جست‌وجوی استاد و پژوهشگر

• digital libraries
• metadata
• linked data
• +۳ مورد دیگر

Matthew Short is an Associate Professor and Head of Technical Services at Northern Illinois University (NIU) Libraries. He earned a Certificate of Advanced Study and an MLIS in Library and Information Science from the University of Illinois at Urbana-Champaign, along with a BA in Literature, Philosophy, and Classical Studies from SUNY Potsdam. Current Role: Associate Professor, Head of Technical Services Education: MLIS (University of Illinois at Urbana-Champaign), BA (SUNY Potsdam) His research focuses on digital libraries , metadata , linked data , and digital humanities , with a particular emphasis on preserving and analyzing 19th-century popular fiction like dime novels . His work integrates computational methods such as text mining and syuzhet algorithms to study historical narratives and develop digital archives. Matthew has published extensively on dime novels , metadata , and linked data , with notable works including a 2019 Oxford University Press chapter and a 2017 study using the Syuzhet algorithm to analyze plot structures in 19th-century fiction. His 2016 project on linked open dime novels bridges historical literature with modern data standards. He has secured significant funding, including a 2016 grant from the Council on Library and Information Resources for the Albert Johannsen Project , which digitized Beadle and Adams collections. He also collaborates on initiatives like the Nickels and Dimes Digital Library , focusing on improving access to historical print culture.

• Cognitive Semantics
• Construction Grammar
• Discourse Analysis
• +۶ مورد دیگر

Alexander Ziem is a Professor of German Linguistics and Chair holder at the Institute of German Studies , Heinrich Heine University Düsseldorf, since 2016. His academic career spans institutions such as the International Computer Science Institute (ICSI) in Berkeley, Universität Basel, and Technische Universität Berlin. Research Focus: Cognitive semantics, construction grammar, FrameNet-Konstruktikon projects, discourse analysis, and socio-cognitive dimensions of language. Projects: Initiator of the long-term ' FrameNet and Constructicon of German ' project; co-editor of Diskursforschung and DiskursNetz handbooks. His recent publications emphasize constructional meanings, semantic frames, and multilingual frameworks. He leads the German Constructicon project and collaborates on digital humanities tools like SimilarCxns and FrameMap3D. Scientific Awards: Preis für Geisteswissenschaften , Akademie der Wissenschaften zu Göttingen (2011). Research Fellow, International Computer Science Institute (ICSI), Berkeley (2013–2014). Ziem’s team at HHU includes 25 members, with 10 research assistants, and he contributes to curriculum development, including the upcoming Digital Cultures degree program. His methodological approach integrates corpus linguistics, frame semantics, and machine learning.

Shen-Shyang Hoمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Artificial Intelligence
• +۷ مورد دیگر

Shen-Shyang Ho is a Full Professor in the Department of Computer Science at Rowan University's College of Science & Mathematics. His work spans machine learning, data mining, and edge computing with applications in urban mobility, precision agriculture, and data privacy. He leads NSF-funded research projects on dynamic graph analysis and spatiotemporal anomaly detection. Ph.D. in Computer Science, George Mason University Post-Doctoral Associate, Caltech & NASA JPL B.S. in Mathematics with Computational Science, National University of Singapore Research expertise includes graph-based machine learning, conformal prediction, cooperative inference, and privacy-preserving ML. Current work focuses on federated learning for edge devices and anomaly detection in evolving systems. He has developed tools like SplitTracer for cooperative inference evaluation and ParkGauge for urban mobility monitoring. Recent publications highlight his contributions to 2026 Pattern Recognition journal (martingale-based graph analysis), 2025 IEEE ICAIC conference (blockchain gas optimization), and 2024 ACM SAC symposium (shared mobility systems). His work integrates machine learning with real-world constraints across energy grids, transportation, and agricultural technology. NSF Grant (2022) for Dynamic Graph Anomaly Detection NSF Grant (2018) for Spatiotemporal Analysis Google Scholar Classic Paper Recognition (2017) for 2006 Radar Micro-Doppler Study Professional memberships include the Association for Computing Machinery (ACM). His teaching portfolio ranges from introductory programming to advanced ML courses. He previously held positions at Nanyang Technological University before joining Rowan in 2016.

Mehmet Kizilمشاهده پروفایل

دانشیار

• Mine Planning and Design
• Mining Systems
• Production Analysis and Improvement
• +۷ مورد دیگر

Associate Professor Mehmet Kizil is the Mining Engineering Program Leader at the School of Mechanical and Mining Engineering, The University of Queensland. With a career spanning 25+ years, he holds a Bachelor of Mining Engineering (1986) from Dokuz Eylul University (Turkey) and PhD (1993) from the University of Nottingham (UK). His affiliations include being an affiliate of Future Autonomous Systems and Technologies and active participation in Mining Education Australia. School of Mechanical and Mining Engineering Australian Research Council (ACARP, CRCTiME, MRIWA, SIMTARS, industry partners) Research interests focus on mine planning/design, production optimization, computer/virtual reality applications, and mine ventilation systems. His 2025-2024 publications highlight innovations in FMIPCC systems, methane dispersion modeling, digital twins for mineral processing, and sustainable underground mining. Notable scientific achievements include being recognized as Higher Education Academy Senior Fellow (2018) and receiving national teaching awards. His work with industry partners (Newcrest, Rio Tinto, BHP, Xstrata, Anglo American, Origin Energy, Iluka Resources) has secured over $3M in research funding.

Stephan Leibleمشاهده پروفایل

پژوهشگر

• Employee-driven (Digital) Innovation
• Intrapreneurship
• Employee Engagement
• +۲ مورد دیگر

Stephan Leible is a Researcher at the Department of Computer Science , University of Hamburg (MIN Faculty). Holding both M.Eng. in Business Engineering and an MBA, he focuses on employee-driven digital innovation, intrapreneurship, and leveraging generative AI for organizational transformation. Research Interests : Employee-driven Innovation, Intrapreneurship, Generative AI Governance, Design & Data Thinking, IT Innovation Management Contact : stephan.leible@uni-hamburg.de | Room 117C, Vogt-Kölln-Straße 30, Hamburg His work bridges citizen development with public sector innovation, emphasizing value co-creation, real-time analytics, and ethical AI implementation. Current studies explore: Generative AI adoption patterns and limitations Interpretable machine learning for urban mobility Participatory frameworks for smart city futures Methodologies for continuous improvement in conversational agents

Olubunmi O. Suleمشاهده پروفایل

استادیار مهمان

• machine learning
• computer vision
• image processing
• +۳ مورد دیگر

Dr. Olubunmi O. Sule serves as a Visiting Assistant Professor in the Department of Computer Science at Wake Forest University, bringing over two decades of international teaching experience from Nigeria and South Africa to her current role. Her academic foundation includes a PhD in Computer Science from the University of KwaZulu-Natal (2022) and a Post Graduate Program in Cybersecurity from The University of Texas at Austin (2024). Education Highlights: PhD in Computer Science, University of KwaZulu-Natal, Durban, South Africa (2022) Post Graduate Program in Cybersecurity, The University of Texas at Austin (2024) Research Focus: Dr. Sule's work bridges machine learning, computer vision, and medical diagnostics through advanced deep learning frameworks. Her primary concentration involves retinal fundus image analysis for blood vessel segmentation, where she innovates in contrast enhancement techniques and CNN architectures to improve early abnormality detection. Complementing this medical focus, she also explores cybersecurity applications and social media analysis, notably examining Nigerian political narratives on Facebook through multimodal data. Publication Trends: Her 2020-2023 publications reveal a dominant specialization in retinal blood vessel segmentation (8 of 9 papers), with systematic progression from algorithmic reviews to novel enhancement schemes and hybrid neural architectures. The recurring emphasis on contrast optimization and feature preservation demonstrates methodological rigor, while her 2023 social media analysis paper indicates expanding interdisciplinary reach into political communication. Scientific Recognition: First Prize Award for Oral Presentation at University of KwaZulu-Natal's Postgraduate Research Symposium (2020) Honourable Mention for Poster Presentation at Deep Learning IndabaX International Conference (South Africa Chapter) (2019) Academic Leadership: Dr. Sule has instructed foundational computer science courses at Wake Forest University including CSC 102 (Problem Solving with Python), CSC 111 (Java Programming), CSC 112 (Computer Science Fundamentals), and CSC 201 (Data Structures & Algorithms). Her two-decade teaching career across African institutions demonstrates exceptional commitment to pedagogy, though specific grant funding details remain unreported in available sources. While institutional lab affiliations aren't explicitly documented, her medical imaging research suggests potential collaborations with ophthalmology departments or healthcare AI initiatives focused on diagnostic automation through computer vision systems.

Thomas Gärtnerمشاهده پروفایل

استاد

• Machine Learning
• Graph Neural Networks
• Geometric Deep Learning
• +۳ مورد دیگر

Thomas Gärtner is a Professor at the Institute of Logic and Computation within the Faculty of Informatics at Vienna University of Technology, leading the Machine Learning research group (E194-06). His work bridges theoretical machine learning with practical applications in chemistry, biology, and network analysis. His primary research focuses on graph neural networks (GNNs) and geometric deep learning, with significant contributions to GNN expressivity, graph transformations, and kernel methods for structured data. He explores fundamental questions about the limitations of message-passing architectures while developing practical enhancements like path-based extensions and expectation-complete representations. His chemical informatics work applies these techniques to binding affinity prediction, reaction classification, and solvent selection, demonstrating real-world impact in computational chemistry. Analysis of his 15 most recent publications (2023-2025) reveals three dominant research thrusts: theoretical GNN advancements (35% of articles), chemical informatics applications (40%), and novel learning frameworks (25%). The theoretical work increasingly addresses expressivity limitations through graph transformations and path-based approaches, while chemical applications show growing sophistication in molecular representation. Recent publications also indicate expanding interest in foundation models for graphs and robustness verification. He actively supervises master's students including Fabian Traxler (binding affinity prediction), Maximilian Plattner (SGD optimization), Fabian Jogl (graph transformations), and Thomas Schmied (reinforcement learning). His research is conducted through the Network Lab at TU Wien, where he serves as Principal Investigator for the Structured Data Learning with Generalized Similarities project.

Bogumiła Hnatkowskaمشاهده پروفایل

استادیار

• Software Engineering
• Analysis and Design of Information Systems
• Software Development Methodologies
• +۳ مورد دیگر

Dr. Bogumiła Hnatkowska serves as Assistant Professor at the Institute of Informatics within the Faculty of Computer Science and Management at Wrocław University of Science and Technology. Her academic career spans software engineering research and education with emphasis on model-driven approaches and quality assurance methodologies. Her research interests include: Software Engineering Analysis and Design of Information Systems Software Development Methodologies Model-Based Software Development Domain-Specific Languages Software Quality Recent publications (2021-2025) reveal concentrated research in model-driven engineering, business rules processing, and ontology integration. Key trends involve textual specification languages for use-cases, automated test generation mechanisms, and formal transformations for ontologies – demonstrating consistent application of theoretical rigor to practical software development challenges across agile and model-based contexts. Scientific Awards: No scientific awards were mentioned in the provided text Dr. Hnatkowska has served as principal investigator for multiple State Committee for Scientific Research grants including UML extensions for multimedia systems (2000), real-time systems analysis (2005), and model-driven database design (2008). Her teaching portfolio includes Software Engineering, Software System Development, and Advanced Programming Techniques courses where she supervises team projects providing students with hands-on development experience. She actively participates in partner programs including Visual Paradigm's Academic Training Partner Program (providing UML/BPMN/agile tools) and IBM Academic Initiative, supporting her research in software engineering methodologies and educational tool development.

Mario Alvianoمشاهده پروفایل

استاد

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Combinatorial Search and Optimization
• +۵ مورد دیگر

Mario Alviano is a Full Professor in Computer Science (INF/01) at the University of Calabria, Department of Mathematics and Computer Science. He leads the LAIA lab (Laboratorio di Applicazioni dell'Intelligenza Artificiale) and serves as co-PI in the PRIN project PRODE ('Probabilistic Declarative Process Mining'). Current projects: FAIR ('Future AI Research'), Tech4You ('Technologies for climate change adaptation'), SERICS ('SEcurity and RIghts in the CyberSpace'), CAL.HUB.RIA , RADIOAMICA , and STROKE 5.0 His research focuses on Answer Set Programming (ASP), particularly in optimization, nonmonotonic reasoning, and applications to logistics, healthcare, and cybersecurity. He has authored over 120 publications in top venues like AIJ, AAAI, and IJCAI. Recent academic contributions includes work on: Temporal Many-valued Conditional Logics Weighted Knowledge Bases with Typicality Explainable AI via xASP and ASP Chef Defeasible Reasoning Scalability Notable awards: Artificial Intelligence Award 'Marco Somalvico' (2017) ICLP Best Paper Awards (2015, 2016) LPNMR Best Paper Award (2022) CILC Best Paper Award (2023)

• Hemostasis
• Thrombosis
• Stem Cell Research
• +۳ مورد دیگر

Ulrich Klostermeier is a researcher at the Institute of Clinical Chemistry and Laboratory Medicine, University Medical Center Hamburg-Eppendorf (UKE). His scientific work spans hemostasis, thrombosis research, and molecular mechanisms of coagulation disorders. Hematology Clinical Chemistry Stem Cell Research Translational Medicine His research focuses on thrombophilia , venous thromboembolism , and genetic regulation in both pediatric and adult populations. Key projects include investigations into protein C/S deficiencies , NOD2 interactions , and hemostatic biomarkers . Recent publications demonstrate expertise in translational thrombosis research , with significant contributions to understanding prothrombotic polymorphisms and stem cell applications in cardiac regeneration. Collaborative work includes international cohort studies and multicenter clinical research. Methodologically, he employs transcriptional profiling , mutation analysis , and animal models for studying blood coagulation dynamics. His scientific work has been published in journals covering hematology, molecular biology, and clinical chemistry. Key research trends: Thrombosis risk stratification Genetic basis of coagulation disorders Comparative pediatric/adult hemostasis Translational approaches to cardiac repair Computational transcriptional analysis Developmental biology applications

Danny Campbellمشاهده پروفایل

استاد

• Discrete Choice Experiments
• Environmental Economics
• Health Economics
• +۳ مورد دیگر

Professor Danny Campbell is affiliated with the University of Stirling in the Department of Economics. His research focuses on discrete choice experiments, environmental valuation, health economics, and consumer behavior. Research Themes: Cultural Heritage, Environmental Change, Global Food Security, Health & Behaviour Current research explores spatial heterogeneity in willingness-to-pay, attribute non-attendance in decision-making, and policy applications of choice modeling. Recent publications analyze wine attribute valuation, aquatic food demand in China, and environmental economics tools. He has contributed to methodological advancements in discrete choice experiments, including satisficing behavior, position bias, and spatial error modeling. Contact: danny.campbell@stir.ac.uk

Jeffery D. Weirمشاهده پروفایل

استاد مدعو

• Operations Research
• Decision Analysis
• Risk Analysis
• +۶ مورد دیگر

Dr. Jeffery D. Weir is an Adjunct Professor in the Department of Operational Sciences at the Air Force Institute of Technology (AFIT). A former U.S. Air Force officer, he specializes in decision analysis, risk analysis, and multi-objective optimization, with applications spanning military logistics, energy systems, and transportation modeling. His research addresses complex operational challenges such as aircraft scheduling, RNA foldability prediction, and cost analysis for military installations. Doctor of Philosophy (Ph.D.), Industrial Engineering & Operations Research, Georgia Institute of Technology, 2002 Master of Science (M.S.), Operations Research, Air Force Institute of Technology, 1995 Bachelor of Science in Electrical Engineering (B.S.E.E.), Georgia Institute of Technology, 1988 Dr. Weir's research bridges theoretical and applied domains, focusing on metaheuristics, data envelopment analysis, and robust optimization. His recent work includes RNA structure prediction frameworks, multi-modal distribution networks, and energy model recommendation systems. His publications span journals like BMC Bioinformatics , European Journal of Operations Research , and IEEE Transactions on Evolutionary Computation , reflecting interdisciplinary expertise in computational biology, military logistics, and building energy systems. Scientific awards include the Toulmin Medal for best article in 2004. He has received grants from the Defense Intelligence Agency, United States Transportation Command, Air Force Materiel Command, and other military organizations. Dr. Weir is a member of the Institute for Operations Research and the Management Sciences (INFORMS), Military Operations Research Society, and Institute of Industrial Engineers. His work emphasizes practical decision support tools for military personnel and resource management.

• Historical Climatology
• Climate Dynamics
• Atmospheric Circulation Reconstruction
• +۴ مورد دیگر

Prof. Dr. Stefan Brönnimann serves as a Professor and Unit Leader of Climatology at the Institute of Geography, University of Bern. His extensive research portfolio spans historical climatology, climate dynamics, and atmospheric circulation reconstruction, with particular focus on the past 400 years. He plays a leadership role in major international climate initiatives including the IPCC assessment reports and various climate reanalysis projects. Brönnimann's research centers on reconstructing historical weather and climate patterns through the innovative combination of early instrumental data, proxy records, and climate models. His work examines large-scale climate variability, interannual-to-decadal atmospheric circulation patterns, volcanic eruption effects on climate, and climate-society interactions. His methodologies often involve transforming historical documents into usable climate datasets and applying advanced machine learning techniques to weather reconstruction challenges. Recent publications reveal a strong emphasis on European climate patterns, hydroclimate extremes, and the development of novel datasets and tools for climate research across high-impact journals in climate science, paleoclimatology, and climate informatics. Notable scientific achievements include: Lead author for Chapter 2 of the IPCC Working Group I 5th Assessment Report President of the Commission 'Atmospheric Chemistry and Physics' (ACP) of sc.nat Editorial leadership for multiple prestigious journals including Meteorologische Zeitschrift, Climate of the Past, and Geographica Bernensia Leadership roles at the Oeschger Centre for Climate Change Research Active participation in international initiatives like the Twentieth Century Reanalysis Project and Atmospheric Circulation Reconstructions over the Earth (ACRE) Brönnimann has secured substantial funding through numerous national and international projects including SNF, NCCR Climate, EU FP-7, HORIZON2020, COST, and ERAnet.RUS. He has organized multiple international workshops on weather and climate extremes, atmospheric circulation variability, and historical climate events like the Tambora eruption. His work connects closely with the Oeschger Centre for Climate Change Research at the University of Bern, where he leads Work Package 2 and serves on the Steering Group, demonstrating his significant institutional leadership within Bern's climate research community.

Lorenzo Vigentiniمشاهده پروفایل

عضو هیئت علمی

• Learning Analytics
• Educational Data Mining
• Psychology of Learning
• +۶ مورد دیگر

Dr. Lorenzo Vigentini serves as an Adjunct Senior Lecturer in the Computer Science and Engineering Department within the Faculty of Engineering at the University of New South Wales (UNSW). With a background spanning psychology, higher education teaching, and extensive IT/e-learning experience, he contributes significantly to the academic community through his expertise at the intersection of cognitive psychology, differential psychology, education, and human-computer interaction. Vigentini's research focuses on learning processes, particularly examining how technology interacts with learning and the human-machine interface. His work emphasizes data-driven approaches to understanding educational patterns, with special interest in Learning Analytics and Educational Data Mining. He has led the development of the Learning Analytics and Educational Data Science research group at UNSW, positioning himself at the forefront of educational innovation. His research interests span technology evolution, student experience enhancement, Quality Assurance processes, and Quality Enhancement in educational contexts. Vigentini's publication record demonstrates consistent focus on learning analytics applications across educational contexts, with recent work examining dashboard development for both secondary schools and higher education institutions. His research shows a clear progression from theoretical frameworks to practical implementations, particularly in MOOC analytics and personalized learning support systems. His work bridges the gap between educational theory and technological implementation, with an increasing emphasis on institutional adoption of data-driven approaches. As a research leader, Vigentini has secured multiple significant grants totaling over AUD 1.9 million, including a major AUD 1.2 million UNSW 'Students as Partners' program in 2018. His grant history reflects a consistent focus on educational innovation, learning analytics implementation, and student experience enhancement across different educational contexts and platforms. Dr. Vigentini has established the Learning Analytics and Educational Data Science research group at UNSW, creating a collaborative environment for exploring data-driven approaches to education. His work integrates quantitative and qualitative methodologies to develop comprehensive understanding of learning processes and technological interventions in educational settings.

Thomas W. Priceمشاهده پروفایل

استادیار

• Computing Education
• Educational Data Mining
• Advanced Learning Technologies
• +۵ مورد دیگر

Thomas W. Price is an Assistant Professor in the Department of Computer Science at North Carolina State University's College of Engineering. He directs the Help through INTelligent Support (HINTS) Lab and is affiliated with NCSU's Center for Educational Informatics. His work bridges computer science, education, and artificial intelligence to create intelligent learning environments. Dr. Price earned his M.S. and Ph.D. in Computer Science from NC State University in 2015 and 2018 respectively, and was named the College of Engineering Doctoral Scholar of the Year in 2018. His research focuses on computing education with an emphasis on automatically generating programming hints and feedback using student data. He has evaluated innovative programming environments including block-based and frame-based systems, and designs intelligent support tools that integrate with these technologies. His work examines how students seek and use help in both classroom and online settings, with particular interest in open-ended programming projects, data-driven feedback systems, and understanding student programming behaviors. His recent publications reveal a strong trend toward integrating AI and educational data mining techniques to support programming education. His work spans from fundamental research on student help-seeking behaviors to practical tools that provide immediate feedback, detect student struggles, and offer personalized support. A significant portion of his recent work addresses emerging challenges like student use of generative AI tools and detecting AI-generated code submissions. College of Engineering Doctoral Scholar of the Year (2018) Exemplary Paper Award from International Conference on Educational Data Mining Exemplary Paper Award from ACM Technical Symposium on Computer Science Education Recognition by STARS Computing Corps for leadership in computing outreach Dr. Price mentors several Ph.D. students including James Skripchuk, John Thomas Bacher, and Keith Tran. He has secured over $4 million in research funding from the National Science Foundation for projects including infrastructure for sustainable innovation in computer science education, data-driven technologies for STEM instruction, and intelligent support for creative programming projects. His research has practical applications in classroom settings and aims to create scalable solutions that don't place additional burden on instructors. As director of the HINTS Lab, Dr. Price leads a team investigating how to re-imagine educational programming environments as adaptive, data-driven systems. The lab focuses on supporting students working in creative, open-ended contexts through automated tools for planning, hint generation, and progress monitoring. Their research emphasizes practical methods that can scale to new classrooms and contexts while providing personalized support tailored to individual student needs.