo Fiumara is an Associate Professor at the University of Messina, Department of Mathematical and Computer Sciences, Physical Sciences and Earth Sciences. He holds academic rank since October 2021. Previously, he served as a Permanent Researcher (2008–2021) and secondary school teacher (1997–2008). He earned a Doctorate in Physics (1993) and a Degree in Physics (1989), both from the University of Messina. He is an associate member of the Accademia Peloritana dei Pericolanti and qualified as an associate professor in INF/01 and ING-INF/05 sectors. His research focuses on social network analysis, network science, data science, criminal networks, knowledge representation, bioinformatics, and computational modeling. He has supervised over 170 theses and advised PhD students in Mathematics and Computational Sciences. Key collaborations include work with Prof. Pasquale De Meo on criminal networks and complex systems, and international projects with institutions in the US, UK, China, and Australia. Teaching includes courses on Algorithms, Data Structures, Bioinformatics, and Machine Learning across Computer Science, Engineering, and Medical programs since 2000. He also contributed to international programs at Lviv Polytechnic, Birzeit University, Cluj-Napoca, and Murcia. His editorial roles include Associate Editor of IEEE Access and Academic Editor of Complexity. He holds a patent for predictive analysis of criminal organizations' social structures and has received FFABR research funding. Key awards include FFABR funding (2017) and recognition in the FFABR Unime 2020 II edition. He organized conferences like Crimenet 2014 and participated in high-profile events such as the 2022 Complex Networks conference in Palermo, presenting on quantum walks for criminal network analysis.

Weichao WangView profile

Professor

• Cybersecurity
• Wireless Networks Security
• Cloud Computing Security
• +7 more

Weichao Wang is a Professor and Chair of the Department of Software and Information Systems at the University of North Carolina at Charlotte (UNC Charlotte). He holds a Ph.D. in Computer Science from Purdue University (2005), with earlier degrees from Tsinghua University. His research focuses on securing pervasive systems, wireless networks, cloud computing, and critical infrastructures, integrating multi-disciplinary approaches like information theory and visualization. He leads efforts in cybersecurity education for K-12 and higher education. Key roles include organizing IEEE conferences (e.g., IPCCC) and editorial roles in journals like ITU Intelligent and Converged Networks. His awards include the 2024 AECT Crystal Award and multiple Distinguished TPC recognitions. Students under his advisement have contributed to areas like mobile cloud computing and network security. Professional contributions include service on over 50 conference committees and journal reviewing. Current research emphasizes cybersecurity in education, IoT defense, and AI-driven security systems. His lab explores immersive visualization for cybersecurity training and augmented reality applications.

Dan M. IonelView profile

Professor

• Alternative and Renewable Energy Technologies
• Electric Machines and Power Electronic Drives
• Electromagnetic Devices
• +3 more

Dan M. Ionel, Professor and inaugural L. Stanley Pigman Chair in Power at the University of Kentucky (UK), directs the Power and Energy Institute Kentucky (PEIK). He is affiliated with the SPARK Laboratory in the Department of Electrical and Computer Engineering, focusing on renewable energy, electric machines, and smart grids. Alternative and Renewable Energy Technologies Electric Machines and Power Electronic Drives Electromagnetic Devices Electric Power Systems Energy Storage Smart Grids and Buildings His research integrates machine learning with electromagnetic design for applications like electric vehicles and aircraft propulsion. Recent work includes axial flux permanent magnet machines with Halbach arrays and cryogenic thermal management systems. Scientific awards include IEEE Fellowship. Research is sponsored by NSF, DOE, NASA, and industry partners like ANSYS, EPRI, and Regal Rexnord. Collaborations span national labs (NREL, ORNL), utilities (LG&E, TVA), and aerospace entities.

Young-Jin ChaView profile

Professor

• Deep Learning
• Structural Health Monitoring (SHM)
• Autonomous UAVs
• +5 more

Dr. Young-Jin Cha is a tenured full Professor in the Department of Civil Engineering at the University of Manitoba, affiliated with the Price Faculty of Engineering. He holds a PhD from Texas A&M University and has postdoctoral experience at MIT. His research focuses on deep learning-based structural health monitoring (SHM), autonomous UAVs for infrastructure inspection, and smart transportation systems, with over 100 peer-reviewed publications and $1.2M in grants. He is a Fellow of ASCE and has received notable awards including the 2021 Merit Award and 2022 International Association of Advanced Materials Scientist Award. His work has been cited over 9,200 times globally. Research interests include automated SHM with UAVs, nonlinear system identification, unsupervised deep learning for damage detection, and sustainable infrastructure design. He serves as an editor for journals like Structural Control & Health Monitoring and Engineering Reports . His lab, the Laboratory for Infrastructure Science and Technology (LIST), develops advanced technologies for infrastructure resilience. Key achievements include pioneering deep learning-based SHM with UAVs, top-cited papers in civil engineering journals, and leadership in organizing international conferences. He actively seeks graduate students for research in AI-driven infrastructure solutions.

Cristina Torres-MachiView profile

Assistant Professor

• Infrastructure Asset Management
• Sustainability and Resilience in Transportation
• Decision-Making and Optimization Systems
• +3 more

Cristina Torres-Machi is an Assistant Professor of Civil, Environmental and Architectural Engineering at the University of Colorado Boulder, holding the Beavers Professorship of Construction Engineering. She earned her PhD in Civil Engineering from Universitat Politècnica de Valencia and Universidad Católica de Chile (2015), a MSc in Planning and Management in Civil Engineering (2012) and BSc in Civil Engineering (2008) from Universitat Politècnica de Valencia. Her research focuses on infrastructure asset management, sustainability/resilience in transportation, and decision-making optimization systems. She leads the Innovation for Resilient Infrastructure (IRI) research group, advancing methodologies for long-term infrastructure management. Recognized with awards including the Abertis International Award (2016) and excellence in PhD dissertation (2015/2017), she serves as Chair of ASCE’s Construction Research Council and Vice-Chair of TRB’s Transportation Asset Management committee. Her work integrates machine learning, predictive analytics, and data science to improve pavement performance and lifecycle cost analyses. Professional affiliations include the American Society of Civil Engineers (ASCE) and active roles in TRB committees. Research emphasizes pavement rehabilitation strategies, flood impact modeling, and equitable infrastructure adoption trends. Over 60 publications (60+ citations) span journal articles, conference proceedings, and book chapters, with projects funded by public/private agencies. Current projects explore satellite-based monitoring, EV infrastructure impacts, and disaster-resilient traffic systems. Her recent work addresses procurement fallacies in design-build projects and integrates social vulnerability assessments in natural hazard planning.