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Holly MunroView profile

Adjunct Assistant Professor

• Forest biometrics
• Disturbance ecology
• Forest entomology
• +3 more

Holly Munro is a Senior Research Scientist (Forest Biometrics and Ecology) at the National Council for Air and Stream Improvement and serves as an Adjunct Assistant Professor in Forest Biometrics Education at the University of Georgia. She holds a Ph.D. in Forestry and Natural Resources from the University of Georgia, an M.S. in Data Science from the same institution, and a B.S. in Biology from the University of North Georgia. Her research focuses on forest biometrics, disturbance ecology, forest entomology, and the application of machine learning to ecological problems. Notable work includes developing predictive models for bark beetle outbreaks and studying the ecological impacts of invasive pests under climate change scenarios. She has contributed to interdisciplinary projects blending data science with traditional ecological methods. Munro’s publications span high-impact journals such as Forest Ecology and Management and Ecological Informatics , with a thematic focus on pest-behavior analysis, climate adaptation strategies, and forest health monitoring. Her work bridges theoretical ecology with practical management solutions for forest ecosystems. No scientific awards are explicitly listed in the provided text. Her advising and grants information is currently unavailable, though her research collaborations suggest active involvement in funded projects. She is affiliated with the Department of Forest Biometrics Education at UGA and contributes to forest management initiatives through her dual roles in academia and industry.

Karl WegmannView profile

Professor

• Geomorphology
• Active Tectonics
• Paleoseismology
• +5 more

Karl Wegmann is a Professor and Associate Head of the Marine, Earth and Atmospheric Sciences (MEAS) department at North Carolina State University. He holds a faculty fellowship at the Center for Geospatial Analytics. His research focuses on Earth surface processes, integrating geospatial analysis with field investigations to study landscape responses to tectonic and climatic forces. Key areas include geomorphology, active tectonics, paleoseismology, and geoarchaeology. Education: Ph.D. (2008, Lehigh University), M.S. (1999, University of New Mexico), B.A. (1996, Whitman College). Research emphasizes landslide dynamics, paleoclimatic reconstructions, and planetary geoarchaeology. Notable projects include studies in Mongolia, Greece, and the Pacific Northwest. He teaches courses in geology, natural hazards, and field geology. His work bridges disciplines, applying remote sensing and machine learning to geohazard assessment and cultural heritage preservation. Scientific achievements include the 2023 NCSU Outstanding Teaching Award. Ongoing research explores lunar anthropocene frameworks and Martian surface processes. Collaborative projects involve students in global fieldwork and innovative geospatial methodologies.

• Resilience in 5G
• Distributed Artificial Intelligence (Edge AI)
• Information assessment
• +3 more

Tobias Meuser is a Researcher at the Multimedia Communications Lab of Technische Universität Darmstadt, leading the "Adaptive Communication Systems" group since 2020. He holds a PhD (2019) focused on vehicular network data management and has been a central figure in the third phase of the Collaborative Research Center (CRC) MAKI as a principal investigator in subproject B1. His work emphasizes resilient 5G networks, edge AI, and distributed systems. Education: B.Sc. Business Informatics (Fernuniversität Hagen) M.Sc. Informatics (TU Darmstadt) Research Interests: Resilience in 5G and beyond Edge AI and distributed machine learning Information assessment in vehicular networks Collaborative perception systems Hardware acceleration for network functions Key Projects: Principal Investigator in CRC MAKI's B1 (Monitoring and Analysis) Collaborations with Opel (cooperative maneuvering) and Deutsche Bahn (5G resilience) Labs/Teams: Head of Adaptive Communication Systems group at Multimedia Communications Lab Member of Distributed Sensing Systems group (2016–2020)

Evaggelia PitouraView profile

Professor

• Data Management
• Distributed Databases
• Database Systems
• +7 more

Evaggelia Pitoura is a Professor in the Department of Computer Engineering and Informatics at the University of Ioannina, Greece, and a Lead Researcher at the Archimedes Research Unit, ATHENA Research Center. Her work spans data management, distributed systems, and mobile computing, with a strong focus on database personalization and diversity. Her research interests include data diversity, contextual preferences, XML data management, peer-to-peer systems, and mobile computing . She has pioneered work in result diversification, preference-aware querying, and distributed data indexing, publishing extensively in top venues such as PVLDB, ICDE, SIGMOD, and IEEE TPDS. The recent publications highlight a consistent trend in personalized, context-aware database systems , with emphasis on improving user experience through intelligent query processing, recommendation, and efficient data organization in distributed environments. Best Paper Award, SIGMOD 1999 Best Paper Award, PVLDB 2013 She has advised several PhD students, including Georgia Koloniari, Kostas Stefanidis, Marina Drosou, and Konstantinos Semertzidis, who now hold positions in academia and industry. She teaches courses in Databases and Information Retrieval and actively contributes to the academic community through program committee roles in major conferences like ICDE, VLDB, SIGMOD, and WWW. She leads the Distributed Management of Data Group , fostering innovation in scalable and intelligent data systems.

Minsu ParkView profile

Assistant Professor

• Computational Social Science
• Human-centered Data Science
• Cultural Consumption
• +9 more

Minsu Park is an Assistant Professor of Social Research and Public Policy at New York University Abu Dhabi (NYUAD), with affiliate status at the Center for Data Science at New York University (NYU). He holds a PhD in Information Science from Cornell University, where he was advised by Michael W. Macy and Mor Naaman. His academic work bridges computational techniques and social science theory, focusing on cultural consumption, social networks, and human-centered data science. PhD in Information Science, Cornell University His research centers on understanding how individuals form cultural preferences through social and psychological mechanisms, particularly in domains like music, food, fashion, and science. He leverages large-scale digital trace data—including social media, streaming platforms, and physiological signals from smart devices—to model behavior. His interdisciplinary approach draws from sociology, social computing, and data science, aiming to uncover both individual and global patterns in cultural dynamics. Key themes include variety-seeking behaviors, affective preferences, and the interplay between social position and taste. The 15 most recent publications reflect a strong trend toward using computational methods to explore sociocultural phenomena. Topics span affective preference rhythms in music, testing sociological theories like cultural omnivory, imputing user attributes from digital behavior, and enhancing model interpretability. These works are published in high-impact venues such as Nature Human Behaviour and ICWSM, indicating recognition in both computer and social sciences. Minsu Park is deeply committed to responsible data science, addressing issues like algorithmic bias, transparency, fairness, data privacy, and research ethics. He emphasizes mixed-methods approaches and critical reflection on data generation and usage. Published in Nature Human Behaviour Presented at ICWSM Focus on ethical and interpretable AI Advocate for reproducibility and data curation He mentors students through capstone projects in computer science and teaches courses such as Human-Centered Data Science and Textual Analysis for the Social Sciences. While specific grants are not detailed in the text, his research program suggests involvement in interdisciplinary, data-intensive projects likely supported by academic or scientific funding bodies. His technical expertise includes Python, R, machine learning frameworks (TensorFlow, Keras), and tools like Gephi and AWS. He leads or contributes to research initiatives involving smartwatch-based data collection, surname-based ethnicity matching, and global music consumption analysis, as evidenced by his public GitHub repositories. These projects highlight his commitment to open science and collaborative research.

Milos NikolicView profile

Lecturer

• Databases
• Data Management
• Incremental Computation
• +7 more

Milos Nikolic is a Lecturer in Database Systems at the School of Informatics, University of Edinburgh. He is a member of the Database Group and the Laboratory for Foundations of Computer Science. Prior to joining Edinburgh, he was a Departmental Lecturer in the Department of Computer Science at the University of Oxford. He holds a PhD in Computer Science from EPFL. Research Interests: Milos Nikolic's research focuses on databases and large-scale data management, with emphasis on incremental computation, in-database learning, stream processing, and query compilation. His work explores how complex analytical queries—such as SQL, linear algebra, and machine learning tasks—can be efficiently maintained and evaluated in dynamic and distributed environments. He develops novel techniques in query optimization and compilation to enable real-time analytics over evolving data. His recent publications demonstrate a strong trend in dynamic query evaluation, particularly around conjunctive and hierarchical queries under updates, incremental view maintenance (e.g., F-IVM), and scalable processing of nested and biomedical data. His work often leverages theoretical foundations to achieve worst-case optimal performance, grounded in conjectures like the Online Matrix-Vector Multiplication (OMv) hypothesis. Scientific Awards: Best Paper Award, ICDT 2019 Advising and Grants: Milos is actively seeking PhD students to work on data management topics. While no formal list of advisees is provided, he collaborates extensively with researchers such as Dan Olteanu, Ahmet Kara, and Haozhe Zhang. His projects, including Adaptive Query Processing , Incremental Maintenance of Complex Analytics , and Declarative Data Pipelines (industry-supported), suggest ongoing grant and industry collaborations. Labs and Teams: He is a key member of the Database Group and the Laboratory for Foundations of Computer Science at the University of Edinburgh, contributing to cutting-edge research in foundational and applied database systems.

• Distributed Computing
• Federated Learning
• Cloud Computing
• +12 more

Salman Zubair Toor is a researcher at Uppsala University, Sweden, specializing in distributed computing, federated learning, and cloud/edge infrastructure optimization. His work spans resource scheduling, data streaming, and secure anomaly detection.

Dan SteinbergView profile

Academic

• Probabilistic Machine Learning
• Variational Inference
• Bayesian Deep Learning
• +7 more

Dan Steinberg is a senior research scientist and team leader of the Decisions & Statistical Learning team at CSIRO Data61 in Canberra, Australia. His expertise lies in probabilistic machine learning, variational inference, Bayesian deep learning, causal inference, and their application to domains spanning synthetic biology, geospatial analytics, and algorithmic fairness. Education PhD in Computer Vision / Machine Learning (2013) – University of Sydney, Australian Centre for Field Robotics Bachelor of Engineering (Mechatronics, First-Class Honours) – University of Sydney (2008) Bachelor of Commerce (Finance) – University of Sydney (2008) Research Interests Steinberg’s core research agenda revolves around building scalable probabilistic models that can learn efficiently from limited or noisy data and provide principled uncertainty estimates. Key themes include: Variational Inference & Bayesian Deep Learning: developing lightweight yet powerful algorithms for approximate posterior inference in complex models (e.g., Aboleth, Revrand). Active Learning & Experimental Design: creating methods that decide which experiments or measurements will maximise information gain, with recent focus on in-silico protein engineering via Variational Search Distributions (VSD). Causal Inference: leveraging machine-learning tools to perform robust observational causal studies for evidence-based policy, including work on youth well-being and academic outcomes. Algorithmic Fairness: translating normative notions of equity into quantifiable objectives for regression-based decision systems. Large-scale Spatial Analytics: Landshark—an open-source TensorFlow toolkit for supervised learning on massive geospatial raster datasets. Notable Software & Tools Aboleth: A minimal-overhead TensorFlow framework for Bayesian deep learning. Landshark: Command-line tools for large-scale spatial inference. Revrand: Scalable Bayesian generalised linear models with non-conjugate likelihoods. libcluster: Extensible C++ library for hierarchical Bayesian clustering. Scientific Awards Oral Presentation Award – ICML 2025 Workshop on Scaling up Intervention Models (SIMS) Oral Presentation Award – NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty (BDU) Oral Presentation Award – NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning Spotlight Paper Award – NeurIPS 2014 (Extended and Unscented Gaussian Processes) Research Team & Collaborations As Team Leader – Decisions & Statistical Learning at CSIRO Data61, Steinberg directs a multi-disciplinary group that partners with government agencies (e.g., Jobs and Skills Australia, Australian Institute of Health and Welfare) and industry to deploy machine-learning solutions at scale. He has previously held roles as Principal Researcher at Gradient Institute (2019-2023), Senior Research Engineer at CSIRO Data61 (2016-2019), Researcher at NICTA (2013-2016), and Research Associate at the University of Sydney (2012-2013).

Mohamed AllaliView profile

Associate Professor

• Data Engineering
• Machine Learning
• Climate Data Analysis
• +3 more

Mohamed Allali is an Associate Professor at Chapman University, affiliated with both the Fowler School of Engineering (Department of Electrical Engineering and Computer Science) and Schmid College of Science and Technology (Department of Mathematics). His research spans data engineering, machine learning, climate informatics, and mathematical education. He has contributed to constraint-based intelligent tutoring systems, data drift detection using KL divergence, and geospatial analysis for environmental monitoring. Education: University of Oklahoma (BS, MA, PhD). His recent work focuses on data distribution divergence, climate modeling for sea turtle habitats, and neural network applications in medical imaging. He has collaborated extensively on drought indices, satellite data validation, and educational technologies. His publications highlight expertise in statistical learning, computational methods, and interdisciplinary environmental applications.

Miguel Sales DiasView profile

Researcher

• Machine Learning
• Smart Cities
• Immersive Technologies
• +6 more

Miguel Sales Dias is affiliated with ISCTE - Instituto Universitário de Lisboa, specifically the Iscte Business School. His research spans multiple domains, including Machine Learning , Immersive Technologies , and Urban Mobility . His work focuses on: Developing AI-driven systems for energy consumption optimization in public buildings Creating co-design interfaces for housing customization Exploring mHealth applications for cardiac diagnostics Advancing virtual reality for education and visitor experiences Recent publications demonstrate expertise in smart city solutions , bioinformatics , and human-computer interaction . While specific awards or educational background weren't explicitly mentioned, his extensive publication record indicates significant contributions to interdisciplinary research.

• High-performance computing
• Graph algorithms
• Graph machine learning
• +1 more

Ariful Azad serves as an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University, where he leads research at the intersection of high-performance computing and graph analytics. His work focuses on developing scalable algorithms for graph machine learning with applications in bioinformatics and security informatics. Educational Background: Ph.D. in Computer Science, Purdue University (2014) B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology (2006) Research Focus: Dr. Azad specializes in high-performance graph algorithms , particularly for distributed-memory systems. His pioneering work includes the Combinatorial BLAS library and novel approaches for graph neural networks (GNNs), with emphasis on explainability through Shapley values and optimization of sparse matrix operations. His bioinformatics research tackles large-scale metagenomics challenges through projects like Exabiome. Publication Trends: Recent publications (2023-2025) reveal three dominant themes: (1) Scalable GNN explanation frameworks using distributed Shapley values, (2) High-performance sparse linear algebra for graph embeddings and knowledge graphs, and (3) Bioinformatics applications in metagenomics and network alignment. His work consistently bridges theoretical algorithm development with practical implementations for exascale systems. Scientific Recognition: NSF CAREER Award (2024) for foundational contributions to scalable graph algorithms Indiana University Trustee's Teaching Award (2024) U.S. Department of Energy Early Career Award (2021) Research Leadership: As principal investigator for multiple federal grants, Dr. Azad directs projects advancing graph analytics at extreme scales. His work on Weapons of Mass Destruction knowledge graphs demonstrates applied security research, while Exabiome represents significant contributions to computational biology. He actively develops open-source tools like PLANETALIGN for network analysis benchmarking, fostering reproducibility in computational science.

• freshwater ecology
• light pollution
• ecophysiology
• +3 more

Dr. Franz Hölker is a Senior Scientist and Research Group Leader at the Leibniz-Institute of Freshwater Ecology and Inland Fisheries (IGB Berlin) and holds the position of Associate Professor (Privatdozent) in Zoology at the Department of Biology, Chemistry and Pharmacy, Freie Universität Berlin. Since 2022, he has served as Programme Area Speaker for 'Aquatic Biodiversity in the Anthropocene' at IGB Berlin, leading research on ecological responses to anthropogenic pressures. Leibniz-Institute of Freshwater Ecology and Inland Fisheries (IGB Berlin): Senior Scientist, Research Group Leader since 2008 Freie Universität Berlin: Associate Professor (Privatdozent) for Zoology since 2009 2012-2017: Deputy Head of Department Ecohydrology, IGB Dr. Hölker earned his Diploma in Biology from the University of Hamburg in 1992, completed his doctorate there in 1999 on fish bioenergetics in eutrophic lakes, and achieved his Habilitation at Humboldt-Universität zu Berlin in 2007 with research bridging ecology, behavior, physiology, and modeling in aquatic food webs. Dr. Hölker's research primarily focuses on freshwater ecology with particular expertise in light pollution, ecophysiology, night ecology, ecological modeling, and citizen science. His work investigates how artificial light at night (ALAN) affects aquatic ecosystems, including impacts on fish physiology, insect behavior, and broader ecological community dynamics. He has pioneered research on the ecological consequences of skyglow and developed methodologies for measuring and assessing ecological light pollution. His research group at IGB Berlin employs a combination of field measurements, laboratory experiments, and modeling approaches to understand how light pollution alters species interactions, community composition, and ecosystem functioning in aquatic environments. His extensive publication record demonstrates a consistent focus on understanding anthropogenic impacts on freshwater systems. Recent work shows increasing emphasis on interdisciplinary approaches, combining ecological field studies with modeling techniques to address complex environmental challenges. His research spans from organism-level physiological responses to ecosystem-level consequences of light pollution, with growing attention to conservation applications and citizen science engagement. The trend in his publications reveals expanding scope from fish ecology to broader ecosystem impacts, with increasing international collaboration and policy relevance. Dr. Hölker has led significant research projects examining the impacts of artificial light at night on freshwater ecosystems, often involving international collaborations across Europe. His work has contributed to policy discussions on sustainable lighting practices through participation in projects like the 'White Paper Citizen Science Strategy 2030 for Germany' and collaborations with lighting professionals to develop ecologically sustainable outdoor lighting guidelines. He leads a research team investigating the ecological impacts of light pollution, with particular focus on how artificial lighting affects nocturnal processes in freshwater ecosystems. His group has developed innovative methodologies for measuring ecological light pollution and has conducted extensive field and laboratory studies to document the impacts of different light spectra and intensities on aquatic organisms. The team's work has significant implications for conservation biology, particularly regarding the protection of nocturnal biodiversity in increasingly illuminated landscapes.

• Graph Theory
• Data Mining
• Network Analysis
• +2 more

Aristides Gionis is a Research Professor Fellow at Aalto University specializing in advanced network analysis and data mining. His work bridges theoretical graph algorithms with practical applications in dynamic and signed networks. His research focuses on graph theory and temporal network analysis , particularly in discovering dense subgraphs, community structures, and event patterns in evolving networks. Key methodologies include contrastive subgraph mining for explainable AI in brain networks and polarization detection in social systems. His publication trends reveal consistent contributions to top-tier venues like KDD, PAKDD, and ECML PKDD, with emphasis on algorithmic solutions for network segmentation, correlation mining, and interpretable classification. Primary application domains include social networks, neuroscience, and temporal data streams. As a Fellow at Aalto University, he leads research in computational network science without explicit departmental affiliation stated in available sources.

Pedro Trapero FernandezView profile

Researcher

• Ancient History
• Landscape Archaeology
• Geographic Information Systems (GIS) in Archaeology
• +3 more

Dr. Pedro Trapero Fernandez is a Postdoctoral Researcher at the University of Cádiz, affiliated with the Department of History, Geography and Philosophy and the Institute of Viticulture and Agri-Food Research (IVAGRO). He leads the HUM240 Historical Heritage of Andalusia in Antiquity research group and specializes in Ancient History, with particular focus on Roman viticulture, landscape archaeology, and geospatial analysis. PhD in Ancient History and Archaeology (2020) with thesis on Roman Viticulture in the Hasta Regia and the Guadalquivir Estuary Research expertise in non-invasive techniques: GIS, drones, ground-penetrating radar, magnetometry Active academic collaborations with international institutions including FORTH (Greece) His research integrates classical agronomic texts like Columella with modern geospatial technologies to reconstruct ancient agricultural practices and mobility networks. Key projects include Roman road network analysis, viticulture modeling, and archaeological applications of GIS in Spain and Portugal. He has contributed to coastal and inland connectivity studies in Roman and Medieval contexts. Scientific publications demonstrate technical proficiency in geophysical prospecting and historical modeling. Notable works include: GIS applications for Roman property surveying, multidisciplinary analysis of wine production, and innovative methodologies for studying ancient transportation networks. As an educator, he has taught at undergraduate and master's levels through predoctoral contracts and developed flipped classroom methodologies for humanities teaching. Current affiliations include the Agustín de Horozco Seminar on Economic Studies and multiple international research partnerships.

• Machine Learning
• Information Fusion
• Data Clustering
• +3 more

Julien Ah-Pine is a lecturer at Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS) under Université Clermont Auvergne , with affiliations at Institut national polytechnique Clermont Auvergne and École des Mines de Saint-Étienne . He also holds a Researcher position at CNRS. Research Interests His work spans machine learning , information fusion , aggregation functions , and multi-criteria decision support , with a focus on complex data types like graphs , functional data , and multi-view datasets . Recent publications emphasize anomaly detection in spectral data streams , online learning , and interpretable AI for industrial applications. Selected Publications 2025 work on OnlineBootKNN introduces a novel framework for real-time spectral anomaly detection, while 2024 research explores multiple kernel methods in functional data classification. Earlier studies cover graph-based clustering , relational data mining , and linguistic network models for NLP tasks. Laboratory & Collaborations Works within LIMOS laboratory at Université Clermont Auvergne, collaborating with institutions like Mines Saint-Étienne and CNRS. Key partnerships include Nicolas Rojas Varela and Engelbert Mephu Nguifo on data stream analysis projects.