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Arthur J. Lembo, Jr.View profile

Professor

• Geographic Information Systems (GIS)
• Geo-processing and spatial analysis
• Enterprise GIS implementation
• +7 more

Arthur J. Lembo, Jr. is Professor of Geography and Geoscience in the Henson School of Science and Technology at Salisbury University, where he has taught since 2007. He also serves as Technical Director of the Eastern Shore Regional GIS Cooperative, leading regional geospatial initiatives and enterprise GIS projects. Education B.S. Geography, State University of New York, College at Oneonta (1987) M.A. Geography, State University of New York at Buffalo (1989) Ph.D. Environmental Resource Engineering, State University of New York, College of Environmental Science and Forestry (1997) Research Interests Dr. Lembo’s research centers on the innovative application of advanced information technologies to geographic problem-solving. His work explores high-performance GIS through massively-parallel algorithms executed on consumer-grade gaming GPUs, enterprise-scale spatial database design, and the integration of smartphone sensors into real-time mapping workflows. Additional emphases include spatial decision support for post-disaster infrastructure assessment, crime analysis, and sustainable local food systems. Selected Research Sponsors National Science Foundation (multiple awards) W. K. Kellogg Foundation Maryland Governor’s Office of Crime Control and Prevention Maryland Department of Natural Resources USDA and NASA Scientific Awards 2018 State of Maryland Board of Regents Award for Teaching Excellence 2006 SUNY Chancellor’s Award for Excellence in Teaching 2005 NACTA Teacher of Merit 2003 CALS Faculty Innovation in Teaching Award, Cornell University 2002 & 2003 Master Teacher recognition, University of Mississippi & NASA Teaching & Outreach Dr. Lembo teaches core GIS programming and spatial-analysis courses (GEOG 204, 415, 419, 435, 619) and maintains an active extension program that provides GIS solutions for municipalities, first responders, and environmental NGOs such as The Nature Conservancy and the City of Havre de Grace. Lab & Teams He directs the Eastern Shore Regional GIS Cooperative, a university-based center that supports multi-agency geospatial data sharing and application development across Maryland’s Eastern Shore.

Bram VanschoenwinkelView profile

Associate Professor

• Habitat Selection
• Spatial Ecology
• Island Biogeography
• +5 more

Bram Vanschoenwinkel is an Associate Professor in the Department of Ecology, Evolution and Genetics within the Faculty of Sciences at Vrije Universiteit Brussel (VUB), where he founded the Community Ecology Lab in 2014. His research integrates experimental approaches, field surveys, and global biodiversity databases to investigate species distributions and abundances across spatial scales, primarily using animal model organisms. His primary research interests span Habitat Selection (particularly in mosquitoes), Spatial Ecology and Biogeography , Island Ecology using ancient terrestrial islands as model systems, and Applied Ecology focused on cacao pollination. His fingerprint analysis reveals strong expertise in habitat dynamics (100%), rock pool ecology (97%), and aquatic ecosystems (65-70%). Recent research output shows consistent productivity with 114 publications, including significant 2024-2025 studies on cacao phenology, waterbird foraging, and mangrove dispersal. His work demonstrates strong interdisciplinary connections between freshwater ecology, tropical agriculture, and coastal conservation. BRGEOZ459: Water quality assessment in Brussels (2025-2026) BASGO41: Fundamental research group funding (2025-2029) FWOSB192: Laser-based biomonitoring of airborne animals (2024-2029) FWOICA2: IUCN Mangrove Specialist Group (2023-2025) He actively mentors students including James Hagan (2018 Master's thesis), supervises 26 research projects, and leads initiatives like the Community Ecology Lab (www.insularecology.com) and Cacao Pollination Project (www.cacaopollination.com). His work has generated 3,703 citations and an h-index of 33.

Caglar KoyluView profile

Associate Professor

• GIScience
• Cartography
• Geovisualization
• +7 more

Caglar Koylu is an Associate Professor in the Department of Geographical and Sustainability Sciences at the University of Iowa's School of Earth, Environment, and Sustainability. He directs the Geo-Social Lab where he leads research at the intersection of GIScience, network science, cartography, and visualization with applications in human mobility, migration, population geography, public health, and social vulnerability. His educational background includes a PhD from the University of South Carolina. He teaches foundational courses in GIS, geospatial programming, geographic visualization, and spatial analysis. His research program focuses on developing innovative computational and visual tools to analyze massive geospatial data and geo-social networks. Koylu's research interests span GIScience, cartography, visualization, spatiotemporal analysis, human mobility and migration, and population-scale kinship networks. His work combines network science, machine learning, and GeoAI approaches to understand complex spatial-social systems. Recent projects include analyzing historical migration patterns using population-scale family trees and developing tools for visualizing spatial flows. His publications reveal a strong focus on methodological innovation in flow mapping, spatial interaction analysis, and geovisual analytics, with applications spanning migration studies, public health, social media analysis, and disaster management. He has developed FlowMapper.org, a web-based tool for visualizing and analyzing flows across space-time. Among his notable recognitions, Koylu was elected Vice President of the Cartography and Geographic Information Society (CaGIS) in 2023. His students have received multiple awards including the CaGIS Doctoral Scholarship Award. He currently advises several graduate students including Maryam Torkashvand (PhD), Jinyi Cai (PhD), and Loretta Nwajiaku (MA). His lab has successfully graduated PhD students including Hoeyun Kwon (2023) and Geng Tian (MA, 2021). Koylu secures substantial research funding, currently serving as Principal Investigator on an NSF grant ($477,734) for population-scale kinship networks research and as Co-PI on an NIH grant ($1.18 million) for cancer risk mapping. His lab provides research experiences for undergraduate and high school students through the Secondary Student Training Program.

• Astrophysics
• Cosmology
• Radio Astronomy
• +2 more

Nceba Mhlahlo is a Lecturer in the School of Physics at the University of the Witwatersrand, where he conducts research in astrophysics and cosmology. He holds a BSc, BScHons, MSc, and PhD from the University of Cape Town. His contact information includes phone: 0117176831 and email: Nceba.Mhlahlo@wits.ac.za . His ORCID profile is accessible via 0000-0001-7435-2269 . Dr. Mhlahlo's research focuses on several interconnected domains: Galaxy clusters : Investigating shock acceleration, non-thermal pressure, and merger dynamics using multiwavelength data. Radio galaxies : Discovering and characterizing giant radio sources with instruments like MeerKAT and KAT-7. Cosmological fluid dynamics : Modeling interactions of dust fluids in expanding universes. Telescope surveys : Leading observational programs including the MeerKAT Galaxy Cluster Legacy Survey. His 15 publications (1999–2024) reveal thematic shifts: early nuclear physics (1999) and cataclysmic variables (2004–2007), transitioning to radio astronomy and galaxy clusters (2013–present). Recent works emphasize radio telescope discoveries, while earlier studies centered on stellar binaries. No awards, grants, or student mentoring activities are documented.

Michele TomaiuoloView profile

Associate Professor

• Social Media Analysis
• Peer-to-Peer Networks
• Security and Trust Management
• +5 more

Michele Tomaiuolo is an Associate Professor at the Department of Engineering and Architecture, University of Parma, Italy. He has been actively teaching undergraduate and graduate courses in computer science and engineering since at least 2014, with current teaching assignments for the 2025/2026 academic year. Dr. Tomaiuolo holds a Master's degree in Computer Engineering and a PhD in Information Technology, both from the University of Parma. His educational background has provided the foundation for his extensive career in academic research and teaching in the field of computer science. Professor Tomaiuolo's research spans several interconnected domains within computer science and information systems. His primary focus is on social media analysis and peer-to-peer social networks, with particular attention to security and trust management. His work also encompasses multi-agent systems, semantic web technologies, rule-based systems, and peer-to-peer networking architectures. More recently, his research has expanded into sentiment analysis applications for digital libraries and educational contexts, as well as fine-grained agent-based modeling for epidemiological simulations. His interdisciplinary approach bridges theoretical computer science with practical applications in information systems and social computing. An analysis of Professor Tomaiuolo's recent publications (2020-2024) reveals several key trends in his research. There is a strong emphasis on social network analysis, particularly in the context of sentiment analysis and user behavior. His work frequently employs agent-based modeling techniques to address complex problems ranging from pandemic spread prediction to digital library user experience. The integration of artificial intelligence methods with traditional computer science approaches is evident across his publications, with applications spanning digital libraries, educational technology, and social media platforms. His research demonstrates a consistent focus on practical implementations of theoretical concepts, often addressing real-world challenges through computational solutions. Professor Tomaiuolo has participated in various research projects, including the EU-funded @lis TechNet, Agentcities, Collaborator, and Comma projects, and the nationally funded Anemone project. These collaborative efforts have provided the foundation for much of his published research. His teaching portfolio is extensive, covering fundamental computer science concepts, object-oriented programming, software engineering, computer networks, mobile code, and security. Current courses for the 2025/2026 academic year include Fundamentals of Computer Science, Computer Science and Programming Laboratory, and Paradigms and Languages for Data Analysis across various engineering programs. Professor Tomaiuolo is actively involved in the academic community, serving as a reference professor and tutor for undergraduate engineering programs at the University of Parma. His work with the CSBNO Consortium demonstrates his commitment to applying computational methods to enhance library services and user experiences.

Rafael da Silva de SouzaView profile

Associate Professor

• Astrostatistics
• Machine Learning
• Extragalactic Astrophysics
• +3 more

Rafael da Silva de Souza holds dual academic appointments as Associate Professor at the University of North Carolina at Chapel Hill (2024-2026) and primary affiliation with the University of Hertfordshire's School of Physics, Engineering & Computer Science. He chairs the Cosmostatistics Initiative and contributes to the Centre for Astrophysics Research (CAR), with prior positions at Shanghai Astronomical Observatory, University of North Carolina at Chapel Hill, Eotvos Lorand University, Korean Astronomy and Space Science Institute, and Kavli IPMU. His educational background includes a PhD in Astrophysics from Universidade de Sao Paulo (2009), specializing in the Origin of Cosmological Magnetic Fields. Research interests focus on statistical methodologies in astrophysics, with key areas including: Astrostatistics and Bayesian modeling Machine learning applications for astronomical data Stellar cluster dynamics (open/globular clusters) Cosmic web structure analysis Extragalactic surveys and galaxy evolution Recent publications demonstrate strong integration of statistical innovation with observational astrophysics, particularly in galaxy morphology classification, supernova cosmology, and stellar evolution studies. His work increasingly leverages machine learning for large-scale survey data interpretation across projects like Galaxy Zoo DECaLS and BASS/MzLS. Key recognition includes the Prose Award for his Cambridge University Press book Bayesian Models for Astrophysical Data . He previously served as Vice-President of the International Astrostatistics Association. Research leadership includes principal investigator roles for the MESCAL project (Multidimensional Exploration of Stellar Clusters via Automated Learning, 2020-2024) and MTA fellowship (2014-2016), with total research output exceeding 96 publications including 85 journal articles, 3 conference contributions, and a book. His laboratory activities center around the Cosmostatistics Initiative, developing open-source tools for statistical astrophysics and fostering international collaboration in astrostatistics methodology.

Ihab Francis IlyasView profile

Professor

• Data Science
• Data Management
• Machine Learning for Data Quality
• +4 more

Ihab Francis Ilyas is a Professor at the University of Waterloo , affiliated with the Cheriton School of Computer Science . He currently holds the Thomson Reuters Research Chair in Data Quality and is on leave while serving as a Distinguished Engineer, Proactive Intelligence at Apple Inc. . He has co-founded two successful startups— Inductiv (acquired by Apple) and Tamr —and is a Fellow of the Royal Society of Canada , IEEE Fellow , and ACM Fellow . Research Interests : AI for Data Quality and Curation Knowledge Graphs Large-Scale Data Integration Information Extraction Managing Uncertain Data Data Cleaning Error Detection and Repair Probabilistic and Uncertain Data Management Scientific Awards and Recognitions : C.C. Gotlieb Computer Award, 2024 IEEE Fellow, 2021 ACM Fellow, 2020 NSERC-Thomson Reuters Industrial Research Chair, 2018 Google Faculty Award, 2014 Ontario Early Researcher Award, 2008 IBM CAS Faculty Fellow, 2006–2010 Taha Hussein Medal (Egyptian Ministry of Education), 1990 Leadership and Service : Board of Trustees, VLDB Endowment (2016–2021) Vice Chair, ACM SIGMOD (2016–2021) Co-founder, Inductiv (acquired by Apple) and Tamr Co-author of the leading text Data Cleaning (ACM Books) Lead developer of the HoloClean open-source data repair system Contributor to Saga , a next-generation knowledge construction platform at Apple Publications and Trends : His recent research focuses on AI-driven data cleaning, knowledge graph construction, and scalable data integration systems. Collaborative works span probabilistic inference, differentially private data synthesis, error detection, and HTAP workloads. His publications appear in top venues like SIGMOD , VLDB , and ICDE .

Juhi KulshresthaView profile

Assistant Professor

• Computational Social Science
• Social Computing
• Misinformation
• +3 more

Juhi Kulshrestha serves as an Assistant Professor in the Department of Computer Science at Aalto University, Finland, specializing in Computational Social Science and Social Computing with a focus on digital ethics, societal impacts of technology, and policy implications. Her research addresses critical challenges at the intersection of human behavior and digital systems. Her scholarly work investigates misinformation propagation, algorithmic bias in search/social media, and AI's societal consequences through interdisciplinary approaches. Key research themes include affect-driven information seeking, Holocaust representation in AI-generated content, digital divides in AI adoption, and cross-national vaccine misinformation dynamics. She employs large-scale data analysis combining computational methods with social science theories. Recent publications (2022-2025) reveal a strong emphasis on real-world societal issues: analyzing ChatGPT user demographics in Germany, examining visual Holocaust representations across search engines, and studying conspiracy theory ecosystems (e.g., flat earth/9-11 theories). Her collaborative work spans public health, political science, and psychology, frequently appearing in top venues like PLOS One and New Media and Society. Kulshrestha leads the Digital Ethics, Society and Policy (Digital-ESP) research group, which examines ethical frameworks and policy interventions for digital technologies. No information regarding student supervision or research grants is provided in the source material.

• Fog Computing
• Edge Computing
• Distributed Systems
• +5 more

Claudia Canali is an Associate Professor at the Department of Engineering "Enzo Ferrari" within the University of Modena and Reggio Emilia. Her work focuses on distributed systems, edge and fog computing, and gender equality in academia. She has extensively researched optimization algorithms for micro-service placement, load balancing, and resource allocation in fog and edge computing environments. Her publications address challenges such as latency reduction, energy efficiency, and network congestion in smart city applications. Recent studies include the development of next-generation collaborative research platforms like NextPyter, containerized Jupyter environments, and simulation tools such as ConSUMO. She also contributes to initiatives like the "Digital Girls" summer camp to reduce gender gaps in computer science education. Her peer-reviewed articles span theoretical models validated through simulation, practical implementations, and interdisciplinary studies on organizational behavior. Her work integrates technical solutions with social impact, particularly in multidisciplinary contexts.

• Financial Big Data Analytics
• Machine Learning in Finance
• Social Media and Mobile Trading Platform
• +7 more

Ning Wang serves as a Senior Research Fellow in Data Science at the Mathematical Institute, University of Oxford, where he is based at the Oxford-NIE financial Big Data Lab. He concurrently holds a Research Associate position at the Oxford Internet Institute. His research applies data science methodologies to complex social and economic challenges, with specialized focus on financial systems and digital behavior analysis. Dr. Wang's research portfolio centers on Financial Big Data Analytics and Machine Learning applications in finance, examining social media dynamics within mobile trading platforms. He investigates online sentiment's impact on financial markets, trading behavior patterns, and behavioral finance phenomena. His work extends into social computing frameworks, advanced data mining techniques, and comprehensive social network analysis, creating interdisciplinary bridges between computational methods and socioeconomic systems. Analysis of his publication trajectory reveals consistent innovation across high-impact domains. During the COVID-19 pandemic, he contributed critical epidemiological modeling and policy evaluation research. His scholarly output spans blockchain consensus mechanisms for financial infrastructure, open government data utilization patterns, and biomedical diagnostics using Raman spectroscopy. This diverse body of work demonstrates exceptional versatility in applying data-driven approaches to finance, public health crises, and digital governance challenges. Dr. Wang actively contributes to the Oxford-NIE financial Big Data Lab and Oxford Internet Institute research ecosystems. His academic leadership includes serving as program chair for the 14th IEEE Conference on Computer and Information Technology (CIT2014), Co-Chair for the 2014 IEEE Conference on Cyber, Physical and Social Computing (CPSCom2014), and guest editor for a Policy and Internet journal special issue on Chinese Web Data. He maintains active roles in conference organization and scholarly review processes.

• Dark matter
• Cosmology
• Gravitational lensing
• +6 more

Professor Richard Massey is a faculty member in the Department of Physics and the Institute for Computational Cosmology at Durham University. He is a leading researcher in dark matter, cosmology, and gravitational lensing, with significant contributions to astronomical instrumentation and computational methods. His work spans theoretical modeling, observational analysis, and instrument development for space and stratospheric platforms. Professor Massey's research focuses on the nature of dark matter through gravitational lensing techniques, particularly using strong and weak lensing to map dark matter distributions in galaxy clusters and individual galaxies. He has pioneered the use of shapelets - a mathematical technique for image analysis - which has become influential in weak lensing measurements. His research extends to computational cosmology, galaxy evolution, and the development of balloon-borne telescopes like SuperBIT that provide near-space quality imaging at a fraction of the cost. Analysis of his recent publications shows a strong focus on leveraging cutting-edge observational capabilities, particularly the James Webb Space Telescope (JWST) and the Euclid mission. His work bridges theoretical cosmology with observational data, with significant contributions to understanding dark matter substructure, galaxy evolution across cosmic time, and the development of novel analysis techniques for gravitational lensing. The COSMOS-Web survey and Euclid mission represent major collaborative efforts where his expertise in lensing is crucial. Professor Massey actively supervises postgraduate research students including Nency Patel and Zane Lentz. His work involves substantial collaboration across international teams, particularly on large projects like Euclid, COSMOS-Web, and SuperBIT. These collaborations represent significant research funding and resources, though specific grant details are not provided in the available information. He is a key member of the Institute for Computational Cosmology at Durham University and plays important roles in several major astronomical projects. His SuperBIT project demonstrates innovative approaches to astronomical instrumentation, using stratospheric balloon platforms to achieve near-space quality observations. This work represents an important alternative pathway for high-resolution astronomical imaging that complements traditional space-based and ground-based observatories.

Srijan KumarView profile

Assistant Professor

• AI for Security
• Security for AI
• Graph Learning
• +7 more

Srijan Kumar is an Assistant Professor in the Computer Science and Engineering department at the College of Computing, Georgia Institute of Technology. His research focuses on developing AI and data mining methods to enhance web safety and security, with particular expertise in graph and multimodal learning for detecting malicious actors and harmful content. Dr. Kumar earned his Bachelor's degree from the Indian Institute of Technology at Kharagpur, followed by his Master's and Ph.D. from the University of Maryland, College Park. Prior to joining Georgia Tech, he was a visiting researcher at Google AI and a postdoctoral researcher at Stanford University. His research spans four main areas: (1) AI for Security, developing methods to detect misinformation, malicious actors, and online hate; (2) Secure, Robust, and Responsible AI, investigating the trustworthiness of AI systems; (3) Graphs and Networks, modeling large-scale interconnected data; and (4) Recommender Systems and Behavior Modeling. His work has been deployed at Flipkart and has influenced Twitter's Birdwatch platform. Dr. Kumar leads The CLAWS (Computational Data Science Lab for the Web and Social Media), which has received funding from NSF, DARPA, CDC, Adobe, Google, Facebook, and Microsoft. His lab mentors numerous PhD, MS, and undergraduate students working on cutting-edge web safety and AI security challenges. NSF CAREER Award recipient Kavli Fellow by the National Academy of Sciences Forbes 30 Under 30 (2022) Rising Star in Data Science by Frontiers in Big Data ACM SIGKDD Doctoral Dissertation Award Runner-Up WWW Best Paper Award Honorable Mention

Nozha BoujemaaView profile

Researcher

• Multimedia Content Search
• Pattern Recognition
• Machine Learning
• +3 more

Nozha Boujemaa is a Research Director at Inria and Director of DATAIA Institute, leading projects in Data Sciences, Algorithmic Transparency, and Societal Impact. She co-founded the Digital Society Institute (ISN) and serves as a Senior Scientific Advisor for AI initiatives. Expert in Large Scale Multimedia Content Search, Pattern Recognition, and Machine Learning Developed methods for visual content enrichment, interactive retrieval, and satellite image analysis Scientific leader of Pl@ntNet (plant identification), CHORUS (multimedia search engines), VITALAS, MUSCLE, and TRENDS projects Her research spans Multimedia Retrieval , Big Data Applications , and Algorithmic Transparency , with over 150 publications. She has supervised 25+ PhD/Master's students and organized conferences like ACM Multimedia 2013 and European Big Data Value Forum 2017. Key scientific awards include: Knight of the National Order of Merit (France) She has contributed to international projects (NSF, European Commission), served on editorial boards for Multimedia Tools and Applications , and co-chaired workshops on visual digital libraries and AI ethics. Her work impacts web search, cybersecurity, biodiversity, and earth observation.

Branko MarkovićView profile

Assistant Professor

• Speech Processing
• Whispered Speech Recognition
• Computer Engineering
• +3 more

Dr. Branko Marković serves as an Assistant Professor at the Department of Computer and Software Engineering, Faculty of Technical Sciences in Čačak, University of Kragujevac, Serbia. His academic career spans over two decades, transitioning from industry experience in telecommunications to dedicated academic work following the merger of the College of Technical Vocational Studies with the Faculty in 2020. Dr. Marković earned his Bachelor's degree from the Faculty of Electrical Engineering in Belgrade in 1992, followed by a Master's degree in 2004 and a Doctorate in 2018, all focusing on telecommunications and speech processing. His doctoral research centered on speech processing and recognition, establishing the foundation for his subsequent research trajectory. His primary research focuses on speech processing , with groundbreaking work in whispered speech recognition - a challenging domain due to reduced acoustic energy in whispered speech. His research explores sophisticated methodologies including data augmentation techniques, wavelet transformations, inverse filtering, and various feature extraction approaches. He has made significant contributions to Serbian speech database development, notably the "Phonemes_1.0" database. His work bridges theoretical research with practical applications in assistive technologies, security systems, and human-computer interaction, demonstrating consistent innovation through 2024 publications. Analysis of his publication trends reveals an evolution from fundamental techniques like Dynamic Time Warping (DTW) and Mel-Frequency Cepstral Coefficients (MFCC) toward more advanced approaches involving deep learning and multimodal systems. His recent work emphasizes data augmentation for model robustness and wavelet-based feature extraction, reflecting the field's progression toward more sophisticated machine learning methodologies while maintaining focus on the unique challenges of whispered speech. Dr. Marković teaches across multiple academic levels, covering essential computer engineering subjects including Computer Assembly and Service, Internet Technologies, Computer Networks, Distributed Software Systems, Web Programming, and Computer Systems Design. His educational contributions extend to several textbooks for vocational studies in computer engineering fields. His professional journey includes valuable industry experience at the Mihailo Pupin Institute in Belgrade and Canadian telecommunications companies Bell Sygma, Architel, and Nortel Networks, followed by founding his own company Big engineering before transitioning to academia. This industry-academia trajectory enriches his practical teaching approach and research relevance.

Jana StrakováView profile

Researcher

• Natural Language Processing
• Deep Learning
• Multilingual Text Processing
• +3 more

Jana Straková is a researcher at the Institute of Formal and Applied Linguistics (ÚFAL) within the Faculty of Mathematics and Physics at Charles University in Prague. Her work focuses on advancing natural language processing techniques for multilingual applications, particularly in deep learning architectures for linguistic analysis. Her research interests span Natural Language Processing , Deep Learning , and Multilingual Text Processing with specialization in named entity recognition, dependency parsing, and morphological analysis. She has pioneered neural network approaches for tasks like diacritics restoration and grammar error correction, with particular emphasis on Czech language processing while extending methodologies to Latin and other languages. Analysis of her publication record from 2019-2025 reveals consistent innovation in NLP tool development, particularly the NameTag and UDPipe systems. Her work demonstrates strong integration of contextual embeddings with linguistic resources, showing increasing focus on cross-lingual transfer and historical language processing in recent years. As part of the ÚFAL research group, she collaborates extensively with Milan Straka and Jan Hajič on developing open-source NLP pipelines. Her projects include creating large-scale datasets like CWRCzech for web relevance ranking and advancing ontology engineering through LLM-augmented approaches.