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Mohammed Eunus Ali is a Senior Lecturer in the Department of Software Systems & Cybersecurity within the Faculty of Information Technology at Monash University, Australia. He holds a PhD in Computer Science and Software Engineering from the University of Melbourne and has previously served as a Professor at the Bangladesh University of Engineering and Technology (BUET), where he led a research group in Data Science and Engineering for over a decade. He has also held research positions at Monash University, Swinburne University, the University of Melbourne, and RMIT University. PhD : Computer Science and Software Engineering, University of Melbourne (2010) M.Sc. Engg. : Computer Science and Engineering, Bangladesh University of Engineering and Technology (2002) B.Sc. Engg. : Computer Science and Engineering, Bangladesh University of Engineering and Technology (1999) Dr. Ali’s research spans data management, analytics, and learning , with a strong focus on spatio-temporal data, geo-social networks, and multimodal high-dimensional data . His work enables applications in urban computing, intelligent transportation systems, and smart, sustainable cities . In recent years, he has expanded into Generative AI and large language models (LLMs) , exploring their role in enhancing geo-spatial query processing, SQL generation, and data engineering tasks. His publications appear in top-tier venues such as ACL, TKDE, VLDB, ICDE, SIGSPATIAL, and IEEE Access . His recent publications reflect a strong trend toward AI-driven solutions for real-world spatial and health problems , including blood glucose prediction for diabetics, seismic intensity forecasting, eco-friendly route planning, and LLM-based code generation. These works demonstrate a convergence of deep learning, spatio-temporal analytics, and real-world system design . Scientific Awards: Bangladesh University Grants Commission Award (2012) ADC Best Poster Award (2016) SSTD Best Demo Award (2017) ADC Best Paper Award (2022) Dr. Ali actively contributes to the research community as a Program Committee Member for premier conferences including SIGMOD, VLDB, ICDE, and SIGSPATIAL . He is a Senior Member of the ACM and currently supervises PhD students, focusing on cutting-edge topics in data science and AI. His collaborative research network spans institutions in Australia and Bangladesh, contributing to advancements in both academic and applied domains. His work aligns with the UN Sustainable Development Goals , particularly in the areas of sustainable cities, innovation, and quality education.

• Data Management
• Data Analytics
• Spatio-temporal Databases
• +9 more

Ignasi Gomez Sebastia is a researcher in the Department of Computer Science at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona School of Informatics (FIB). He holds a PhD and specializes in Artificial Intelligence applied to healthcare, multi-agent systems, and social network analysis. His work focuses on developing intelligent systems for healthcare support (e.g., LactApp for breastfeeding), agent-based models for elderly care, and smart city technologies for mobility optimization. Key contributions include frameworks like NoMoDEI for norm monitoring in electronic institutions and research on spatio-temporal patterns in location-based social networks. He actively collaborates with groups such as KEMLG and IDEAI-UPC, contributing to projects like SUstainable and PERsuasive Human Users mobility (SUPErHUB) and the AVICENA elderly healthcare initiative. His research spans over 30 publications since 2008, covering topics from mHealth applications to agent-oriented organizational modeling. He has led or participated in competitive R+D+i projects focusing on healthcare technology, smart cities, and AI-driven solutions for SMEs. His work bridges theoretical AI advancements with practical implementations in health, urban systems, and collaborative environments.

• Artificial Intelligence
• Health Informatics
• Multi-Agent Systems
• +3 more

Katy Serafin is an Assistant Professor at the University of Florida, affiliated with the Florida Climate Institute and the UF Water Institute. Her research focuses on coastal hazards, extreme sea levels, and climate adaptation. She holds a PhD in Ocean, Earth, and Atmospheric Sciences from Oregon State University and completed postdoctoral work at Stanford University's Department of Geophysics. Her interdisciplinary research combines observational data with statistical and numerical models to study coastal flooding drivers, compound flood risks, and community resilience. Key projects include assessing atmospheric river impacts in California, developing scalable flood risk frameworks, and analyzing extreme water level dynamics along US coastlines. Funded projects (2020-2027) include NOAA-supported studies on total water level variability and USACE-funded research on flood transition zones. She advises four doctoral and one master's student, and teaches courses like 'Living with Rising Seas' and 'Water Risk and Extreme Events.' Her work emphasizes equity in flood mitigation, traffic disruption resilience, and policy-relevant climate adaptation strategies. Collaborations span academia, government agencies (e.g., NOAA, USGS), and international institutions.

• Coastal Science
• Climate Change
• Flood Risk Management
• +4 more

Benjamin Aubrey Robson is an Associate Professor in Geomatics at the Department of Earth Science, University of Bergen (UiB), specializing in Geomatics and Remote Sensing. His work focuses on applying advanced geospatial technologies to study glacial and periglacial environments in Norway and high-mountain regions worldwide. Robson leads research initiatives that integrate Earth Observation, UAV technology, and Geospatial Artificial Intelligence to monitor and analyze cryospheric changes. Dr. Robson's research interests span two primary areas: Quantifying Glacial and Periglacial Changes Over Time and Geospatial Artificial Intelligence (GeoAI). He utilizes photogrammetry and topographic analysis with historical aerial imagery, UAVs, LiDAR, and satellite data to reconstruct glacier and rock glacier evolution since the mid-20th century across Norway, Chile, Nepal, and the European Alps He applies machine learning techniques to geospatial data for automated identification of complex landforms like rock glaciers and landslides, while also investigating the reliability and trustworthiness of GeoAI algorithms His recent publications demonstrate a strong focus on integrating deep learning with remote sensing for cryospheric monitoring. The research shows increasing sophistication in using multiple sensor platforms (UAVs, satellite imagery, LiDAR) combined with advanced AI techniques to address complex glaciological questions. Notable trends include the application of transfer learning for limited-data scenarios, comparative analysis of deep learning versus traditional methods, and expanding GeoAI applications from glacier monitoring to hydrocarbon seepage detection and other geoscientific domains. Dr. Robson actively supervises graduate students and leads significant research projects: PhD students: Daniel Thomas (GeoAI mapping of rock glaciers), Yiyao Chen (Snow-depth predictions using GeoAI) Co-supervision of multiple PhD and MSc students on GeoAI applications Principal Investigator for SnowAI, CO-INVENT, and AI integration in Earth Sciences education projects Co-PI for ROGER and JOSTICE projects He contributes extensively to education at UiB through courses including Introduction to GIS, Practical Skills in Remote Sensing, and Field Methods in Quaternary Geology, while also organizing specialized summer schools on UAV applications and machine learning in glaciology.

• Geomatics
• Remote Sensing
• Earth Observation
• +7 more

George Ashdown is a Research Fellow at the School of Biosciences, part of the College of Health and Life Sciences at Aston University. He leads a research program focusing on host-pathogen interactions, particularly targeting intracellular Mycobacterium tuberculosis using advanced imaging and 'omics techniques. His work integrates super-resolution microscopy with machine learning to study drug delivery systems and disease biomarkers. He holds a PhD from King's College London (2017) on T cell immunological synapse dynamics and an MSc from the University of Edinburgh (2011) in Quantitative Cell and Molecular Imaging. His research has spanned malaria parasite lifecycle analysis using high-throughput imaging and AI-driven drug response studies. Ashdown's expertise spans lattice light-sheet microscopy, AI applications in biological imaging, and the development of novel drug delivery systems. He actively supervises PhD projects in host-pathogen interactions and lipid-based therapies. His contributions include pioneering methods to quantify molecular dynamics in T cells and advancing automated malaria diagnostics using convolutional neural networks. Current projects emphasize leveraging spatiotemporal data from cutting-edge microscopy for clinical translation. Research outputs highlight interdisciplinary approaches combining microscopy innovation with computational biology. He collaborates extensively on malaria and tuberculosis projects, contributing to both fundamental understanding and applied therapeutic development.

• Host-pathogen interactions
• Mycobacterium tuberculosis
• Advanced imaging techniques
• +3 more

Pere Barlet-Ros is a Full Professor in the Department of Computer Architecture at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona School of Informatics (FIB) and the Barcelona Neural Networking Center (BNN-UPC). He leads research in AI-driven network technologies and is a key member of the UPC CBA and IDEAI-UPC research groups. Ph.D. in Computer Science, UPC (2008) M.Sc. in Computer Science, UPC (2003) His research focuses on applying artificial intelligence, particularly graph neural networks, to solve critical challenges in network monitoring, traffic classification, congestion control, and cybersecurity. His work bridges theoretical innovation with practical deployment, as evidenced by integration into commercial tools like Auvik TrafficInsights and Talaia Polygraph. He explores digital twins for network optimization, privacy-preserving technologies, and next-generation web tracking detection. The recent publications highlight a strong trend toward leveraging graph-based machine learning for network modeling, anomaly detection, and traffic compression. His work spans top-tier journals like IEEE/ACM Transactions on Networking and conferences such as IEEE EuroS&P and ACM SIGCOMM workshops, emphasizing robust, real-world AI/ML applications in networking. 2nd VALORTEC prize for best business plan (2014) Fiber Entrepreneurs award, best entrepreneur at FIB (2015) Pere Barlet-Ros has served as Principal Investigator on seven competitive research projects and has advised at least 14 PhD students, including Paul Almasan, Miquel Ferriol, and Ismael Castell. His lab, BNN-UPC, fosters interdisciplinary collaboration in neural networking and AI for infrastructure. His entrepreneurial experience with Talaia Networks underscores a commitment to translating research into impactful technology.

• Cybersecurity
• Artificial Intelligence
• Computer Networks
• +7 more

Robert Hecht is a Senior Lecturer at the Chair of Spatial Development and Transformation, Faculty of Environmental Sciences, Dresden University of Technology (TUD), and a Senior Researcher and Project Coordinator at the Leibniz Institute of Ecological Urban and Regional Development (IOER). His work bridges academic research and practical urban planning, with a strong focus on geospatial technologies, machine learning, and citizen science. Research Interests: Spatio-temporal analysis of urban and settlement structures Machine learning and AI in geospatial data Urban green space monitoring and accessibility Volunteered Geographic Information (VGI) and crowdsourcing Building stock analysis and climate-friendly architecture Quality of spatial data and open urban data platforms Recent Research Trends: His recent publications highlight a shift toward open, participatory urban data ecosystems. He leads the Colouring Cities initiative, particularly the Colouring Dresden platform, which uses citizen science to document building characteristics for research and climate resilience. His work increasingly integrates digital twins, urban analytics, and interdisciplinary methods to address urban sustainability and equity in access to green and medical services. Scientific Awards: Winner, Citizen Science Competition 'Auf die Plätze!' (2022) Best Paper Award, SDSC2021 Winner, Pallas Ludens Crowdsourcing Challenge (2015) DAAD Research Scholarship (IIT Kanpur) Advising and Grants: While no direct advisees are listed, Hecht has led or contributed to numerous funded research projects such as GOAT 3.0, meinGrün (BMVI), ScaDS.AI (BMBF), ENOB:dataNWG (BMWI), and the Colouring Cities Research Programme. He has secured funding from federal ministries and international bodies, demonstrating strong grant leadership in urban data science and sustainability. Labs and Teams: He is a key member of the Research Area Spatial Information and Modelling at IOER and leads the European Hub of the Colouring Cities Research Programme . He collaborates with interdisciplinary teams across Germany and Europe, including researchers in geoinformatics, urban planning, ecology, and computer science.

• Geoinformatics
• Spatial Analysis
• Urban Analytics
• +15 more

Changqing Lu serves as an ERCIM Fellow within the Stochastics department at Centrum Wiskunde & Informatica (CWI), specializing in the integration of spatial statistics and machine learning for environmental risk modeling. Primary research focuses on Dutch fire risk prediction using advanced point process methodologies. Lu's work bridges theoretical statistics with practical applications, particularly through tree-based algorithms like XGBoostPP for intensity function estimation. Key research domains include spatio-temporal point process modeling, environmental variable integration, and data-driven hazard assessment, with emphasis on chimney fire prediction in residential contexts. Recent publications (2022-2025) demonstrate consistent progression toward operational fire risk systems, combining journal articles in Journal of Computational and Graphical Statistics and Annals of Applied Statistics with conference presentations at the ISI World Statistics Congress. The research trajectory shows increasing methodological sophistication in machine learning applications for spatial risk modeling. Scientific recognition includes the competitive ERCIM Fellowship. No student advising records or additional awards are documented in current materials. Lu maintains active research collaboration with M.-C. van Lieshout and contributes to discussion papers in leading statistical journals.

• Spatial Statistics
• Point Process Modeling
• Machine Learning
• +3 more

Malcolm Kennett is a Professor in the Department of Physics at Simon Fraser University's Faculty of Science, where he serves as Principal Investigator leading an active research group in quantum condensed matter physics. His work spans theoretical investigations of disordered and out-of-equilibrium quantum systems with significant emphasis on particle interactions. Professor Kennett's research interests center on quantum condensed matter systems, particularly disordered and out-of-equilibrium quantum phenomena. His group investigates Bose-Hubbard models, two-dimensional electron gases, Dirac fermions in graphene systems, glassiness in electronic materials, and connections to plasma astrophysics. Recent work has focused on machine learning applications to quantum dynamics, fractional quantum Hall states in graphene, and charge glass behavior in extended Hubbard models. Analysis of his recent publications reveals a strong trend toward computational and theoretical approaches to quantum dynamics, with increasing integration of machine learning techniques for analyzing correlation spreading in quantum systems. His work bridges condensed matter physics with quantum information science, particularly in understanding the dynamics of quantum phase transitions and non-equilibrium behavior. Supervised 20+ graduate and undergraduate students since 2006 Principal Investigator for ongoing quantum condensed matter research Author of textbook 'Essential Statistical Physics' Regularly teaches graduate courses including PHYS 841 (Graduate Statistical Mechanics) and undergraduate computational physics Professor Kennett maintains an active laboratory environment with multiple graduate students working on theoretical and computational projects related to quantum many-body systems. His research group regularly presents at conferences including Quantum BC Research Day, where his students have won multiple awards.

• Quantum Condensed Matter
• Disordered Quantum Systems
• Out-of-Equilibrium Quantum Dynamics
• +4 more

Carla Ferreira is a Full Professor in the Computer Science Department at NOVA University of Lisbon and a researcher at NOVA Laboratory for Computer Science and Informatics (NOVA LINCS) in the Software Systems research group. Her academic career began as an Assistant at Universidade do Minho, followed by positions at the University of Southampton's Dependable Systems & Software Engineering Group, and later as an Assistant Professor at IST before joining her current position at NOVA University Lisbon. Her primary research interests focus on Models of concurrency and distribution , Foundations of cloud computing , and Language-based techniques for low-code software development . She has made significant contributions to distributed systems, particularly in the areas of data replication, consistency models, and programming languages for distributed environments. Her work bridges theoretical foundations with practical applications in cloud and edge computing systems. Dr. Ferreira's recent publications demonstrate a strong focus on low-code development platforms, particularly the OSTRICH language framework she has developed, as well as replicated data types, spatio-temporal monitoring systems, and ontology-based approaches to software development. Her research consistently addresses the challenges of building reliable distributed systems while improving developer productivity through language-based techniques. She has served on the program committees of numerous prestigious conferences including ECOOP, FORTE, POPL, and TACAS, demonstrating her standing in the programming languages and distributed systems communities. As an academic advisor, Dr. Ferreira has supervised numerous PhD and MSc students, both ongoing and completed, working on topics ranging from distributed databases to low-code development and formal verification. She has also led significant research projects including PRECISE (as Principal Investigator), GOLEM, LightKone, and currently TaRDIS (Trustworthy and Resilient Decentralised Intelligence for Edge Systems).

• Models of concurrency and distribution
• Foundations of cloud computing
• Language-based techniques for low-code software development
• +5 more

Mohamed F. Mokbel is a Professor at the University of Minnesota with a distinguished career in spatial databases and big spatial data management. His research spans over two decades with more than 260 publications in top-tier venues including ICDE, SIGMOD, VLDB, and GIS conferences. He has established himself as a leading researcher in trajectory data management, mobility data science, and scalable spatial processing systems. Dr. Mokbel's research interests focus on the intersection of spatial databases, big data, and machine learning. He has pioneered work in trajectory data management systems, developing frameworks like ST-Hadoop for processing spatio-temporal data at scale. His recent research explores the application of large language models to trajectory analysis, spatial data cleaning systems with spatial awareness, and innovative approaches to mobility data science. His work addresses fundamental challenges in handling massive spatial datasets while maintaining efficiency and accuracy. His publication record reveals significant trends toward integrating machine learning with spatial data management, particularly in trajectory imputation using BERT models (KAMEL system) and applying NLP techniques to trajectory analysis. Recent work shows increasing focus on urban mobility applications, privacy considerations in location data, and the development of specialized frameworks for processing different types of spatial data at scale. Dr. Mokbel has mentored numerous students who have become productive researchers in their own right, with many publications featuring students as first authors. His collaborative network is extensive, with frequent co-authorship with researchers like Walid G. Aref, Ahmed Eldawy, and Amr Magdy, indicating strong research group leadership. He has contributed significantly to the spatial database community through systems like RASED for monitoring OpenStreetMap updates, KAMEL for trajectory imputation, and Sparcle for spatial data cleaning. His work bridges theoretical database concepts with practical applications in transportation, urban planning, and location-based services.

• Spatial Databases
• Big Spatial Data
• Location-Based Services
• +6 more

Roberto Neglia is a Research Fellow at the Department of Mathematics and Statistics , UiT The Arctic University of Norway . He is affiliated with the Northernmost Graph Machine Learning Group , focusing on foundational research in machine learning for time series and graphs. Research Focus: Generative models, randomized architectures, and topological data analysis tools for large spatio-temporal models and uncertainty quantification. Location: Forskningsparken 1 B496, Tromsø. Research Interests include: Machine Learning for Graphs and Time Series Topological Data Analysis in Machine Learning Spatio-temporal Modeling Uncertainty Quantification

• Machine Learning for Graphs and Time Series
• Generative models for graphs
• Topological Data Analysis applied to Machine Learning
• +2 more

David Lattanzi is a Professor at George Mason University within the Sid and Reva Dewberry Department of Civil, Environmental, and Infrastructure Engineering. He is also the John Toups Faculty Fellow, focusing on the development of advanced infrastructure inspection technologies that combine data analytics, robotics, and artificial intelligence to address aging infrastructure challenges. His work extends into healthcare, creating tools like a mobile application for documenting bruises in survivors of interpersonal violence. Education: PhD, Civil Engineering, University of Washington MS, Mechanical Engineering, University of Washington MS, Civil Engineering, Tufts University BS, Civil Engineering, Tufts University Research Interests: Dr. Lattanzi's research spans diagnostic computer vision, digital twins, robotic inspection systems, and bodily injury detection. His work integrates computational geometry, strain gauge data, and point cloud analysis to enhance structural health monitoring, while also applying AI to medical imaging for victim support. Publication Trends: His recent articles emphasize AI-driven infrastructure diagnostics, including multi-modal data fusion for bridge assessments, digital twin development for transportation systems, and bias mitigation in medical imaging. Collaborations across civil engineering, computer science, and healthcare domains reflect his interdisciplinary approach. Scientific Awards: John Toups Faculty Fellow Grants: George Mason University received a $4.85 million gift to advance intimate partner violence detection and reporting, likely linked to Dr. Lattanzi's AI-enabled bruise documentation tools. Labs and Teams: He leads a multidisciplinary research group at GMU, specializing in autonomous robotic inspection systems, ultra-high-resolution 3D imaging, and virtual world creation for infrastructure assessment. His team also develops mobile applications for forensic documentation and healthcare equity.

• Diagnostic computer vision
• Digital twins
• Robotic inspection
• +1 more

Assoc. Prof. Dr. Seçil Karatay is an Associate Professor at Kastamonu University's Faculty of Engineering and Architecture, Department of Electrical-Electronics Engineering. She holds a PhD in Physics from Fırat University (2010), an MSc in Physics (2005), and a BSc in Physics (2001). Her academic career spans roles including Department Head (2013-2017), Dean Assistant (2013-2016), and Director of Research Application Center (2016-2021). PhD: Physics, Fırat University, 2005-2010 MSc: Physics, Fırat University, 2002-2005 BSc: Physics, Fırat University, 1997-2001 Her research focuses on Signal Processing and Ionospheric Physics , particularly analyzing ionospheric disturbances related to seismic activity and space weather phenomena. She has pioneered algorithms like IONOLAB-FFT and Differential Rate of TEC (DROT) for detecting geophysical effects in ionospheric data. Her work spans machine learning applications for earthquake prediction, GPS-TEC analysis , and seasonal atmospheric variability studies. Recent publications highlight her expertise in 2023 Kahramanmaraş earthquakes ionospheric impact analysis, LSTM algorithm for disturbance classification, and Random Forest models for seismic precursor detection. With over 60 publications and citations exceeding 230, her research has significant SCI/SSCI indexing presence. Collaborations include Feza Arıkan (29 joint works), Ali Çınar (19), and Orhan Arıkan (14).

• Signal Processing
• Ionospheric Physics
• Seismology
• +3 more

Xosé Ramón Fernández Vidal is an Associate Professor at the Applied Physics Department of the University of Santiago de Compostela (Spain), where he has worked since 1992. His research focuses on Computer Vision and Visual Attention , with applications in robotics, photogrammetry, and scene recognition. PhD in Physics (1996) from the University of Santiago de Compostela MS in Physics (1991) from the same institution Key research areas include: Visual attention modeling and saliency detection 3D reconstruction using wireframes Scene recognition with invariant features Line matching in low-textured images Motion segmentation via energy features Recent publications (2017-2021) demonstrate expertise in dynamic saliency , UAV navigation , and synthetic dataset generation . Collaborations include researchers like Xosé M. Pardo, Xavier Otazu, and Victor Leboran. Funding projects involve robotics automation and "glocal" machine learning for intelligent devices.

• Computer Vision
• Visual Attention
• Scene Recognition
• +5 more