Find Academic Supervisors

• Social Psychology
• Intergroup Relations
• Prejudice and Discrimination
• +9 more

Orkun Yetkili is a Senior Lecturer in Social Psychology within the Department of Psychology at the University of Westminster, Faculty of Science and Engineering. He joined the university in 2016 and serves as the module leader for the Level 4 Social Psychology course, contributing to both undergraduate and postgraduate teaching in areas such as prejudice, identity, and developmental psychology. His research lies at the intersection of group processes and intergroup relations. Key interests include developing interventions to reduce prejudice across diverse social settings—such as ethnic divisions in Cyprus, religious groups in the United States, and political affiliations in the UK. A second major strand examines the social psychology of deviance, focusing on how individuals respond to norm violations. A third line explores attachment insecurity and its impact on emotional expression across cultural contexts, comparing individualistic and collectivist societies. The body of his recent publications reveals a strong focus on prejudice reduction mechanisms (e.g., imagined contact, storytelling), identity dynamics in conflict zones, and the psychological underpinnings of misinformation spread on social media. His work often integrates social identity theory with applied interventions, aiming to improve intergroup understanding and reduce societal division. Orkun Yetkili is actively involved in doctoral supervision, mentoring students on topics including online misinformation, intergroup contact, and value transmission. He has secured external funding from the Society for Research into Higher Education (SRHE) for a project on the impostor phenomenon among racially minoritised university students. Professional Affiliations: • Centre for Psychological Sciences, University of Westminster Selected Engagement Activities: British Educational Research Association Conference, 2023 University of Huddersfield Teaching and Learning Conference, 2023 Difference Festival, 2021 Public engagement: Degrees of difference: Exploring degree-award gaps , 2024 Supervision Interests: Prejudice and stereotyping Intergroup contact and conflict reduction Self and identity Leadership and group dynamics Social norms and deviance

• Stochastic Processes
• Probability Theory
• Mathematical Biology
• +5 more

Xavier Bardina Simorra is a Professor in the Department of Mathematics at Universitat Autònoma de Barcelona (UAB). He holds a Ph.D. in Mathematics (2000) and a Licenciatura in Mathematics (1994), both from UAB. His research focuses on stochastic processes, including applications in biosciences, population dynamics, and epidemiological modeling. He has led multiple research projects funded by institutions like the European Regional Development Fund and the Ministerio de Ciencia e Innovación. Key projects include the 2019-2022 initiative on stochastic and deterministic models for biosciences, exploring topics like asymptomatic transmission and hierarchical competition. His work also involves weak convergence analysis, Lévy processes, and fractional Brownian motion. Bardina contributes to UN Sustainable Development Goals, particularly in education and bioscience applications. He has advised two doctoral theses and maintains active collaborations in stochastic modeling and mathematical biology. His lab, Advanced Stochastic Modelling, emphasizes interdisciplinary applications.

Stefano TomasinView profile

Professor

• Wireless Communications
• Physical Layer Security
• Reconfigurable Intelligent Surfaces
• +5 more

Stefano Tomasin is a Full Professor at the Department of Information Engineering, University of Padova, Italy. His research focuses on wireless communications, physical layer security, and reconfigurable intelligent surfaces (RIS) for 5G and beyond networks. He leads projects on RIS-assisted physical layer security, including signal processing for confidentiality, authentication techniques, and secure localization. Research Interests: Prof. Tomasin specializes in wireless security mechanisms with emphasis on physical-layer authentication, underwater acoustic networks, and RIS optimization. His work integrates signal processing with machine learning for jamming detection, secure key generation, and adaptive reconfiguration of intelligent surfaces. Recent Publications: His 2023-2025 articles demonstrate concentrated work on RIS-assisted security, underwater authentication, and anti-jamming techniques for 5G/6G networks. Dominant themes include physical-layer authentication protocols, channel-based key generation, and machine learning applications for signal security. Projects: Currently leading a national project on RIS for physical layer security in 5G+ networks, investigating confidentiality enhancement, authentication methods, and secure localization using advanced metasurfaces. Actively recruiting post-doctoral researchers for related work. Teaching & Advising: Supervises thesis projects and teaches courses in communications systems. Coordinates with the Department of Information Engineering's research initiatives in wireless technologies.

• Marine Sensors
• Scientific Applications of Submersibles
• Polymetallic Sulfide Resources
• +5 more

Dr. Wei Cai is a Research Associate Professor in the Department of Ocean Science and Engineering at the Southern University of Science and Technology (SUSTech). He also serves as Assistant to the President of the Academy of Marine Sciences at SUSTech and Assistant Director of the Shenzhen Branch of the Laboratory of Marine Science and Engineering of Guangdong Province. Additionally, he is the Deputy Director of the Marine Robot & Power System Laboratory and a member of the National Technical Committee for the Standardization of Submersibles. Dr. Cai received his educational background as follows: Doctor of Biomedical Engineering : Zhejiang University, September 2007 - June 2012 Bachelor of Biomedical Engineering : Zhejiang University, October 2002 - June 2007 Dr. Cai has dedicated his career to marine technology research for nearly 15 years, with expertise spanning multiple disciplines of ocean science and engineering. His primary research interests focus on marine sensors , scientific applications of submersibles , and polymetallic sulfide resources in the international seabed area . His work bridges engineering innovation with practical oceanographic applications, particularly in deep-sea exploration technologies. Dr. Cai has extensive field experience, having participated in 12 cruises on China Ocean Voyages, where he served as chief scientist for several important missions including sea trials of the "Qianlong II" AUV in 2016, "Qianlong III" AUV in 2018, and Deep-Sea Drilling Rig in 2021. His January 2019 expedition with the "Deep Sea Warrior" manned submersible reached a depth of 2,773 meters in the Southwest Indian Ocean, demonstrating his hands-on expertise in extreme deep-sea environments. Dr. Cai's research portfolio demonstrates a clear progression from fundamental sensor development to sophisticated deep-sea exploration systems. His early work focused on chemical sensors for water quality monitoring, particularly for heavy metal detection, which laid the foundation for his later specialization in marine applications. Over time, his research evolved toward deep-sea technologies, with increasing emphasis on autonomous underwater vehicles (AUVs), hydrothermal vent systems, and polymetallic sulfide resource exploration. His publications reveal strong interdisciplinary connections between chemical engineering, robotics, marine geology, and environmental science, with practical applications in both resource exploration and environmental protection. Dr. Cai's scientific achievements have been recognized with several prestigious awards: Second Prize of National Science and Technology Progress Award (Team), 2019 Outstanding Contribution Award of China Marine Engineering Consulting Association (Team), 2019 Outstanding Graduate of Zhejiang Province, 2012 2022 Pearl River Talent Plan Young Top Talent 2022 Shenzhen "Pengcheng Peacock Plan" Special Post C As a principal investigator, Dr. Cai has secured significant research funding, including the National Key Research and Development Program of China, projects from the Deep Blue Project Fund by Shanghai Jiao Tong University, and various provincial and national research grants. His leadership extends to serving as deputy chief designer of technical upgrade and scientific application of the "Qianlong II" AUV. Dr. Cai has also contributed to major scientific endeavors as a participant in numerous China Ocean Voyage projects, with funding ranging from millions to tens of millions of RMB per project. Dr. Cai leads the Marine Robot & Power System Laboratory at SUSTech, where his team focuses on developing advanced marine technologies for deep-sea exploration. His laboratory work integrates robotics, sensor development, and data analysis to create comprehensive solutions for underwater research and resource exploration. The lab's current projects include developing high-sensitivity methane sensors for deep-sea environments, systems for detecting heavy metals in hydrothermal plumes, and wireless sensor networks for red tide early warning systems. This multidisciplinary approach positions Dr. Cai's team at the forefront of marine technology innovation in China.

Tom BrittonView profile

Professor

• Epidemic modeling
• Biostatistics
• Network analysis
• +5 more

Tom Britton is a Professor in Mathematical Statistics (Holder of the Cramér chair) at the Department of Mathematics, Stockholm University. He leads the SU-Infection spread modeling research group and serves as chairman of the Stockholm Mathematics Center (SMC). His work primarily focuses on developing and analyzing mathematical/statistical models for infection spread and applying statistical methods to infection spread data. His research interests span epidemic modeling, biostatistics, network analysis, and mathematical statistics with applications toward genetics and molecular biology including phylogenetics. Britton's work encompasses applications of biostochastics and biostatistics, discrete random structures, and finance and insurance. He has made significant contributions to understanding disease transmission dynamics, particularly during the COVID-19 pandemic. Britton's recent publications demonstrate a strong emphasis on practical applications of mathematical models to public health challenges. His work examines contact tracing strategies, vaccine impact analysis, immunity waning dynamics, and optimal intervention strategies. There is a clear pattern of translating theoretical mathematical models into actionable public health insights, with particular focus on the interplay between population heterogeneity, immunity levels, and intervention effectiveness. As an academic leader, Britton serves as academic editor for PLoS Comp Biol and associate editor for Journal of Mathematical Biology and JRSS A (Statistics in Society). He is also a board member of the European Society for Mathematical and Theoretical Biology (ESMTB). Britton actively mentors the next generation of researchers, currently supervising PhD student Fanny Bergström and Post Doc Martina Favero. Throughout his career, he has successfully guided 13 PhD students and multiple postdoctoral researchers. His current research is supported by significant grants from the Swedish Research Council (3.6 MSek, PI 2021-2024) and the KA Wallenberg Foundation (0.7 MSek, PI 2023-2025). The SU-Infection spread modeling research group, which Britton leads, develops and analyzes mathematical/statistical models for the spread of infection and applies statistical methods to adapt these models to infection spread data. This work has been particularly relevant during recent pandemics and continues to inform public health decision-making.

Kamal PremaratneView profile

Professor

• Conspiracy Theory Research
• Social Media Analysis
• Machine Learning
• +5 more

Kamal Premaratne serves as a Professor in the Department of Electrical & Computer Engineering at the College of Engineering, University of Miami. His scholarly work uniquely bridges technical expertise in machine learning and signal processing with social science research on conspiracy theories and political extremism. With numerous publications in both technical and social science journals, he demonstrates an exceptional interdisciplinary approach to understanding complex contemporary phenomena. Professor Premaratne's research spans two primary domains: advanced computational methods and social/political analysis. In computational methods, he focuses on quantum tensor networks for time series analysis, graph neural networks for gesture recognition, and uncertainty quantification in machine learning systems. His social science research examines conspiracy theories including the "White Replacement" theory, QAnon, and "white genocide" narratives, investigating their sociodemographic correlates and relationship to political extremism. His work often employs mixed-methods approaches combining qualitative analysis with quantitative content analysis and network analysis. Analysis of Premaratne's recent publications reveals a strong focus on understanding how conspiracy theories spread on social media platforms and their relationship to political behavior. His technical work shows innovative applications of quantum physics concepts to machine learning problems, creating more interpretable models while maintaining performance. This dual focus demonstrates how computational methods can be applied to social science questions and vice versa. Professor Premaratne actively collaborates with researchers across disciplines including psychology, political science, and communication studies. His research has been published in high-impact journals such as Scientific Reports, Journal of Politics, and Political Science Quarterly. Though specific grant information isn't detailed in the available materials, his extensive publication record suggests substantial research activity. His work has significant implications for understanding political polarization, misinformation spread, and developing more interpretable AI systems.

Tsichlas KostasView profile

Associate Professor

• Algorithms and Data Structures for Main and Secondary Memory
• Algorithms and Data Structures in Distributed Environments
• Computational Geometry
• +5 more

Tsichlas Kostas serves as an Associate Professor in the Department of Computer Engineering and Informatics at the University of Patras, Greece, within the Division of Applications and Foundations of Computer Science. His research spans fundamental and applied computing domains with active involvement in the ML@Cloud laboratory. His expertise centers on algorithmic innovation across multiple dimensions: Memory-optimized algorithms for primary/secondary storage systems Distributed environment data structures and computational geometry Physics-informed computing and complex network analysis Specialized domains including alphanumeric and graph algorithms Recent publication trends (2022-2025) demonstrate concentrated research in historical graph management systems, temporal network analysis, and physics-computing intersections. Key contributions include vertex-centric partitioning strategies, temporal community detection frameworks, and machine learning applications for energy data motif discovery. He maintains active laboratory affiliations with the LARGE-SCALE CLOUD DATA MACHINE LEARNING WORKSHOP (ML@Cloud lab), Combinatorial Algorithms Laboratory, and Distributed Systems and Telematics Laboratory, contributing to Greece's computational research infrastructure.

• Operations Research
• Management Science
• Optimization
• +5 more

Evangelos F. Mageirou serves as Professor of Operations Research at the Department of Informatics, School of Information Sciences and Technology, Athens University of Economics and Business (AUEB), a position held continuously since 1986. Previously, he worked at the Scientific Secretariat of the National Energy Council under Greece's Ministries of Coordination, Industry and Energy from 1977-1986. His academic credentials include: PhD in Decision and Control Sciences, Harvard University (1972-1976), Thesis: 'Topics in the Analysis of Interconnected Systems' MS in Decision and Control Sciences, Harvard University (1971-1972) BS in Electrical Engineering, Princeton University (1967-1971) Mageirou's research centers on Theoretical and Applied Operations Research with significant applications to the Greek business environment, particularly in finance, shipping logistics, and production systems. His foundational work spans optimization theory, probabilistic modeling, and quantitative methods development. The consistent publication record through 2020 demonstrates enduring scholarly productivity in his core domains. Analysis of recent publications reveals three dominant research trajectories: maritime logistics optimization (vessel speed/positioning studies), game-theoretic security applications (spam email modeling), and control theory extensions (policy iteration algorithms). These threads demonstrate methodological continuity from his early work on interconnected systems to contemporary applications in financial mathematics and pandemic response modeling.

• Fluid Dynamics
• Heat Transfer
• Thermal Sciences
• +6 more

Professor K. Muralidhar of the Department of Mechanical Engineering at Indian Institute of Technology Kanpur specializes in fluid dynamics, thermal sciences, and optical imaging techniques. With over 35 years of academic leadership, his work bridges experimental and computational approaches in transport phenomena. PhD from University of Delaware (1985) 20+ Doctoral students supervised 500+ Publications across 13 research areas Fellow of ASTFE, NAS, and INAE His key research domains include: Drop dynamics on hydrophobic surfaces Heat transfer in porous media Crystal growth visualization Biofluid mechanics in vascular systems Advanced tomographic imaging Cryogenic flow modeling Recent work focuses on CO2 sequestration , solar desalination , and non-invasive medical diagnostics , with significant contributions to Physics of Fluids and International Journal of Heat and Mass Transfer . Scientific awards highlight his global recognition: 2019: Fellow-ASTFE 2014: Keynote speaker, International Heat Transfer Conference 2006: INAE Fellowship 1995: Institution of Engineers Prize 1981: IIT Madras Institute Medal Pioneering optical measurement techniques for convection and phase change processes, he has developed novel approaches in schlieren tomography, interferometry, and digital holography. His laboratory's patent portfolio includes medical devices and fluid control systems.

• Control Systems Engineering
• Networked Control Systems
• Diffusion Processes
• +4 more

Fangzhou Liu is currently a Professor at the Research Institute of Intelligent Control and Systems, School of Astronautics, Harbin Institute of Technology, China (2022–Present). He previously held a postdoctoral position (2019–2022) and completed his PhD (2014–2019) at the Chair of Automatic Control Engineering, Technische Universität München, Germany. His academic journey includes a visiting PhD stint at the University of Groningen (2018) and degrees from Harbin Institute of Technology: M.Sc. (2012–2014) and B.Sc. (2008–2012) in Control Science and Engineering. Research Interests: Focus on control of complex systems, including diffusion processes over complex networks, opinion dynamics, networked control systems, model predictive control, and sliding mode control. Key themes: large-scale systems, dynamic optimization, and robust control. Publication Trends: Recent works emphasize robotic control (reinforcement learning, barrier functions), switched systems (parameter estimation, identification), and epidemic modeling (deep transfer learning for forecasting). Collaborations with Martin Buss and others span robotics, control algorithms, and optimization. Awards: PhD Award 2019 of the Department of Electrical and Computer Engineering Labs & Teams: Affiliated with the Chair of Automatic Control Engineering (Technische Universität München) and Harbin Institute of Technology’s Research Institute of Intelligent Control and Systems. Collaborates on projects involving intelligent control, robotics, and networked systems.

Adam KochanskiView profile

Associate Professor

• Wildland Fire Modeling
• Fire-Atmosphere Coupling
• Smoke Dispersion and Air Quality
• +5 more

Adam Kochanski is an Associate Professor in the Department of Meteorology and Climate Science at San Jose State University's College of Science, where he leads research in wildfire-atmosphere interactions through the Wildfire Interdisciplinary Research Center. His work bridges atmospheric science, fire ecology, and numerical modeling to address critical wildfire forecasting challenges. Dr. Kochanski's research focuses on fire-atmosphere coupling mechanisms, with particular expertise in coupled fire-atmosphere modeling using WRF-SFIRE. His work spans numerical forecasting of large wildland fire events, air quality impacts of wildfire smoke, plume dynamics, fuel moisture modeling, and satellite data assimilation techniques. He develops and applies advanced modeling systems including WRF, WRF-CHEM, QUIC, and STILT on high-performance computing platforms. His publication record shows consistent output with 15+ publications annually, demonstrating active leadership in wildfire modeling research. Recent work emphasizes practical applications including fire perimeter assimilation, fuel moisture reanalysis, and machine learning approaches for wildfire forecasting, addressing both fundamental science questions and operational fire management needs. Dr. Kochanski collaborates extensively with federal agencies including the National Science Foundation, Joint Fire Science Program, US Forest Service, and NASA, indicating significant research support and real-world impact of his work. His contributions to the WRF-SFIRE modeling system have established him as a key developer in the international wildfire modeling community.

Daniel Edgington-MitchellView profile

Associate Professor

• Aeroacoustics
• Fluid Mechanics
• High Speed Propulsion
• +7 more

Daniel Edgington-Mitchell is an Associate Professor and ARC Future Fellow in the Department of Mechanical and Aerospace Engineering at Monash University. He is also a researcher at the Laboratory for Turbulence Research in Aerospace and Combustion, contributing significantly to the fields of aeroacoustics and fluid mechanics. His research focuses on jet noise, high-speed propulsion, and turbulence, with particular emphasis on understanding and controlling screech, resonance, and coherent structures in non-axisymmetric and supersonic jets. He employs advanced experimental techniques, including optical diagnostics and acoustic characterization, to study complex flow phenomena. His work contributes to the United Nations Sustainable Development Goals related to sustainable industry and innovation. The trend in his recent publications shows a strong focus on elliptical and non-axisymmetric jets, guided waves, axis switching, and noise reduction strategies. His research integrates theoretical, experimental, and computational approaches to address fundamental challenges in aerospace propulsion and aeroacoustics. Scientific Awards: Vice-Chancellor's Diversity and Inclusion Award, Monash University (2020) Daniel Edgington-Mitchell has led multiple competitive research projects funded by the Australian Research Council (ARC), including 'The art of controlling multijet resonance' and 'Elliptical nozzles: the shape of silence?'. He serves as the Primary Chief Investigator on active projects aimed at reducing rocket resonance for safer spaceflight and improving jet noise control. He mentors students and collaborates with leading international researchers such as Colonius, Jordan, and Samimy. He is affiliated with the Laboratory for Turbulence Research in Aerospace and Combustion, where he conducts experimental and theoretical work on high-speed flows and combustion dynamics. His lab utilizes advanced diagnostics and high-fidelity simulations to explore turbulence and noise generation mechanisms in propulsion systems.

Laura PollacciView profile

Research Fellow

• Social Network Analysis
• Visual Analytics
• Human Migration
• +5 more

Laura Pollacci is a postdoctoral researcher at the Knowledge Discovery and Data Mining Laboratory (KDDLab), a joint research group of the Information Science and Technology Institute of the National Research Council (CNR) in Pisa and the University of Pisa, where she holds a position as Research Fellow in the Department of Computer Science. Her educational background includes: Bachelor's Degree in Digital Humanities (2014) from University of Pisa with 110/110 cum laude Master's Degree in Digital Humanities (2015) from University of Pisa with 110/110 cum laude PhD in Computer Science (2019) from University of Pisa with thesis on 'conjunct usage of Big Data and Sentiment Analysis for the study of Human Migration' Pollacci's research focuses on Social Network Analysis and Visual Analytics for Social Mining, Human Migration, and Sentiment Analysis. Her work bridges computational methods with social science questions, particularly examining how digital traces can reveal patterns in human behavior, migration, and emotional expression across social platforms. She has developed expertise in analyzing complex social networks, music diversity, and political polarization through data-driven approaches. Her publication record shows a clear trajectory from computational linguistics and sentiment analysis toward broader applications in social network analysis, migration studies, and musicology. Early work focused on emotive lexicon development and sentiment polarity classification for Italian language, while more recent publications examine political polarization on Reddit, human migration patterns through big data, and the fractal dimensions of music diversity. Pollacci is actively involved with the HumMingBird project, which focuses on 'Enhanced migration measures from a multidimensional perspective.' Her work at KDDLab places her at the intersection of data science and social research, contributing to the laboratory's mission of developing theory, techniques and systems for extracting knowledge from large data sets.

Andreas KuebartView profile

Researcher

• Human Geography
• Economic Geography
• Entrepreneurial Ecosystems
• +12 more

Dr. Andreas Kuebart is a Research Associate in the Research Area 'Economy and Civil Society' at the Leibniz Institute for Spatial Social Research (IRS) and a Post-Doc Fellow at the Chair for Socio-Spatial Transformation at Brandenburg University of Technology (BTU). His interdisciplinary work bridges human and economic geography, focusing on entrepreneurial ecosystems, digital economy, and socio-spatial dynamics of health crises like the COVID-19 pandemic. Leibniz Institute for Spatial Social Research (IRS), Research Area Economy and Civil Society Chair for Socio-Spatial Transformation, Brandenburg University of Technology (BTU) He earned his Dr. rer. nat. in Geography from Freie Universität Berlin in 2020, with prior studies at Humboldt University of Berlin, University of Florida, Stockholm University, and Bonn University. PhD (Dr. rer. nat.), Geography, Freie Universität Berlin (2020) Studies in Geography, Humboldt University of Berlin, University of Florida, Stockholm University, Bonn University His research centers on the geographies of socio-spatial processes in the digital economy, particularly examining entrepreneurial ecosystems, financial geographies, regional development, and health geography. Methodologically, he combines qualitative case studies with spatial data science and sequence analysis to understand temporal and spatial dynamics in innovation and pandemic spread. The recent publications highlight a strong trend in analyzing trans-local entrepreneurial networks, startup trajectories, and the spatial-temporal evolution of both innovation ecosystems and public health crises. His work increasingly integrates advanced spatial analytics and sequence-based modeling, especially in the context of Berlin and European-wide systems. Dr. Kuebart has contributed to major policy-oriented outputs for the European Commission through the StepUP Startups project, including influential reports on Europe’s startup landscape and spin-off innovation. He is actively involved in third-party funded research such as 'Disruption and Spatial Development' and 'CoDiff' (Socio-Spatial Diffusion of COVID-19 in Germany), indicating sustained engagement with impactful, interdisciplinary research. He has advised on or led projects analyzing the role of open creative labs, seed accelerators, and spatial infrastructure in entrepreneurial ecosystems. His collaborative work spans multiple institutions and disciplines, often involving teams from IRS, BTU, and international partners. Dr. Kuebart is a key contributor to the 'CODIFF' project on the spatial diffusion of COVID-19 in Germany and has developed phase models and severity indicators for pandemic dynamics. He also leads analytical components in the 'StepUP Startups' project, which maps Europe’s entrepreneurial ecosystem using data-driven methods.

• Social Dynamics
• Statistical Mechanics
• Complex Systems
• +3 more

Romualdo Satorras is a Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona School of Informatics (FIB) and the Department of Physics. His primary research focuses on complex systems, statistical mechanics, and network theory. He leads the Condensed, Complex and Quantum Matter Group (CCQM) and has contributed extensively to understanding epidemic spreading, opinion dynamics, and collective behavior in social and biological systems. With an h-index of 44, his work bridges theoretical physics and applied computational modeling. Education: Holds a Doctorate in Physics (DOCTOR C. FÍSIQUES). Research interests include social dynamics, complex networks, and critical phenomena, with notable studies on pandemic modeling and collective decision-making mechanisms. His work integrates advanced mathematical methods with real-world data analysis. Recent research trends emphasize multidimensional opinion polarization, temporal network dynamics, and the interplay between risk perception and collective behavior in biological systems like fish schooling. His publications analyze both theoretical frameworks and empirical data from social media interactions and epidemiological systems. Scientific Awards: Not explicitly listed, but his h-index indicates significant scholarly impact. Advising and Grants: While specific grants are not detailed, his active research group suggests involvement in competitive funding. His work on activity-driven networks and epidemic thresholds has attracted international attention. Collaborates widely with institutions through networks analyzed in his studies. Labs/Teams: Member of the CCQM group, focusing on interdisciplinary research at the intersection of condensed matter physics and complex systems science.