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Enzo D'InnocenzoView profile

Assistant Professor

• Econometrics
• Statistics
• Data Science
• +6 more

Enzo D'Innocenzo is a Junior Assistant Professor in Econometrics, Statistics, and Data Science at the Department of Economics, University of Bologna. His primary research focuses on advanced econometric methodologies, including time series analysis, statistical inference, asymptotic theory, score-driven models, and financial econometrics with applications to option pricing. Previously, he served as an Associate Researcher at the School of Business and Economics, Vrije Universiteit Amsterdam. Research Interests: Time Series Econometrics Volatility Modeling (GARCH, Score-Driven) Financial Markets Analysis Option Pricing Theories Extreme Value Theory Dynamic Pricing Strategies Notable Publications: His recent work includes advancements in score-driven models for spatio-temporal data (JASA 2023), dynamic partial correlation networks in financial markets (Journal of Econometrics 2024), and analysis of last-minute hotel pricing strategies (2024). His research bridges theoretical econometric innovation with practical applications in finance and hospitality sectors. Professional Activities: Presenter at NESG Conference 2022 (University of Groningen) Active contributor to econometric journals and conferences Maintains personal academic webpage with research links

• Landscape dynamics
• Biophysical properties of vegetation
• Vegetation productivity
• +6 more

Andrés Viña is an Associate Professor at Michigan State University (MSU), affiliated with the Department of Fisheries and Wildlife and the Center for Systems Integration and Sustainability. His work focuses on spatio-temporal vegetation dynamics and their impacts on human and natural systems, leveraging remote sensing technologies. Viña’s research bridges ecological processes with policy interventions, addressing global challenges like biodiversity conservation, climate change adaptation, and sustainable land use. His research interests include landscape dynamics, habitat modeling, and environmental policy, with a focus on tropical regions such as Brazil and China. He explores telecoupled systems—how distant human and environmental actions interconnect—through projects like analyzing soybean trade impacts on Amazon deforestation and climate-driven shifts in the Bering Sea. Viña’s publications often synthesize interdisciplinary approaches, combining field data with satellite imagery to inform conservation strategies. He emphasizes the need for integrated frameworks to tackle tri-sector challenges (biodiversity, climate, food security) and advocates for policies that balance economic demands with ecological preservation. Despite extensive research output, no specific scientific awards are listed, though his contributions to global environmental policy are widely recognized. His advising and grants focus on projects at the nexus of ecology, technology, and governance, though detailed grant information is not provided here. Viña is based at MSU’s Manly Miles Building and maintains an active research group within the College of Agriculture & Natural Resources.

Benjamin Aubrey RobsonView profile

Associate Professor

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

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

Changqing LuView profile

Research Fellow

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

Carla FerreiraView profile

Professor

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

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

David LattanziView profile

Professor

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

Annette MiltenbergerView profile

Assistant Professor

• Cloud microphysics
• Mixed-phase and ice-phase clouds
• Aerosol–cloud interactions
• +4 more

Jun.-Prof. Dr. Annette Miltenberger is an Assistant Professor at the Institute for Atmospheric Physics, Johannes Gutenberg University Mainz, Germany. She leads and co-leads multiple large-scale atmospheric research projects funded by the German Research Foundation and the HALO research aircraft programme. Education PhD (Dr. sc. ETH Zurich), 2014 – Thesis: Lagrangian perspective on dynamic and microphysical processes in orographically forced flows Research interests Her work centres on cloud microphysics, with particular emphasis on mixed-phase and ice-phase clouds, aerosol–cloud interactions, and the coupling between microphysics and atmospheric dynamics. She develops and applies Lagrangian analysis methods to study moisture transport, cirrus formation, and the representation of uncertainties in weather and climate models. Additional foci include foehn flow dynamics and the impacts of diabatic processes on the upper troposphere–lower stratosphere (UTLS) region. Projects & Funding 2025–2028 – NAWDIC (North Atlantic Waveguide, Dry Intrusion, and Downstream Impact Campaign), lead PI of the HALO sub-project NAWDIC-DImoist 2025–2028 – Co-PI of NAWDIC-MESO GLACIATE: Modelling-satellite-aircraft approach for cirrus advanced characterisation TPChange Project B8: Lagrangian analysis of extratropical cyclones for UTLS aerosol and humidity BINARY: Big Data in Atmospheric Physics M3ODEL: Mainz Institute of Multiscale Modeling ACPC initiative deep convection working group Scientific output trend Across 40+ peer-reviewed papers since 2013, her publications reveal a clear trajectory from fundamental process studies (secondary ice production, ice-nucleating particles) toward integrated model-intercomparison and field-campaign analyses. Recent work increasingly targets the UTLS region, warm conveyor belts, and the climatic impacts of aerosol perturbations, leveraging high-resolution ICON simulations, HALO aircraft data, and multi-model ensembles. Teaching activities Since 2019 – Supervising journal clubs and Bachelor/Master theses Since 2022 – Lecturer for “Clouds and Aerosols I” and “Introduction to Modelling and Data Analysis in Environmental and Atmospheric Sciences” 2020–2024 – Lecturer for “Clouds and meso-to large-scale dynamics”, “Application of Models / Modelling with Partial Differential Equations”, and project courses on urban heat islands and Rhine flooding 2019–2020 – Teaching assistant for modelling courses under Prof. Spichtinger Laboratory & Collaborations She operates within the Institute for Atmospheric Physics modelling group and collaborates extensively with the Karlsruhe Institute of Technology (KIT), ETH Zurich, the UK Met Office, and international HALO campaign partners. The group maintains high-performance computing resources for ICON-ART simulations and Lagrangian trajectory analyses.

Joanna Bac-BronowiczView profile

Associate Professor

• Geographic Information Systems (GIS)
• Cartographic Generalization
• Spatio-Temporal Data Modeling
• +3 more

Joanna Bac-Bronowicz serves as an Associate Professor at Wrocław University of Science and Technology within the Faculty of Geoengineering, Mining and Geology. Her primary affiliation is with the Department of Geodesy and Geoinformatics, where she conducts research on geospatial modeling and cartographic applications. She maintains active office hours through both physical and virtual (Zoom) channels. Her research focuses on spatio-temporal data modeling , cartographic generalization , and topographic database integration . Key interests include GIS-based environmental analysis, urban green space assessment, and spatial data infrastructure development. She has pioneered methodologies for integrating national geospatial registers and developing multi-resolution topographic databases. Analysis of her 15 most recent publications reveals strong emphasis on Geometric simplification techniques for cartographic features Urban ecosystem service evaluation using 3D geoportals Integration of LiDAR and remote sensing in smart city frameworks Topographic influences on precipitation patterns Her work consistently bridges theoretical cartography with practical applications in environmental management. Professional recognition includes membership in key national bodies: National Council of Geodesy and Cartography of the Surveyor General of Poland Council of Spatial Information Infrastructure at Minister of Development and Technology Founding member and President (since 1999) of the Association of Polish Cartographers She manages significant research projects including 'Methodology and procedures for integration, visualization, generalization and standardization of reference databases' and has authored over 130 publications. Her work with the Lower Silesian Spatial System demonstrates applied expertise in regional geospatial infrastructure. Current laboratory activities center on the Geocentrum facility (building L-1, room 372) where she develops advanced visualization techniques for spatio-temporal datasets using GIS environments. Her team specializes in converting national geospatial registers into actionable environmental conflict analysis tools.

• Signal Processing
• Ionospheric Physics
• Seismology
• +3 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).

Ute HahnView profile

Associate Professor

• Statistics
• Biostatistics
• Neurodegenerative Diseases
• +5 more

Ute Hahn is an Associate Professor at the Department of Mathematics, Aarhus University . Her research bridges Statistics, Biostatistics, and Neuroscience , with a focus on neurodegenerative diseases like ALS and advanced microscopy techniques . Research Interests: Statistical modeling in ALS and genetic research Stochastic geometry and spatio-temporal analysis Super-resolution microscopy (PALM, STED) Medical statistics and clinical epidemiology Functional data analysis and methodological innovations Selected Publications highlight her interdisciplinary work, including studies on ALS mouse models , microscopy artifacts , and spatio-temporal patterns in medical and environmental contexts . Collaborative Projects: Ute is actively involved in the MechanoGeometry research initiative, which began in 2022 and continues into 2023.

Fan BuView profile

Assistant Professor

• Bayesian statistics
• statistical computation
• dynamic and stochastic models
• +16 more

Fan Bu is an Assistant Professor in the Department of Biostatistics at the University of Michigan School of Public Health. Their research bridges Bayesian and computational statistics with applications in public health, observational healthcare studies, computational social science, and sports sciences. PhD in Statistics from Duke University (2021) BS in Mathematics from Peking University (2017) Research focuses on: Spatio-temporal statistics for modeling infectious disease spread Network inference methods for analyzing dynamic contact patterns Distributed learning frameworks for federated data analysis Recent publications address vaccine safety surveillance, HIV transmission dynamics, and epidemic modeling on dynamic networks. Their work combines methodological innovation with real-world applications in health data science and computational social science. Contact: fbu@umich.edu | Office: M4015 SPH II, 1415 Washington Heights, Ann Arbor, MI

Frédéric FlouvatView profile

Professor

• Data Science
• Artificial Intelligence
• Pattern Mining
• +7 more

Frédéric Flouvat is a Full Professor in computer science and data scientist at Aix-Marseille Institute of Technology , conducting research at the Computer Science and Systems Laboratory (LIS - UMR 7020) . As co-leader of the Data Centric AI (DCAI) team in the Data Science department, he focuses on data-centric AI, pattern mining, and spatio-temporal data analysis. His interdisciplinary work spans environmental monitoring, urban dynamics, and educational data mining. His research emphasizes Representation learning in Agent-Based Models Innovative approaches to data-centric AI Expert model integration for pattern discovery Spatio-temporal pattern extraction from complex data Applications to soil erosion, urban growth, and educational outcomes Scientific recognition includes INFORSID 2005 Young Researcher Best Paper Prize Best paper award at EGC'13 He has coordinated pedagogical projects like Development of the FabLab spirit and supervised numerous students across institutions including University of New Caledonia, Grenoble, Paris, Lyon, and Brest. Current research projects include SITI (ANR-funded urban dynamics study) and Descol IA (Carnot Star Institute health benefits research).

• Machine Learning for Visual Computing
• Big Data Visualization and Analytics
• Data Science and High-Performance Computing (HPC)
• +4 more

Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the Indian Institute of Technology Kanpur (IITK) since 2022. He previously held positions at Los Alamos National Laboratory as a Postdoctoral Researcher (2018-2019) and Scientist II (2019-2022). Dr. Dutta earned his Ph.D. and M.S. in Computer Science from The Ohio State University (2011-2018) and a B.Tech in Electronics and Communication Engineering from the West Bengal University of Technology (2005-2009). His research lies at the intersection of Machine Learning , Visual Computing , Big Data Analytics , and High-Performance Computing (HPC) . He focuses on developing scalable solutions for extreme-scale data, such as exascale simulations, social media, IoT, and healthcare. His work emphasizes uncertainty quantification in AI models and interactive visualization techniques. Dr. Dutta’s recent publications highlight his expertise in in situ visualization for climate modeling, implicit neural representations for uncertainty-aware rendering, and statistical sampling for exascale systems. His funded projects include AI-driven data analytics frameworks and deepfake defense mechanisms supported by ISRO, SERB, and C3iHub. Scientific Awards include Best Reviewer (TVCG), Best Paper (ISAV, TopoInVis), and LAAP Award (LANL).