جست‌وجوی استاد و پژوهشگر

Jingxia Wangمشاهده پروفایل

مدرس

• Urban Green Infrastructure Planning
• Environmental Analysis
• Sustainability Transformations
• +۱۱ مورد دیگر

Dr. Jingxia Wang is a Lecturer in GIS at the School of Geography and Planning, University of Sheffield, UK. She joined the university in 2022 after working as a full-time postdoctoral researcher at the Institute of Geography, Ruhr University Bochum (RUB), Germany. Her academic journey includes a PhD (Dr.rer.nat.) from the Technical University of Munich (TUM), Germany, awarded in October 2020 for her dissertation on urban green infrastructure planning and development. Dr. Wang's research spans urban ecology, spatial planning, and environmental analysis, with a strong focus on sustainability transformations. Her work integrates concepts from urban ecology and spatial planning with advanced methods in computational landscape ecology, GIS, and remote sensing. She has conducted extensive research on land use planning and management in German metropolitan regions (Ruhr and Leipzig) and Chinese cities including Beijing, Inner Mongolia, Tianjin, Kunming, and Lijiang. Her research methodology combines traditional GIS and remote sensing with emerging technologies such as environmental sensors, Internet of Things, point cloud, digital twins, and other smart technologies for urban nature applications. This interdisciplinary approach enables comprehensive analysis of urban green infrastructure connectivity across spatiotemporal scales. Dr. Wang's publication record demonstrates a clear trajectory in ecosystem services assessment, green infrastructure planning, and urban sustainability. Her recent work shows increasing focus on equity in green infrastructure distribution, biodiversity conservation, and the development of decision-support tools for sustainable urban planning. The geographic scope of her research spans Europe and Asia, with particular emphasis on comparative urban studies between German and Chinese metropolitan regions. Doctoral project certified by HIGRADE doctoral program with distinction Extensive international research experience in both European and Chinese urban contexts Guest Editor for Remote Sensing Special Issue Active reviewer and editorial contributions to academic journals As an educator, Dr. Wang teaches several GIS-related modules including Advanced GIS Methods, Spatial Analysis, Sustainable Cities, GIS for Built Environment Professionals, and The Professional GIS Project. She serves as a personal tutor for undergraduate students (level 2) and supervises dissertations. Her teaching combines theoretical knowledge with practical applications in urban green infrastructure planning and environmental analysis. Dr. Wang maintains active research collaborations with EU and Chinese universities and institutions, reflecting her transnational research approach to urban sustainability challenges. Her work bridges disciplinary boundaries between geography, ecology, urban planning, and environmental science to address complex urban sustainability issues.

Pepijn Van De Venمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• eHealth
• +۷ مورد دیگر

Pepijn Van De Ven is a Professor in the Department of Electronic and Computer Engineering at the University of Limerick, with affiliations to multiple research centers including the Centre for Research Training in Foundations of Data Science, Data-Driven Computer Engineering Research Centre, and Lero – the Research Ireland Centre for Software. His work bridges artificial intelligence with healthcare applications, particularly focusing on mental health interventions, mobile app development, and ambient assisted living technologies. PhD in AI Techniques for Marine Vehicles (UL, 2005) MSc in Electronic Engineering (Eindhoven University of Technology, 2000) His research interests center on leveraging machine learning and mobile ICT to enhance mental health care, with a specific emphasis on eMental Health, treatment prediction models, and context-aware intervention systems. He also explores computer vision applications in waste classification, medical imaging, and Industry 4.0 safety systems. The trends in his recent publications highlight interdisciplinary work combining computer vision (e.g., instance segmentation, contamination detection), mental health (e.g., depression, diabetes comorbidity), and AI-driven clinical tools (e.g., interpretable models, Bayesian analysis). Subfields like ecological momentary assessment, graph-based learning, and human-AI trust mechanisms are recurrent. Pepijn has contributed to teaching through modules on Android programming and has held leadership roles such as Course Leader for the National MSc in Artificial Intelligence at UL and Past-President of the International Society for Research on Internet Interventions (ISRII). He leads significant research projects including the PROACTIVE (UK-MRC), PERSONAE (Innovation Fund Denmark), and BINSPECT (Science Foundation Ireland) grants. Current research group: D2iCE (Design, Intelligence, and Computer Engineering) Collaborations: King’s College London, University of São Paulo, University of Bristol

Eric Milouمشاهده پروفایل

استاد

• K-12 Mathematics Education
• Curriculum & Instruction Development
• Technology in Mathematics Education
• +۳ مورد دیگر

Eric Milou is a Professor of Mathematics at Rowan University, affiliated with the College of Science & Mathematics and the Department of Mathematics. He specializes in K-12 mathematics education, curriculum development, and the integration of technology in teaching. His work emphasizes middle/high school mathematics programs and has led to widely used textbooks like Pearson's EnVision series and Digits. Education: EdD (Mathematics Education), Temple University MA (Mathematics), West Chester University BA (Mathematics), Franklin & Marshall College Research Interests: Dr. Milou focuses on curriculum design, instructional strategies, and technology-enhanced learning in mathematics education. He advocates for equitable access to rigorous math pathways and has contributed to transformative high school math programs. Recent work addresses urgent reforms in secondary mathematics education. Awards: 2009 Max Sobel Outstanding Mathematics Educator Award 2015 Rowan University Joseph Barnes Outstanding Service Award Teaching & Service: Currently teaches FOUND MATHEMATICAL REASONING (online), STRUCTURES OF MATH II (MW 1830-1945), and MATH SEM FOR EDUCATORS - WI (T 1700-1815/1830-1945). Office hours MW 5:30-6:30pm via email. Active member of NCTM, NCSM, AMTNJ, and TODOS: Mathematics for ALL. Publications: Over 20 impactful works since 1997, focusing on curriculum development, technology integration, and systemic reforms. Recent articles highlight high school math pathways, daily classroom routines, and critical policy analyses.

Johannes Boehmمشاهده پروفایل

استاد

• International Trade
• Development Economics
• Environmental Economics
• +۷ مورد دیگر

Johannes Boehm is a Professor of International Economics at the Geneva Graduate Institute, where he also holds a position in the Department of International Economics. He is a research fellow at the Centre for Economic Policy Research (CEPR) in London and the Centre for Economic Performance (CEP) at the London School of Economics. Previously, he was a professor at Sciences Po, Paris, and has held visiting positions at Princeton University, Harvard University, and INSEAD. His research focuses on international trade, industrial development, growth, and environmental economics, with a particular emphasis on how trade networks influence economic growth and resilience to geopolitical and climate risks. Boehm earned his PhD in Economics from the London School of Economics in 2014. His work explores the interplay between trade policies, firm dynamics, and macroeconomic outcomes, including studies on supply chain fragmentation, climate adaptation, and historical trade patterns. Notable research includes analyzing how extreme weather events impact production networks in Pakistan and investigating the historical shift of economic activity post-antiquity using ancient coin data. His thematic expertise spans trade policy, globalization, development cooperation, climate change, and central bank policies. Geographically, his research covers the Americas, Asia, and Europe. Boehm is also involved in software development for econometric analysis, creating tools like GLFixedEffectModels and Douglass to facilitate high-dimensional fixed-effects modeling and data manipulation.

• Computer Vision
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Xiaoming Wang is a Professor at Shaanxi Normal University's School of Computer Science, specializing in computer vision, machine learning, and artificial intelligence. His research spans multiple domains including object detection, federated learning, and industrial process modeling. He maintains active collaborations with researchers across China and internationally, particularly with Yongmeng Liu, Chuanzhi Sun, and Shitong Wang. Dr. Wang's research interests focus on developing advanced AI models for practical applications. His work in computer vision includes improving object detection systems like YOLO variants for security and industrial applications. In machine learning, he has made significant contributions to federated learning with privacy preservation techniques. His industrial applications research addresses real-world problems in manufacturing, aerospace, and process control systems. Analysis of Dr. Wang's recent publications reveals a strong emphasis on practical AI applications with industrial relevance. His work shows consistent innovation in adapting established AI techniques like LSTM networks, transformers, and attention mechanisms to solve specific domain challenges. The publications demonstrate a clear trajectory toward more efficient, privacy-preserving, and real-time AI systems applicable across multiple sectors. Dr. Wang actively mentors students and junior researchers, with multiple co-authored publications featuring individuals likely to be his advisees. His collaborative approach is evident through numerous multi-institutional projects and consistent research partnerships. While specific grant information isn't detailed in the publication record, the scope and consistency of his work suggest substantial research funding support.

Tamer Özsuمشاهده پروفایل

استاد

• Distributed Data Management
• Graph Databases
• RDF Systems
• +۲ مورد دیگر

Tamer Özsu is a University Professor at the David R. Cheriton School of Computer Science, University of Waterloo, where he previously served as Director (2007-2010) and currently holds the role of Associate Dean of Research. He is also a Distinguished Visiting Professor at Tsinghua University. Professor Özsu's research spans distributed data management, graph/RDF systems, and database fundamentals. His current work focuses on: Distributed graph processing algorithms SPARQL query optimization for RDF systems Streaming graph analytics Indexing techniques for modern hardware Distributed database architectures He has authored the seminal textbook Principles of Distributed Database Systems and co-edited the Encyclopedia of Database Systems . As founding Editor-in-Chief of ACM Books, he has shaped computing literature. His recent publications demonstrate continued innovation in graph partitioning, streaming graph algorithms, and scalable RDF processing. Özsu maintains active research leadership in database systems with over 30 years of influential contributions.

• Programming Languages
• Category Theory
• Domain-Specific Languages
• +۲ مورد دیگر

Nicolas Wu is a Reader (Associate Professor) of Computer Science at Imperial College London, leading the Functional Programming Research Group and serving as Senior PhD Tutor. Education: DPhil (Oxford), BS/BA (Oxford). Research applies category theory to programming language semantics, connecting domain-specific languages, algebraic effect handlers, and recursion schemes. Current work formalizes scoped effects and parameterized algebraic theories. Teaches functional programming and type theory. Supervises doctoral research on language design and formal verification.

• Programming Language Theory
• Functional Programming
• Logic Programming
• +۳ مورد دیگر

Tom Schrijvers is a Professor at the Department of Computer Science, Faculty of Engineering Science, KU Leuven. His research focuses on programming language theory, functional programming, logic programming, and computational effects. Advancing multi-stage programming with computational effects (2024–2028) Generating Educational Feedback with Program Analysis (2024–2028) Programino: An Educational Programming Platform (2023–2027) eTeacher: Interactive web-based programming education platform (2023–2025) AmPERSand: Programming Education Runtime System (2023–2026) His recent publications emphasize bidirectional transformations, effect handlers, and programming education tools. He collaborates extensively with British institutions like Imperial College London and works on the book Language Engineering in Haskell with Dr. Nicolas Wu.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۹ مورد دیگر

Han Chen is an active academic researcher in the fields of artificial intelligence, machine learning, computer vision, and biomedical informatics, with extensive publications in top-tier venues such as CVPR, ICLR, IEEE Transactions, and Bioinformatics. The research spans diverse applications including medical image analysis, deepfake detection, federated learning, and multimodal affective computing. The research interests of Han Chen include artificial intelligence, machine learning, computer vision, medical image analysis, graph neural networks, and cybersecurity. The work emphasizes deep learning architectures, multimodal fusion, and robustness in AI systems, particularly in healthcare and security applications. Key themes include disentangled representation learning, contrastive learning, and transformer-based models. The recent articles demonstrate a strong trend toward multimodal AI, with applications in medical diagnostics (e.g., mammography, nasopharyngeal imaging), trustworthy AI (e.g., deepfake detection, backdoor defense), and efficient learning paradigms (e.g., federated learning, weak supervision). There is a consistent focus on enhancing model interpretability, robustness, and real-world applicability across domains. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: There is no explicit information about students or advising. However, the high volume of collaborative research suggests active involvement in research teams and likely supervision of graduate students. Funding sources are not mentioned, but the scope of work implies support from national or institutional research grants in AI and health informatics. Labs and Teams: While no specific lab or team is named, the collaborative nature of the work—especially with researchers in medical imaging and federated learning—suggests membership in a multidisciplinary AI research group, possibly affiliated with a medical school or engineering institute.

Michael Stiberمشاهده پروفایل

استاد

• Computational Neuroscience
• Cybersecurity
• Artificial Intelligence
• +۵ مورد دیگر

Michael Stiber is a Professor and Division Chair in the Computing & Software Systems Division at the University of Washington Bothell (UWB), affiliated with the School of Science, Technology, Engineering & Mathematics. He is also the Director of Cybersecurity Initiatives and Principal Investigator at the Intelligent Networks Laboratory (formerly BrainGrid Laboratory). Education: Ph.D. in Computer Science from UCLA (1989), M.S. in Computer Science from UCLA, B.S. in Electrical Engineering from Washington University in St. Louis His research focuses on complex networks, high-performance simulation, and data management, applied to computational neuroscience, cybersecurity, artificial intelligence, and nonlinear dynamics. He leads projects modeling emergency services communications systems (e.g., 911 networks) and biological neural network development using the Graphitti simulation framework. He has supervised numerous graduate students, including Jardi Martinez Jordan, Divya Kamath, Ashwini Rudrawar, Snigdha Singh, Emily Hsu, Smriti Singh, Victoria Salvatore, Joe Conquest, Tori Salvatore, and Tony Varela, who have defended theses or projects related to graph-based simulation, GPU migration, neural networks, and AI-driven data analysis. Scientific awards: Fulbright Scholar (Czech Academy of Sciences) Grants: US National Security Agency (2-year funding), InterPARES Trust AI (international cybersecurity-AI collaboration) The Intelligent Networks Laboratory, under his leadership, rebranded from BrainGrid, expanded its scope to AI-driven data analysis, and developed tools like Graphitti (for simulating network-structured systems) and Workbench (for tracking simulation code and data provenance).

• Mental Health
• Gender Diversity and Health
• Suicide Prevention
• +۴ مورد دیگر

Dr. Davi Manzini Macedo is an Adjunct Assistant Professor at the University of South Australia's Clinical & Health Sciences school. His primary affiliation is with the Mental Health and Suicide Prevention Research and Education Group. He has collaborated extensively with Child and Adolescent Mental Health Services (CAMHS) and the Women's and Children's Health Network. His research focuses on mental health care for marginalized populations, particularly trans and gender diverse youth from culturally and linguistically diverse (CALD) backgrounds. Key areas include suicide prevention, adolescent development, and culturally competent healthcare practices. He also contributes to studies on domestic violence policy and child behavioral assessments through longitudinal cohort analyses. Recent work emphasizes scoping reviews on self-harm among TGD youth and practical clinical strategies to improve service accessibility. His collaborations span Brazil (Universidade Federal do Rio Grande do Sul) and Australia, reflecting a global perspective on health disparities. He has contributed to open-access publications in International Journal of Mental Health Nursing and BMJ Open , with grants from the SA Department for Health and Wellbeing. His work integrates network psychometrics and community-of-practice methodologies to address systemic barriers in mental healthcare.

Michael Gebelمشاهده پروفایل

استاد

• Life Course Research
• Labor Market Sociology
• Education and Employment Transitions
• +۴ مورد دیگر

Prof. Dr. Michael Gebel is Full Professor of Sociology, especially Methods of Empirical Social Research, at the University of Bamberg's Faculty of Social and Economic Sciences. Since 2014, he has served as Director of the Institute of Sociology and Chairholder of the National Educational Panel Study's Pillar 5 on Returns to Education. His research focuses on Life course transitions Labor market dynamics Education-to-employment pathways Socioeconomic impacts on health Key methodological expertise includes Longitudinal data analysis Causal inference techniques Multilevel modeling Geographic research scope spans Western/Eastern Europe, Middle East, Caucasus, Central Asia, and East Asia. Notable projects include EU Horizon 2020 EXCEPT (€2.5M, 2015-2018) and Volkswagen Foundation TEW-CCA (€0.4M, 2015-2019). Scientific recognition includes ERC Starting Grant (€1.4M, 2018-2023) Inclusion in Germany's Top 40 Under 40 in Society and Science (2018) Co-editorship of European Sociological Review (since 2022) Advising contributions focus on European Research Network on Transitions in Youth European Consortium for Sociological Research International conferences across 25+ countries

Rafał Loskaمشاهده پروفایل

دانشیار

• Organic Chemistry
• Catalysis
• Heterocyclic Chemistry
• +۳ مورد دیگر

Assoc. Prof. Rafał Loska is affiliated with the Institute of Organic Chemistry, Polish Academy of Sciences , where he leads a scientific group focused on advanced organic synthetic methods. His research spans catalytic C–H activation, nucleophilic substitution of hydrogen, enantioselective polycyclic scaffold construction, and computational modeling of chemical reactions. Academic Rank: Associate Professor Research Focus: Transition metal-catalyzed reactions, allene synthesis, heterocyclic chemistry Email: rafal.loska@icho.edu.pl Research Trends His recent publications (2021–2023) emphasize C–H activation , cycloaddition reactions , and computational reaction modeling , with applications in alkaloid synthesis and asymmetric catalysis. Collaborative work with Prof. Mieczysław Mąkosza and PhD students explores novel reaction mechanisms and stereoselectivity. Grants Currently leading the OPUS NCN grant 2022/47/B/ST4/01498 for palladium-catalyzed enantioselective polycyclic alkaloid scaffolds.

Mingyao Liمشاهده پروفایل

دانشیار

• Bioinformatics
• Biostatistics
• Genetics
• +۲ مورد دیگر

Mingyao Li, PhD, is an Associate Professor of Biostatistics whose scholarship bridges high-dimensional statistics with modern genomics and spatial biology. Her work is recognised for advancing computational methods that dissect complex tissue architecture and disease heterogeneity at single-cell resolution. Research Interests Bioinformatics and computational biology Statistical methods for high-dimensional genomic data Single-cell and spatial multi-omics integration Genetics and genomics of cardiovascular and cancer systems Systems biology approaches to disease mechanisms Across more than one hundred peer-reviewed publications, Li’s research exhibits a clear trajectory from developing rigorous statistical frameworks—such as hypothesis testing in stochastic block models—to large-scale applications in human disease. A dominant theme is the fusion of spatial transcriptomics, single-cell multi-omics and AI-driven image analysis to uncover cell-type heterogeneity, lineage relationships and microenvironmental crosstalk in atherosclerosis, lung adenocarcinoma, pancreatic cancer and neurodegenerative disorders. Scientific Awards & Recognition While specific awards are not listed in the provided text, the consistent appearance of Dr Li as senior or corresponding author on high-impact papers in Nature family journals and the development of widely-used computational tools (e.g., MISO, iSCALE) attest to significant scholarly recognition. Research Support & Teams Granting agencies and collaborative networks are not explicitly detailed, but the scale and scope of the projects—from whole-organ spatial atlases to primate genome-editing studies—imply substantial multi-institutional funding and interdisciplinary teams integrating biostatisticians, wet-lab scientists and clinician-scientists.

Andreea Costeaمشاهده پروفایل

استادیار

• Programming Languages
• Software Verification
• Automated Program Repair
• +۵ مورد دیگر

Dr. Andreea Costea is an Assistant Professor at the Programming Languages Group in the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at TU Delft, Netherlands. She completed her PhD at the School of Computing, National University of Singapore (NUS), and previously collaborated with NUS's Programming Languages and Software Engineering lab, Automated Program Repair team, and VERSE lab. Research: Formal Methods, Software Verification, Automated Program Repair, Rust Programming, Concurrency Funding: Ministry of Education Tier 3 Grant (2022) Projects: HIPPODROME (Java data race repair), ROBoSuSLik (heap synthesis), Mercurius (session protocols) Recent MSc Students: Matei Mirică (2025), Arjan Seijs (2025), Jim van Vliet (2025), Timen Zandbergen (2025), Arthur de Groot (2024), Julius de Jeu (2024), Matteo Bertorotta (2024). Conference Involvement: Program Committee member for PriSC 2024, PEPM 2024, SPLASH/ISSTA 2026, ICSE 2025, ICFP 2023, APLAS 2021.